Effect of electrode materials in the electroreductive formation of 1,1,2,2-tetramethyl-1,2-diphenyldisilane (2a)a
\r\n\t1. Geopolymers chemistry topic describes the chemical reaction models and chemical kinetic of the geopolymerization which occurs after mixing the aluminosilicate raw materials with an alkaline solution.
\r\n\t2. Advanced characterization of geopolymers topic includes innovative technologies applied on geopolymers characterization at the nanoscale level, meant to explain the bond between the reacted and nonreacted particles from the composition.
\r\n\t3. Sustainability with geopolymers topic should provide clear information about the characteristics and applications of the geopolymers which use as raw materials industrial waste. Moreover, environmental impact studies which offer a clear view of the effects produced by geopolymers manufacturing, compared to conventional materials, is included.
\r\n\t4. Geopolymers as functional materials topic will present key aspects in developing geopolymers with tailored properties that increase further the heavy metals adsorption capacity, offering outstanding opportunities for energy-efficient separations and process intensification, in terms of saving energy, reducing capital costs, minimizing environmental impact and maximizing the raw materials exploitation.
\r\n\t5. Reinforced structures topic describe the effects produced by the introduction, in the geopolymers matrix, of different types of reinforcing elements.
Polysilanes (Scheme 1) [1] have attracted considerable attention due to their usefulness as the precursors for thermally stable ceramics [2, 3] or a material for microlithography [4, 5], and also due to their potentiality in the preparation of new types of material showing semiconducting, photoconducting, or nonlinear optical property [6-8].
In contrast to the growing interest with the polysilane, the method of preparation hitherto known is highly limited. So far, the almost only practical method is the condensation of organodichlorosilane with alkali metal (Wurtz type condensation). This method, however, requires drastic reaction conditions and hence, is very much limited in the type of substituent that is allowed to be located on the monomer. Although several modified or alternative methods such as sonochemical coupling of dichlorosilane promoted by alkali metal [9-11], transition metal catalyzed reaction of hydrosilane [12, 13], anionic polymerization of masked disilene [14, 15], and ring opening polymerization of cyclic organosilane [16, 17] have been proposed, they are not always extensively effective as preparative methods.
The electroreductive coupling of dichlorosilanes with mercury electrode has been reported by Hengge in 1976 as a method to form disilanes [18], though this method was not effective in the preparation of polysilanes [19, 20].
On the other hand, we have recently found that the electroreduction of organic compounds with Mg electrode promotes a variety of unique reactions which can not be attained without using the Mg electrode. The use of Mg electrodes was highly effective to the formation of Si-Si bond and the synthesis of high molecular weight polysilanes [21, 22].
In this chapter, we describe the details of the electroreductive synthesis of high molecular weight polysilane and some types of functionalized polysilanes and also polygermanes, including the additional information about the effects of electrode material and monomer concentration. We also demonstrate that our electroreduction system is successfully applied for the synthesis of the sequence-ordered oligosilanes and polysilanes.
The electroreduction of chlorodimethylphenylsilane (1a) was studied as the model reaction (Scheme 2) and carried out under a variety of reaction conditions. In the first place, the cathodic reduction was performed in a divided cell since Si-Si bond is electrochemically oxidized at the potential range 0.7-1.6V vs. SCE. The yield of 1,1,2,2-tetramethyl-1,2-diphenyldisilane (2a) was, however, unexpectedly low under this reaction conditions. In the next place, the idea of using a sacrificial electrode was studied in order to avoid the undesirable anodic oxidation of Si-Si bond in an undivided cell and it was found that the electroreduction of 1a with Mg electrode was highly effective for the formation of Si-Si bond and 2a was obtained in an excellent yield.
The material of electrode is one of the most important factors to control the formation of Si-Si bond (Table 1). When a solution of 1a in dry THF containing LiClO4 as a supporting electrolyte was electrochemically reduced with Mg cathode and anode with a constant current (current density = 30 mA/cm2, supplied electricity = 2.0 F/mol), the coupling product 2a was obtained in 92% isolated yield (entry 1). The results that Pt, carbon, or Zn is not effective electrode in the formation of Si-Si bond (entries 2-4) clearly indicate that Mg plays some important roles in the formation of Si-Si bond. Although details of the role of Mg in the mechanism of formation of Si-Si bond is not always clear at present, the unique reactivity of Mg electrode is undoubtedly shown in this reaction.
The cathodic coupling of other organochlorosilanes was carried out under the optimized reaction condition, that is, Mg cathode and anode were alternated with the interval of 1 min., supporting electrolyte was LiClO4, solvent was THF, and the electricity passed was 2.0 F/mol (Scheme 3). The results summarized in Table 2 show the high potentiality of this method in the synthesis of a variety of disilanes. Moreover, it is remarkable that the extent of contamination with siloxane (Si-O-Si) was less than 2%.
entry | anode | cathode | alternationb | yield of 2a, %c |
1 | Mg | Mg | yes | 92 |
2 | Pt | Pt | yes | 0 |
3 | C | C | yes | 0 |
4 | Zn | Zn | yes | trace |
5 | Pt | Mg | no | 0 |
6 | Mg | Pt | no | 93d |
Effect of electrode materials in the electroreductive formation of 1,1,2,2-tetramethyl-1,2-diphenyldisilane (2a)a
entry | chlorosilanes 1 | yield of 2, %a | ||||
R1 | R2 | R3 | ||||
1 | 1b | Me | Me | Me | 2b | 82 |
2 | 1c | Me | Ph | Ph | 2c | 77 |
3 | 1d | Ph | Ph | Ph | 2d | 85 |
Electroreductive synthesis of disilanes 2
Two types of mechanism may be proposed to this electroreductive Si-Si bond forming reaction. The first prosible mechanism is a radical coupling in which a silyl radical formed by one electron reduction of the starting chlorosilane couples with another silyl radical to give the disilane. In the second mechanism, two-electron reduction of the chlorosilane yields an active species equivalent to silyl anion which reacts with chlorosilane to give a dimer. In order to have an insight into the mechanism, the products obtained in the mixed system of chlorotrimethylsilane (1b) and chlorotriphenylsilane (1d) (1b : 1d = 1 : 1) were studied in detail (Scheme 4). The resulting products were 1 : 1 mixture of the mixed coupling product (2e) and the homocoupling product of 1d (hexaphenyldisilane (2d)), whereas the homocoupling product of 1b, hexamethyldisilane (2b) was not found in the products at all. This result seems to agree with the anionic mechanism. That is, 1d is first reduced to triphenylsilyl anion that reacts with 1b and 1d to afford disilanes 2e and 2d respectively. The electrophilicity of chlorosilanes 1b and 1d are high enough to be attacked equally by the triphenylsilyl anion at the half conversion (supplied electricity = 1 F/mol based on the total amount of 1b and 1d). In the radical mechanism, however, if only 1d is reduced to yield a radical, the formation of 2e is not reasonable, whereas if two types of radical are formed by the reduction of both 1b and 1d, the absence of 2b in the products is unreasonable. Thus, the anionic mechanism is the most reasonable in this coupling reaction. The fact that the reduction potential of 1d is much more positive than that of 1b also supports the above mentioned reaction mechanism.
This method is also applicable to the synthesis of trisilanes and tetrasilanes. For example, the electroreductive cross coupling of organodichlorosilanes (3) with chlorotrimethylsilane (1b) (5 equivalent to 3) gave the corresponding trisilanes 4 in moderate to good material yields (Scheme 5) and that of 1,2-dichloro-1,1,2-trimethyl-2-phenyldisilane (5) and 1b (5 equivalent to 5) gave tetrasilane 6 in 55% yield (Scheme 6). Trisilane 4c is a key intermediate for the photochemical synthesis of tetramesityldisilene which is known as an isolable disilene.
The mildness of the reaction conditions of this electroreductive method is favorable for the synthesis of oligosilanes having various functionalities such as Si-H bonds which are known to be reactive under radical or anionic condition. The electroreductive cross-coupling reaction of chlorodimethylsilane (7) with dichlorodiphenylsilane (3b), in fact, gave the corresponding trisilane 8 (Scheme 7). The Si-H bond was readily transformed to the Si-Cl bond by the treatment with catalytic amount of benzoylperoxide in carbontetrachloride. Using this method 1,3-dihydro-1,1,3,3-tetramethyl-2,2-diphenyltrisilane (8) was transformed to the corresponding chloride 9 (Scheme 7). The further electroreduction of 1,3-dichloro-1,1,3,3-tetramethyl-2,2-diphenyltrisilane (9) with chlorodimethylsilane (7) gave the corresponding pentasilane 10 (Scheme 8), and these sequences were utilized for the synthesis of the odd-numbered oligosilanes. In the same manner, the even-numbered oligosilanes can be prepared. For instance, 1,4-dichloro-1,1,2,3,3,4,4-heptamethyl-2-phenyltetrasilane (11) can be prepared by the reaction between 7 and 1,2-dichloro-1,1,2-trimethyl-1-phenyldisilane (5) (Scheme 9). Accordingly, the electroreductive cross-coupling reactions followed by the chlorination provided a powerful method for the stepwise elongation of Si-Si bonds and synthesis of sequence-controlled oligosilanes.
Electroreduction of dichloromethylphenylsilane (3a) (Scheme 10) carried out under the above mentioned reaction conditions gave polymethylphenylsilane (13) in low yield (Table 3, entries 1, 2). The sonication of ultrasound was found to be necessary for the electroreductive polymerization of 3a (entry 3). The low yield of 13 may be explained by the difficulty of keeping the electric current in a suitable level due to the increase of the terminal voltage with progress of the reaction. This difficulty was overcome by the alternation of anode and cathode with a suitable interval (15 sec., Table 3, entry 4) and the material yield of 13 was remarkably improved.
entry | alternationc | sonicationd | Mne | Mw/Mne | yield of 13, %f, g |
1 | no | no | - | - | - |
2 | yes | no | 4000 | 1.4 | 7 |
3 | no | yes | 3900 | 1.4 | 17 |
4 | yes | yes | 5200 | 1.5 | 43 |
Electroreductive synthesis of poly(methylphenylsilane) (13)a, b
Mg is a remarkably effective material of electrode also for the formation of 13 (Table 4), whereas Al gave low yield (entry 4) and other materials such as Cu and Ni were rather ineffective (entries 2, 3). The electroreduction systems using Al or Cu anode in other electrolytes (Al anode/Bu4NCl/DME [24], Al anode/LiCl/THF-HMPT [25], or Cu/Bu4NClO4/DME [26]) have been also reported, however, the molecular weight of the resulting polysilanes is relatively low. Other than Mg electrodes, the use of Ag anode and Pt cathode in DME containing Bu4NCLO4 is also reported to be effective to obtain high molecular weight polysilane [27, 28].
entry | electrode materials | Mnb | Mw/Mnb | yield of 13, %c, d |
1 | Mg | 5200 | 1.5 | 43 |
2 | Cu | 700 | 1.1 | -e |
3 | Ni | 640 | 1.1 | -e |
4 | Al | 4700 | 1.5 | 15 |
Effect of electrode materials in the electroreductive synthesis of poly(methylphenylsilane) (13)
The effect of monomer concentration was investigated in order to obtain high molecular weight polysilane (Table 5). The molecular weight of 13 becomes higher with the increase in the concentration of 3a. The molecular weight (Mn) of 13 was, for instance, 31,000 when the electroreduction of 3a was carried out under high concentration condition (1.2 mol/L) at 0.5 F/mol of supplied electricity though the material yield of 13 decreased.
The most satisfactory result, in which material yield was 79 % and molecular weight (Mn) was 9900, was obtained when the concentration of 3a was 0.67 mol/L (entry 2). The polysilane 13 obtained here showed relatively sharp monomodal distribution of molecular weight in the elution profile of GPC, whereas the polysilanes prepared by the alkali metal condensation method usually showed broad bimodal distribution.
entry | monomer 3a, mol/L | supplied electricity, F/mol | Mnb | Mw/Mnb | yield of 13, %c, d |
1 | 0.33 | 4.0 | 5200 | 1.5 | 43 |
2 | 0.67 | 4.0 | 9900 | 2.1 | 79 |
3 | 2.5 | 2.2 | 18000 | 2.1 | 43 |
4 | 6.3 | 0.8 | 19000 | 2.8 | 15 |
5 | 12 | 0.5 | 31000 | 1.8 | 8 |
Effect of monomer concentration in the electroreductive synthesis of 13a
The mechanism of electroreductive formation of polysilane is not always perfectly clear, though the initial step of reaction is obviously the reduction of 3a to a silyl anion species 14. Two types of reaction patterns may be proposable to the propagation step. In the first case, the reaction of 14 with 3a gives a dimer that yields trimer, tetramer, and finally polymer upon repeated reaction with 14 (Scheme 11). In another pattern of the propagation, the oligomers such as dimer, trimer, and the like 15 are reduced to give the oligomeric active species, which then react with oligomer 15 or 3a to give finally polymer. (Scheme 12). Although it is not always possible to specify the extent of contribution of each pattern to the propagation step, the former reaction probably proceeds mainly, since the electrochemical reduction of oligomeric silyl chloride 15 may be rather difficult when it is analogized with the electroreduction of long chain alkyl chlorides.
The mildness of the polymerization conditions of the electroreductive method is favorable for the synthesis of the polysilanes having a variety of hydroxyl-related functional groups. The electroreduction of a mixture of 3a and the dichlorosilanes having protected hydoxyphenyl groups (3d-g) with Mg electrode afforded the corresponding copolymers 16 (16d-g, Scheme 13), and the deprotection of the resulting copolymers gave the polysilanes having hydroxyl groups. The reactivity of 3d-g highly depends on the type of protecting group (Table 6). Homopolymerization of 3g, for example, gave 16g (entry 10), whereas other monomers did not afford polymers but oligomeric compounds (entries 3, 5, 8).
The modification or the property of polysilane must be achieved by using the hydroxyl group located on the polymer 16d as a key functional group. The deprotection of the methoxymethyl group of 16d (Table 6, entry 1) with 10%HCl aqueous solution followed by the reaction with hexamethylene diisocyanate resulted in a remarkable increase in the molecular weight with indicating the linkage of the polymer chain (Scheme 14).
entry | charged mol% of 3d-gb | yield of 16, %c, d | Mne | Mw/Mne |
1 | 7 (3d) | 79 (7) | 9900 | 1.9 |
2 | 10 (3d) | 57 (12) | 6900 | 1.7 |
3 | 100 (3d) | 28 (100) | 1100 | 1.2 |
4 | 10 (3e) | 36 (11) | 6100 | 1.5 |
5 | 100 (3e) | -f (100) | 1100 | 1.2 |
6 | 10 (3f) | 50 (6) | 4500 | 1.3 |
7 | 50 (3f) | 22 (46) | 4600 | 1.3 |
8 | 100 (3f) | -f (100) | 1700 | 1.3 |
9 | 10 (3g) | 56 (17) | 4600 | 1.3 |
10 | 100 (3g) | 57 (100) | 4000 | 1.1 |
Electroreductive synthesis of functionalized polysilanesa
The electroreductive polymerization of the dichlorooligosilanes is highly promising for the synthesis of sequence-ordered polysilanes. The electroreduction of dicholodisilane 5 was found to give the corresponding polysilane 18 consisting of disilane units (Scheme 15). The electroreductive polymerization was carried out under a variety of reaction conditions, however, the yield of the resulting polymer was very low (Table 7). It is probably due to high reactivity of the disilene intermediate formed by the electroreduction of 5. In fact, the addition of naphthalene, which could make a masked disilene intermediate, into the reaction system slightly increased the yield of the polysilane 18 (entry 6).
entry | dichlorosilane 5, mol/L | supplied electricity, F/mol | polysilane 18 | ||
Mnb | Mw/Mnb | yield, %c | |||
1 | 0.11 | 4 | 2900 | 2.7 | 2.7 |
2 | 0.33 | 4 | 3600 | 2.3 | 2.7 |
3 | 0.67 | 4 | 2100 | 2.9 | 1.0 |
4 | 0.33 | 2 | 2800 | 2.7 | 3.9 |
5 | 0.33 | 6 | 3000 | 3.0 | 1.0 |
6c | 0.33 | 4 | 2500 | 1.8 | 13.0 |
Electroreduction polymerization of dichlorosilane 5a
Dichlorooligosilanes, such as dichlorotrisilane 9 was found to be good monomers for the electroreductive synthesis of the polysilanes having longer sequence units (Schemes 16). The temperature control is found to be very important in the electroreductive polymerization of 9 (Table 8). The reaction at higher temperature, the backbiting reaction of the propagating polymer proceeded forming cyclohexasilane as a by-product (entry 1). This side reaction was successfully suppressed when the reaction was carried out below 0°C, and polysilanes 19 having relatively high molecular weight were obtained (entries 3, 4). In the optimized reaction conditions, the electroreduction of dichlorotetrasilane 12 gave the corresponding polysilane 20, units of which were ordered in four sequences in satisfactory yield (Scheme 17). The polymerizability of dichlorooligosilanes under the electroreduction conditions seems to be mainly affected by the substituents on the chlorinated terminal silicon atom, and this fact provides a wide possibility to design the oligosilane sequences of the inner silicon atoms.
entry | polymerization temperature, °C | Mnb | Mw/Mnb | yield of 19, %c |
1 | 18 | 3800 | 1.44 | (42)d |
2 | 0 | 4700 | 1.87 | 50 |
3 | -10 | 5500 | 1.54 | 35 |
4 | -15 | 4400 | 1.42 | 16 |
Electroreductive polymerization of dichlorosilane 9a
The disilane additives, which are generated in situ in electroreductive coupling of the corresponding chlorosilanes, were found to be effective to the promotion of the electroreductive polymerization of dichloromethylphenylsilane (3a) and the control of the molecular weight distribution of the resulting polysilanes (Scheme 18, Table 9). The electroreduction of dichlorosilane 3a in the presence of 1,1,1-trimethyl-2,2,2-triphenyldisilane gives the corresponding polysilane 13 in 56% yield, and the number average molecular weight and the molecular weight distribution are determined by GPC to be 3000, and 1.10 respectively (Table 9, entry 3). The reduction of dichloromethylphenylsilane (3a) by Wurtz type condensation using metal lithium in the presence of catalytic amount 1,1,1-trimethyl-2,2,2-triphenyldisilane affords five- and six-members ring products [31]. On the other hand, the cyclosilanes are not detected under the electroreductive conditions. The use of 1,1,12,2,2-hexaphenyldisilane affords the polysilane 13 in 59% yield, and the Mw/Mn is 1.08 (entry 4). Thus, the polysilanes prepared in the presence of the disilane additive containing triphenylsilyl group show narrower molecular weight distributions than the polysilane prepared without the disilane additive.
The mechanism for the triphenylsilyl substituted disilane to control the electroreductive polymerization suggested is as follows (Scheme 19). The electroreductively generated reactive silyl anion species at the terminus of the propagating polymer is trapped with the disilane forming a relatively stable triphenylsilyl anion and inhibits the undesirable side reactions such as backbiting reaction. The triphenylsilyl anion attacks as a nucleophile to the chlorinated silicon atom at the terminal of the propagating polymer to give the
entry | disilane additivesb | Mnc | Mw/Mnc | yield of 2a, %d |
1 | - | 3200 | 1.65 | 38 |
2 | Me3SiSiMe3 | 3700 | 1.39 | 54 |
3 | Me3SiSiPh3 | 3000 | 1.10 | 56 |
4 | Ph3SiSiPh3 | 2600 | 1.08 | 59 |
Electroreductive polymerizartion of dichlomethylphenylsilane (3a) in the presence of disilane additivesa
triphenylsilyl group terminated polysilane. The resulting polysilane is isolable but does not lose its polymerizability completely since the triphenylsilyl group at the terminal position acts as an activator, that is, it probably reacts as a macroinitiator.
The triphenylsilyl group terminated polysilanes have been synthesized by the electroreductive polymerization of dichloromethylphenylsilane (3a) in the presence of the electroreductively prepared disilane additives 2. The electroreductive termination with chlorotriphenylsilane (1d) was carried out to ensure the terminus of the resulting polysilane for triphenylsilyl group. The polysilanes were obtained as white powders in 15-32% yields after reprecipitation from benzene-ethanol, and the number average molecular weights were estimated by GPC to be 3000-3740 (Table 10). By using the isolated triphenylsilyl group-terminated poly(methylphenylsilane) 13 as a macroinitiator, the electroreductive polymerization of dibutyldichlorosilane (3h) was carried out (Scheme 20, Table 10). Under these conditions dichlorsilane 3h was first electroreduced to form the corresponding oligomeric silyl anion. Electroreductive copolymerization was found to proceed by the attack of the oligomeric silyl anion to triphenylsilyl group-terminated polysilane 13 and further electroreductive condensation with dichlorosilane 3h affording the corresponding copolymer, polymethylphenylsilane-block-polydibutylsilane (21), in 16-38% yields depending on the disilane additives (entries 1-3). The molecular weight of the copolymer obtained from the polysilane 13 (Mn = 3350) was 4730 (entry 3). The GPC profiles of the resulting copolymers 21 were monomodal and the polydispersity index values (Mw/Mn) were 1.2-1.4. The repeat unit ratio (-Si(Me)Ph- : -SiBu2-) of the resulting copolymer 21 (entry 3) was 75 : 25, which showed a good agreement with the calculated ratio (74 : 26).
Polydibutylsilane-block-polymethylphenylsilane (21’) was also obtained by using triphenylsilyl group-terminated polydibutylsilane (22) as a macroinitiator (Scheme 21). The electroreductive polymerization of dibutyldichlorosilane (3h) in the presence of the disilane 2d followed by electroreductive termination with chlorotriphenylsilane (1d) afforded the macroinitiator 22 (Mn = 3950, Mw/Mn = 1.7). The electroreductive polymerization of dichloromethylphenylsilane (3a) was found to initiate from 22 producing the corresponding copolymer 21’ in 25% yield, and the molecular weight of 21’ was 4390 (Scheme 21). The polydispersity index values (Mw/Mn) was 1.3, and the repeat unit ratio (-SiBu2- : -Si(Me)Ph-) was 61 : 39.
entry | preparation of the macroinitiator 13a | polymethylphenylsilane-block-polydibutylsilane (21)b | |||||||
disilane additives | Mnc | Mw/Mnc | Mnc | Mw/Mnc | m : n | yield, %f | |||
observedd | calculatede | ||||||||
1 | Me3SiSiMe3 | 3740 | 1.9 | 5530 | 1.4 | 83 : 17 | 70 : 30 | 16 | |
2 | Me3SiSiPh3 | 3000 | 1.3 | 4080 | 1.4 | 66 : 34 | 77 : 23 | 28 | |
3 | Ph3SiSiPh3 | 3350 | 1.4 | 4730 | 1.2 | 75 : 25 | 74 : 26 | 38 |
Electroreductive block copolymerization with dibutyldichlorosilane (3h) using triphenylsilyl group-terminated poly(methylphenylsilane) (13) as a macroinitiator
The UV absorption spectra of the resulting polysilane 21 (entry 3, Table 10) was compared with those of poly(methylphenylsilane) (Mn = 3350, Mw/Mn = 1.4), poly(dibutylsilane) (Mn = 3950, Mw/Mn = 1.7), and poly(methylphenylsilane-co-dibutylsilane) (random copolymer) (Figure). Poly(methylphenylsilane) showed a π-π* band at 271 nm (εmax = 4831) and a σ-σ* band at 326 nm (εmax = 4766) at 4°C (Figure (a)). Poly(methylphenylsilane) (Mw = 9000) prepared by Wurtz coupling under sonication was reported to show a σ-σ* band at 332 nm (εmax = 4100) [33]. The λmax value of the electroreductively prepared poly(methyphenylsilane) was a little shorter and it is probably due to relatively lower molecular weight. In fact Wurtz coupling-synthesized poly(methylphenylsilane) having 5000 of Mw showed a σ-σ* band at 328 nm [34]. Poly(dibutylsilane) showed only a σ-σ* band at 309 nm, and the εmax value (9611) was two times higher than that of poly(methylphenylsilane) (Figure (b)). The UV absorption spectrum of the helical form of poly(di-n-alkylsilane)s is typically centered near 315 nm, while the more planar conformation is typically centered at 375 nm [35, 36]. Poly(dibutylsilane) obtained in this study showed the λmax value corresponding to helical backbone conformation. The εmax of the σ-σ* band of poly(methylphenylsilane) was observed to decrease with an increase in temperature [5]. The UV spectra of copolymer 5 showed a π-π* band at 273 nm (εmax = 2723) and a σ-σ* band at 306 nm (εmax = 2949) (Figure (d)), while the random copolymer showed a π-π* band at 272 nm (εmax = 2438) and a σ-σ* band at 309 nm (εmax = 2065) (Figure (c)). The εmax value of the σ-σ* band of copolymer 21 was higher than that of the random copolymer, and less temperature dependence of εmax was observed in the spectrum of copolymer 21. These results indicate that copolymer 21 has long sequences of dibutylsilylene units, that is, block structure.
UV absorption spectra of (a) poly(methylphenylsilane) (Mn = 3350, Mw/Mn = 1.4), (b) poly(dibutylsilane) (Mn = 3950, Mw/Mn = 1.7), (c) poly(methylphenylsilane-co-dibutylsilane) (Mn = 4340, Mw/Mn = 1.5, -Si(Me)Ph- : -SiBu2- = 67 : 33), and (d) poly(methylphenylsilane)-block-poly(dibutylsilane) (Mn = 4080, Mw/Mn = 1.4, -Si(Me)Ph- : -SiBu2- = 66 : 34) in THF at 0, 10, 20, 25, and 30°C.
The formation of Si-Si bonds was achievable by the electroreductive condensation of organochlorosilanes with Mg sacrificial electrode. Disilanes, trisilanes, and tetrasilanes were readily obtained in good to moderate yield. Moreover, this method was also remarkably effective to the synthesis of polysilanes. The molecular weight and yield of the polymers was controlled by the concentration of monomers and the supplied electricity. The mildness of the reaction conditions allowed to use a wide variety of monomers, and enabled the synthesis of the functionalized polysilanes and the structure-controlled polysilanes. The electroreductive polymerization of the dichlorooligosilanes was highly useful for the synthesis of sequence-ordered polysilanes. Moreover, this electroreductive method also provided a new procedure to synthesize well-controlled di-block polysilane copolymers. Since the present electroreductive polymerization requires only a single compartment cell, it is undoubtedly one of the simplest and most powerful tools for synthesis of polysilanes.
The world’s human population increases by approximately 240,000 people every day: it is expected to reach 8 billion by 2025 and approximately 9.6 billion by 2050. Cultivated land is at a near-maximum, yet estimates predict that food production must be increased by 70% for worldwide peace to persist circa 2050 [1]. Thus, producing sufficient food to meet the ever-growing demand for this rising population is an exceptional challenge to humanity. To succeed at this vital objective, we must build more efficient—yet sustainable—food production devices, farms, and infrastructures. To accomplish that objective, the precision farming concept—a set of methods and techniques to accurately manage variations in the field to increase crop productivity, business profitability, and ecosystem sustainability—has provided some remarkable solutions.
Figure 1 summarizes the cycle of precision agriculture and distinguishes the activities based on analysis and planning (right) from those that rely on providing motion (left). The solutions for activities illustrated in Figure 1 right are being based on information and communication technologies (ICT), whereas the activities on the left rely on tractors, essential devices in current agriculture, that are being automated and robotized and will be also critical in future agriculture (smart farms).
UGVs in the cycle of precision agriculture.
The activities indicated in Figure 1 left can be applied autonomously in an isolated manner, i.e., a fertilization-spreading task, can be performed autonomously if the appropriate implement tank has been filled with fertilizer and attached to a fueled autonomous tractor (UGV); the same concept is applicable to planting and spraying. In addition, harvesting systems must offload the yield every time their collectors are full. However, tasks such as refilling, refueling/recharging, implement attachment, and crop offloading are currently primarily performed manually. The question that arises is: would it be possible to automate all these activities? And if so, would it be possible to combine these activities with other already automated farm management activities to configure a fully automated system resembling the paradigm of the fully automated factory? Then, the combination becomes a fully automated farm in which humans are relegated to mere supervisors. Furthermore, exploiting this parallelism, can we push new developments for farms to mimic the smart factory model? This is the smart farm concept that represents a step forward from the automated farm into a fully connected and flexible system capable of (i) optimizing system performances across a wider network, (ii) learning from new conditions in real- or quasi-real time, (iii) adapting the system to new conditions, and (iv) executing complete production processes in an autonomous way [2]. A smart farm should rely on autonomous decision-making to (i) ensure asset efficiency, (ii) obtain better product quality, (iii) reduce costs, (iv) improve product safety and environmental sustainability, (v) reduce delivery time to consumers, and (vi) increase market share and profitability and stabilize the labor force.
Achieving the smart farm is a long-term mission that will demand design modifications and further improvements on systems and components of very dissimilar natures that are currently being used in agriculture. Some of these systems are outdoor autonomous vehicles or (more accurately) UGVs, which are essential in future agriculture for moving sensors and implementing to cover crop fields accurately and guarantee accurate perception and actuation (soil preparation, crop treatments, harvest, etc.). Thus, this chapter is devoted to bringing forward the features that UGVs should offer to achieve the smart farm concept. Solutions are focused on incorporating the new paradigms defined for smart factories while providing full mobility of the UGVs. These two activities will enable the definition of UGV requirements for smart farm applications.
To this end, the next section addresses the needs of UGVs in smart farms. Then, two main approaches to configure solutions for UGVs in agricultural tasks are described: the automation of conventional vehicles and specifically designed mobile platforms. Their advantages and shortcomings regarding their working features are highlighted. This material enables the definition of other operating characteristics of UGVs to meet the smart farm requirements. Finally, the last section presents some conclusions.
Ground mobile robots, equipped with advanced technologies for positioning and orientation, navigation, planning, and sensing, have already demonstrated their advantages in outdoor applications in industries such as mining [3], farming, and forestry [4, 5]. The commercial availability of GNSS has provided easy ways to configure autonomous vehicles or navigation systems to assist drivers in outdoor environments, especially in agriculture, where many highly accurate vehicle steering systems have become available [6, 7]. These systems aid operators in the precise guidance of tractors using LIDAR (light/laser detection and ranging) or GNSS technology but do not endow a vehicle or tool with any level of autonomy. Nevertheless, other critical technologies must also be incorporated to configure UGVs, such as the safety systems responsible for detecting obstacles in the robots’ path and safeguarding humans and animals in the robots’ surroundings as well as preventing collisions with obstacles or other robots. Finally, robot communications with operators and external servers (cloud technologies) through wireless communications that include the use of cyber-physical systems (CPSs) [8] and Internet of things (IoT) [9] techniques will be essential to incorporate decision-making systems based on big data analysis. Such integration will enable the expansion of decision processes into fields such as machine learning and artificial intelligence. Smart factories are based on the strongly intertwined concepts of CPS, IoT, big data, and cloud computing, and UGVs for smart farms should be based on the same principles to minimize the traditional delays in applying the same technologies to industry and agriculture.
The technology required to deploy more robotic systems into agriculture is available today, as are the clear economic and environmental benefits of doing so. For example, the global market for mobile robots, in which agricultural robots are a part, is expected to increase at a compound annual growth rate of over 15% from 2017 to 2025, according to recent forecast reports [10]. Nevertheless, manufacturers of agricultural machinery seem to be reluctant to commercialize fully robotic systems, although they have not missed the marketing potential of showing concepts [11, 12]. In any event, according to the Standing Committee on Agricultural Research [13], further efforts should be made by both researchers and private companies to invent new solutions.
Most of the robotics and automation systems that will be used in precision agriculture—including systems for fertilizing, planting, spraying, scouting, and harvesting (Figure 1)—will require the coordination of detection devices, agricultural implements, farm managing systems, and UGVs. Thus, several research groups and companies have been working on such systems. Specifically, two trends can be identified in the development of UGVs: the automation of conventional agricultural vehicles (tractors) and the development of specifically designed mobile platforms. The following sections discuss these two types of vehicles.
The tractor has been the central vehicle for executing most of the work required in a crop field. Equipped with the proper accessories, this machine can till, plant, fertilize, spray, haul, mow, and even harvest. Their adaptability to dissimilar tasks makes tractors a prime target for automation, which would enable productivity increases, improve safety, and reduce operational costs. Figure 2 shows an example of the technologies and equipment for automating agricultural tractors.
An example of agricultural tractor automation‑distribution of sensorial and actuation systems for transforming an agricultural tractor into a UGV (Gonzalez-de-Santos et al., 2017).
Numerous worldwide approaches to automating diverse types of tractors have been researched and developed since 1995 when the first GNSS was made available to the international civilian community of users, which opened the door for GPS-guided agricultural vehicles (auto-steering) and controlled-traffic farming.
The first evaluations of GPS systems for vehicle guidance in agriculture were also published in 1995 [14] demonstrating its potential and encouraging many research groups around the world to automate diverse types of tractors. The earliest attempts were made at Stanford University in 1996, where an automatic control system for an agricultural tractor was developed and tested on a large farm [15]. The system used a location system with four GPS antennas. Around the same time, researchers at the University of Illinois, USA, developed a guidance system for an autonomous tractor based on sensor fusion that included machine vision, real-time kinematics GPS (RTK-GPS), and a geometric direction sensor (GDS). The fusion integration methodology was based on an extended Kalman filter (EKF) and a two-dimensional probability-density-function statistical method. This system achieved a lateral average error of approximately 0.084 m at approximately 2.3 m s−1 [16].
A few years later, researchers at Carnegie Mellon University, USA, developed some projects that made significant contributions. The Demeter project was conceived as a next-generation self-propelled hay harvester for agricultural operations, and it became the most representative example of such activity [17]. The positional data was fused from a differential GPS, a wheel encoder (dead reckoning), and gyroscopic system sensors. The project resulted in a system that allowed an expert harvesting operator to harvest a field once, thus programming the field. Subsequently, an operator with lesser skill could “playback” the programmed field at a later date. The semi-autonomous agricultural spraying project, developed by the same research group, was devoted to making pesticide spraying significantly cheaper, safer, and more environmentally friendly [18]. This system enabled a remote operator to oversee the nighttime operation of up to four spraying vehicles. Another example is research conducted at the University of Florida, USA, [19], in which two individual autonomous guidance systems for use in a citrus grove were developed and tested along curved paths at a speed of approximately 3.1 m s−1. One system, based on machine vision, achieved an average guidance error of approximately 0.028 m. The other system, based on LIDAR guidance, achieved an average error of approximately 0.025 m.
Similar activities started in Europe in the 2000s. One example is the work performed at LASMEA-CEMAGREF, France, in 2001, which evaluated the possibilities of achieving recording-path tracking using a carrier phase differential GPS (CP-DGPS), as the only sensor. The vehicle heading was derived according to a Kalman state reconstructor and a nonlinear velocity independent control law was designed that relied on chained systems properties [20].
A relevant example of integrating UGVs with automated tools is the work conducted at the University of Aarhus and the University of Copenhagen, Denmark [21]. The system comprised an autonomous ground vehicle and a side shifting arrangement affixed to a weeding implement. Both the vehicle and the implement were equipped with RTK-GPS; thus, the two subsystems provided their own positions, allowing the vehicle to follow predefined GPS paths and enabling the implement to act on each individual plant, whose positions were automatically obtained during seeding.
Lately, some similar automations of agricultural tractors have been conducted using more modern equipment [22, 23], and some tractor manufacturers have already presented noncommercial autonomous tractors [11, 12]. This tendency to automate existing tractors has been applied to other types of lightweight vehicles for specific tasks in orchards such as tree pruning and training, blossom and fruit thinning, fruit harvesting, mowing, spraying, and sensing [24]. Table 1 summarizes the UGVs based on commercial vehicles for agricultural tasks.
Institution | Year | Description |
---|---|---|
Stanford University (USA) [15] | 1996 | Automatic large-farm tractor using 4 GPS antennas |
University of Illinois (USA) [16] | 1998 | A guidance system using a sensor based on machine vision, an RTK-GPS, and a GDS |
Carnegie Mellon University (USA)—Demeter project [17] | 1999 | A self-propelled hay harvester for agricultural operations |
Carnegie Mellon University (USA)—Autonomous Agricultural Spraying project [18] | 2002 | A ground-based vehicles for pesticide spraying |
LASMEA-CEMAGREF (France) [20] | 2001 | This study investigated the possibility of achieving vehicle guiding using a CP-DGPS as the only sensor |
University of Florida (USA) [19] | 2006 | An autonomous guidance system for citrus groves based on machine vision and LADAR |
University of Aarhus and the University of Copenhagen (Denmark) [21] | 2008 | An automatic intra-row weed control system connected to an unmanned tractor |
RHEA consortium (EU) [22] | 2014 | A fleet (3 units) of tractors that cooperated and collaborated in physical/chemical weed control and pesticide applications for trees |
Carnegie Mellon University (USA) [24] | 2015 | Self-driving orchard vehicles for orchard tasks |
University of Leuven (Belgium) [23] | 2015 | Tractor guidance using model predictive control for yaw dynamics |
UGVs based on commercial vehicles.
Nevertheless, UGVs suitable for agriculture remain far from commercialization, although many intermediate results have been incorporated into agricultural equipment—from harvesting to precise herbicide application. Essentially, these systems are installed on tractors owned by farmers and generally consist of a computer (the controller), a device for steering control, a localization system (mostly based on RTK-GPS), and a safety system (mostly based on LIDAR). Many of these systems are compatible only with advanced tractors that feature ISOBUS control technology [25], through which controllers connected to the ISOBUS can access other subsystems of the tractor (throttle, brakes, auxiliary valves, power takeoff, linkage, lights, etc.). Examples of these commercial systems are AutoDrive [26] and X-PERT [27].
An important shortcoming of these solutions is their lack of intelligence in solving problems, especially when obstacles are detected because they are not equipped with technology suitable for characterizing and identifying the obstacle type. This information is essential when defining any behavior other than simply stopping and waiting for the situation to be resolved. Another limitation of this approach is that the conventional configuration of a standard tractor driven by an operator is designed to maximize the productivity per hour; thus, the general architecture of the system (tractor plus equipment) is only roughly optimized.
The second approach to the configuration of mobile robots for agriculture is the development of autonomous ground vehicles with specific morphologies, where researchers develop ground mobile platforms inspired more by robotic principles than by tractor technologies. These platforms can be classified based on their locomotion system. Ground robots can be based on wheels, tracks, or legs. Although legged robots have high ground adaptability (that enables the vehicles to work on irregular and sloped terrain) and intrinsic omnidirectionality (which minimizes the headlands and, thus, maximizes croplands) and offer soil protection (discrete points in contact with the ground that minimize ground damage and ground compaction, an important issue in agriculture), they are uncommon in agriculture; however, legged robots provide extraordinary features when combined with wheels that can configure a disruptive locomotion system for smart farms. Such a structure (which consists of legs with wheels as feet) is known as a wheel-legged robot. The following sections present the characteristics, advantages, and disadvantages of these specifically designed types of robots.
The structure of a wheeled mobile platform depends on the following features:
Number of wheels: Three nonaligned wheels are the minimum to ensure platform static stability. However, most field robots are based on four wheels, an approach that increases the static and dynamic stability margins [28].
Wheel orientation type: An ordinary wheel can be installed on a platform in different ways that strongly determine the platform characteristics. Several wheel types can be considered:
Fixed wheel: This wheel is connected to the platform in such a way that the plane of the wheel is perpendicular to the platform and its angle (orientation) cannot change.
Orienting wheel: The wheel plane can change its orientation angle using an orientation actuator.
Castor wheel: The wheel can rotate freely around an offset steering joint. Thus, its orientation can change freely.
Wheel power type: Depending on whether wheels are powered, they can also be classified as follows:
Passive wheel: The wheel rotates freely around its shaft and does not provide power.
Active wheel: An actuator rotates the wheel to provide power.
Wheel arrangement: Different combinations of wheel types produce mobile platforms with substantially different steering schemes and characteristics.
Coordinated steering scheme: Two fixed active wheels at the rear of the platform coupled with two passive orienting wheels at the front of the platform are the most common wheel arrangement for vehicles. To maintain all wheels in a pure rolling condition during a turn, the wheels need to follow curved paths with different radii originating from a common center [29]. A special steering mechanism, the Ackermann steering system, which consists of a 4-bar trapezoidal mechanism (Figure 3a), can mechanically manage the angles of the two steering wheels. This system is used in all the vehicles presented in Table 2. It features medium mechanical complexity and medium control complexity. One advantage of this system is that a single actuator can steer both wheels. However, independent steering requires at least three actuators for steering and power (Figure 3b).
Skid steering scheme: Perhaps the simplest structure for a mobile robot consists of four fixed, active wheels, one on each corner of the mobile platform. Skid steering is accomplished by producing a differential thrust between the left and right sides of the vehicle, causing a heading change (Figure 3c). The two wheels on one side can be powered independently or by a single actuator. Thus, the motion of the wheels in the same direction produces backward/forward platform motion; and the motion of the wheels on one side in the opposite direction to the motion of wheels on the other side produces platform rotation.
Independent steering scheme: An independent steering scheme controls each wheel, moving it to the desired orientation angle and rotation speed (Figure 3d). This steering scheme makes wheel coordination and wheel position accuracy more complex but provides some advantages in maneuverability. In addition, this scheme provides crab steering (sideways motion at any angle α; 0 ≤ α ≤ 2π) by aligning all wheels at an angle α with respect to the longitudinal axis of the mobile platform. Finally, the coordination of driving and steering results in more efficient maneuverability and reduces internal power losses caused by actuator fighting. The independent steering scheme requires eight actuators for a four-wheel vehicle.
Steering driving systems: (a) Ackermann steering system; (b) independent steering; (c) skid steering system and (d) independent steering and traction system.
Steering scheme | Characteristics |
---|---|
Coordinated | Advantages:
|
Skid | Advantages:
|
Independent | Advantages:
|
Characteristics of wheeled structures.
Table 2 summarizes the advantages and drawbacks of these schemes. Note that the number of actuators increases the total mass of a robot as well as its mechanical and control complexity (more motors, more drivers, more elaborate coordinating algorithms, etc.).
Some examples of wheeled mobile platforms for agriculture are the conventional tractor using the Ackermann steering system (Figure 2) with two front passive and steerable wheels and two rear fixed and active wheels.
Skid steering platforms can be found in many versions. For example,
Four fixed wheels placed in pairs on both sides of the robot
Two fixed tracks, each one placed longitudinally at each side of the robot,
Two fixed wheels placed at the front of the robot and two castor wheels placed at the rear (Figure 4c), etc.
Pictures of several specifically-designed agricultural platforms. (a) Robot for weed detection, courtesy of T. Bak, Department of Agricultural Engineering, Danish Institute of Agricultural Sciences; (b) ladybird, courtesy of J. P. Underwood, Australian Centre for Field Robotics at the University of Sydney [34]; (c) AgBot II, courtesy of O. Bawden, strategic Investment in Farm Robotics, Queensland University of Technology [31].
Regarding the independent steering scheme, the robot developed by Bak and Jakobsen [30] is one of the first representative examples (Figure 4a). This platform was designed specifically for agricultural tasks in wide-row crops and featured good ground clearance (approximately 0.5 m) and 1-m wheel separation. The platform is based on four-identical wheel modules. Each one includes a brushless electric motor that provides direct-drive power, and steering is achieved by a separate motor.
An example of a mobile platform under development that focuses on performing precision agricultural tasks is AgBot II (Figure 4c). This is a platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels. It is intended to work autonomously on both large-scale and horticultural crops, applying fertilizer, detecting and classifying weeds, and killing weeds either mechanically or chemically [31, 32]. Another robot is Robot for Intelligent Perception and Precision Application (RIPPA), which is a light, rugged, and easy-to-operate prototype for the vegetable growing industry. It is used for autonomous high-speed, spot spraying of weeds using a directed micro-dose of liquid when equipped with a variable injection intelligent precision applicator [33]. Another example is Ladybird (Figure 4b), an omnidirectional robot powered with batteries and solar panels that follows the independent steering scheme. The robot includes many sensors (i.e., hyperspectral cameras, thermal and infrared detecting systems, panoramic and stereovision cameras, LIDAR, and GPS) that enable assessing crop properties [34]. One more prototype, very close to commercialization, is Kongskilde Vibro Crop Robotti, which is a self-contained track-based platform that uses the skid steering scheme. It can be equipped with implements for precision seeding and mechanical row crop cleaning units. This robot can work for 2–4 hours at a 2–5 km h−1 rate and is supplied by captured electric energy [35].
These robots are targeted toward fertilizing, seeding, weed control, and gathering information, and they have similar characteristics in terms of weight, load capacity, operational speed, and morphology. Tools, instrumentation equipment, and agricultural implements are connected under the robot, and tasks are performed in the area just below the robot, which optimizes implement weight distribution. These robots have limitations for use on farmland with substantial (medium to high) slopes or gully erosion. Nevertheless, some mobile platforms are already commercially available. Two examples of these vehicles are the fruit robots Cäsar [36] and Greenbot [37].
Cäsar is a remote-controlled special-purpose vehicle that can perform temporarily autonomous operations in orchards and vineyards such as pest management, soil management, fertilization, harvesting, and transport. Similarly, Greenbot is a self-driving machine specially developed for professionals in the agricultural and horticultural sectors who perform regular, repetitious tasks. This vehicle can be used not only for fruit farming, horticulture, and arable farming but also in the urban sector and even at waterfronts or on roadsides.
Despite their current features, the existing robots lack flexibility and terrain adaptability to cope with diverse scenarios, and their safety features are limited. For example:
They focus only on orchard and vineyard activities.
They have ground clearance limitations.
They are unsuitable for rough terrain or slopes.
They must be manually guided to the working area rather than freely and autonomously moving to different working areas around the farm.
They possess no advanced detection systems for weed or soil identification, which limits their use to previously planned tasks related to selective treatment.
They lack dynamic safety systems capable of recognizing or interpreting safety issues; thus, they are incapable of rescheduling or solving problems by themselves.
In addition, existing UGVs for agriculture lack communication mechanisms for providing services through cloud technologies, CPS, and IoT techniques, crucial instruments to integrate decision-making systems based on big data analysis, as is being done in the smart factory concept.
Table 3 summarizes the diverse robotic platforms, and Figure 4 depicts some of these platforms.
Vehicle | Type* | Year | Description |
---|---|---|---|
AgBot II [32] | P | 2014 | A platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels |
Ladybird [34] | P | 2015 | An omnidirectional robot powered with batteries and solar panels that uses the independent steering scheme |
Greenbot [37] | C | 2015 | A self-driving robot for tasks in agriculture and horticulture |
Cäsar [36] | P | 2016 | A remotely controlled platform for temporary, autonomous use in fruit plantations and vineyards |
RIPPA [33] | P | 2016 | A light, rugged, and easy-to-operate prototype for the vegetable growing industry |
Vibro Crop Robotti [35] | C | 2017 | A self-contained track-based platform that uses the skid steering scheme |
Robots designed specifically for agriculture.
P-prototype; C-commercial.
The structure of a wheel-legged mobile platform depends on (i) the number of legs, (ii) the leg type, and (iii) the leg arrangement. The feet consist of 2-DOF steerable powered wheels as illustrated in Figure 5.
Wheel-legged structures. (a) 4-DOF articulated leg; (b) 3-DOF SCARA leg; (c) 2-DOF SCARA leg; (d) 1-DOF leg.
Number of legs: The minimum number of legs required for statically stable walking is four-three legs providing support in the form of a stable tripod while the other leg performs the transference phase [38]. Combining sequences of leg transferences with stable tripods produce a walking motion. A wheel-legged robot requires only three legs for translational motion, which provides additional terrain adaptation.
Leg type: Legs are based on the typical configurations of manipulators; thus, articulated, cylindrical, Cartesian, and pantographic configurations are the types used most often.
Leg arrangement: The normal arrangement for a 2n-legged robot is to distribute n legs uniformly on the longitudinal sides. Four-legged structures present some advantages regarding terrain adaptability, ground clearance, and track width control (crop adaptability) but also have some drawbacks, such as additional mechanical complexity (complex joints designs, including actuators and brakes) and control of redundant actuated systems, which exhibit complex interactions with the environment and make motion control more difficult than that of conventional wheeled platforms. Table 4 illustrates different theoretical wheel-legged structures.
Structure | Characteristics |
---|---|
A 4-DOF articulated leg with a 2-DOF wheeled foot (Figure 5a) | Advantages:
|
A 3-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5b) | Advantages:
|
A 2-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5c) | Advantages:
|
A 1-DOF leg with a 2-DOF wheeled foot (Figure 5d) | Advantages:
|
Wheel-legged structures.
Cylindrical, Selective Compliant Articulated Robot Arm (SCARA) or Cartesian.
Figure 6a illustrates the structure scheme of a wheel-legged robot based on the 3-DOF SCARA leg (See Figure 5b) with full terrain adaptability, ground clearance control, crop adaptability, and capability of walking, and Figure 6b shows the structure of a wheel-legged robot exhibiting full terrain adaptability, ground clearance control, and crop adaptability; however, it cannot walk under static stability.
Model of wheel-legs: (a) full terrain-crop adaptability, (b) full terrain and partial crop adaptability.
Another interesting example is the structure of BoniRob [39], a real wheel-legged platform for multipurpose agriculture applications, which consists of four independently steerable powered wheeled legs with the structure illustrated in Figure 5d (1-DOF legs with a 2-DOF wheeled foot). This robot can adjust the distance between its wheel sets, making it adaptable to many agricultural scenarios. The platform can be equipped with common sensorial systems used in robotic agricultural applications, such as LIDAR, inertial sensors, wheel odometry, and GPS. Moreover, the robotic platform can be retrofitted and upgraded with swappable application modules or tools for crop and weed identification, plant breeding applications, and weed control. This robotic platform is completely powered by electricity, which is more environmentally friendly but reduces its operational working time compared to conventional combustion-engine systems. Nevertheless, this robot configuration requires custom-built implements, which prevent the reuse of existing implements and, thus, jeopardize the introduction of this robot to the agricultural market.
In addition to their needed characteristics for infield operations, the robots fulfilling the demands of a smart farm will require the operating requirements summarized in the following paragraphs and Table 5.
Characteristics | Value |
---|---|
Dimensions | Length: ~3.0 m; width: ~1.50 m; height: ~1.00 m |
Weight | 1200–1700 kg |
Payload | 500–1000 kg |
Comments: These characteristics are estimations based on the current medium-sized vehicles reported in this chapter that are capable of carrying agricultural implements. Robots for carrying sensing systems can be truly small (low payloads), but vehicles for treatments need to carry medium to heavy loads (pesticides, fertilizes, etc.). For example, existing sprayers [45] weigh approximately 600–700 kg including 200–300 L of active ingredient. | |
Speed | 3–25 km h−1 |
Comments: Treatment speed is limited by the treatment process that depends on physical laws. However, robots need to move among working fields minimizing moving time; therefore, they must feature a reasonably high top speed. | |
Position accuracy | ±0.02 m |
Comments: The current DGPS accuracy seems to be sufficient for real applications. However, specific real-time localization systems, RTLS, can be used in small areas where GNSS is unavailable (radio frequency identification tags (RFID), ultra-wide band tags (UWB), etc.). These technologies will be essential in smart farms to ensure positioning precision in GNSS occluded areas. | |
Clearance | 0.35–1 m |
Comments: Weed control is performed at an early crop-growth stage; therefore, the minimum ground clearance of the robot must be approximately 0.35 m. A ground clearance of approximately 1 m will facilitate application of treatments at later crop-growth stages. The ideal approach would be to control the ground clearance to optimize the working height of the implements based on the crop. Existing robots cannot control their ground clearance, but some wheel-legged configurations can meet this specification (Figure 5a,b, and c). | |
Track width | 1.50–2.25 m |
Comments: To preserve crops in narrow-row situations, a tramline control is required; however, in wide-row crops, the tramlines must be located in the inter-row spacing. Taking maize as an example, which is planted at an inter-row spacing of approximately 0.75 m in some areas in Europe, a robot track width of 1.50 to 2.25 m is required to enable 2 or 3 rows to pass under the robot’s body. Controlling robot track width is imperative in a smart farm world. This characteristic is exhibited by wheeled-legged robots, which makes them a good candidate for UGVs in smart farms. | |
Energetic autonomy | ~10 h |
Comments: Robots based on combustion engines (e.g., tractors) can operate autonomously for approximately 10 hours, at minimum. The duration of autonomous operation for electrically driven systems should be similar. Some existing prototypes already meet this expectation [31]. In any case, the increasing improvement in battery technology will enlarge the energetic autonomy of future vehicles and robots. |
Prospective characteristics for UGVs in smart farms.
Small size: The idea that using small robots provides many advantages over the use of conventional large vehicles has been widely discussed over the past decade [22, 40]. It is broadly accepted that although several small robots can cost the same as a large machine and accomplish the same amount of work, using small robots allows a multi-robot system to continue a task even if a number of robots fail (re-planning the task). Moreover, the reduced weight of the small robots reduces terrain compaction and allows farmers to acquire robots incrementally.
Flexibility: Agricultural robots must be capable of adapting to many different scenarios (e.g., crops, row types, etc.) and tasks (e.g., plow, sow, fumigate, etc.). Thus, the robots must also be able to accommodate different agricultural implements, which should attach to or connect to (respectively, detach or disconnect from) the robots automatically.
Although conventional tractors are proven and highly reliable machines, they lack some adaptability features. Tractors have normally fixed distances between wheels, which makes them unsuitable for working on crops with different distances between rows. Using mobile platforms capable of controlling the distance between wheels could alleviate this problem, allowing the machines to adapt to different crops under different situations.
Maneuverability: Robots must be capable of performing small radius turns while adapting to different terrain. This last feature requires independent vertical control of wheels with respect to the robot’s body.
A steering system capable of zero-radius turns would be a proper solution, and this feature can be implemented by different structures as discussed in the previous section. Thus, minimization of headlands and wheel distance control can be achieved using either conventional or new articulated structures. Among the conventional structures, the skid steering scheme based on wheels or tracks is capable of zero-radius turns without additional steering mechanism, which helps in minimizing the headlands. However, separating and controlling the distance between contralateral wheels/tracks requires an active system (which already exists for some tracked vehicles used in the building industry).
Mobile platform structures based on coordinated or independent steering schemes can achieve zero-radius turns, but they still lack intrinsic track width control and require additional mechanisms. Another structure is the wheel-legged mechanism. Legged robots exhibit high terrain adaptability on irregular ground, but wheeled robots have speed advantages on smooth terrain; that is, they complement each other. Therefore, the most complete wheel-legged mechanism (Figure 6a) is a leg with three degrees of freedom [38] with an active wheel as a foot, where the wheel is steered and driven separately. This is a disruptive design not verified yet that will provide extraordinary characteristics to robots for smart farm applications. Thus, the wheels drive and steer, while the legs provide track-width control and terrain adaptation, i.e., they control the robot’s body leveling and ground clearance. This is the most capable system regarding ground clearance and body pose control, but it comes at the cost of higher mechanical complexity. Nevertheless, intermediate solutions can be developed to reduce the number of actuators while maintaining appropriate robot characteristics. Table 4 summarizes different wheel-legged theoretical solutions indicating advantages and shortcomings, and Figure 5 shows some sketches of practical solutions.
Resilience: Resilience is the ability to recover from malfunctions or errors. Initializing complex robots is a time-consuming procedure, especially when several robots are collaborating on the same task. Agricultural mobile robots must be resilient enough to ensure profitability. Thus, they must be easily shut down and started up (essential for error recovery); moreover, they must facilitate changing between manual operation mode and autonomous operation mode and vice versa.
Efficiency: UGV should be more efficient than conventional, manned solutions. This can be accomplished by systems that:
Minimize energy consumption by optimizing the robot trajectories during the mission
Drastically reduce the use of herbicides and fertilizers by using intelligent detection systems, tools, and decision-making algorithms
Eliminate the need for a driver and minimize operator risk
Minimize unnecessary crop damage and soil compaction
Friendly human-machine interfaces (HMI): A friendly interface is required to facilitate the introduction of robots into agriculture and to achieve profitability. Intuitive, reliable, comfortable, and safe HMIs are essential for farmers to accept robotic systems. The HMIs should be implementable on devices such as smartphones and tablets.
Communications: Communications in the smart farm must capitalize on CPS and IoT to collect sufficient data to take advantage of the big data techniques and enable communication with the cloud for use via different services (software as a service, platform as a service, and infrastructure as a service) offered by cloud providers [41].
Wireless communications with the operator and/or a central controller for control commands and data exchanges, including images and real-time video, will be required. Wireless communication among robots will also be required for coordination and collaboration.
Standardization of mechanical and electrical/electronic interfaces: Commercial equipment must comply with well-defined standards and homologous procedures before adoption by industry. Subsystems such as LIDAR units, computers, and wireless or Internet communication (4G/5G) devices and GNNS receivers and antennas are already off-the-shelf components, but mobile platforms must also cope with some standards related to agricultural machinery [25, 42].
Safety: Safety systems for agricultural robots must focus on three stages: (i) safety to humans, (ii) safety to crops, and (iii) safety to the robots themselves.
Safety for humans and robots can usually be accomplished through a combination of computer vision, LIDAR, and proximity sensors to infer dangerous situations and halt robot motion, whereas safety to crops is achieved through precise steering that guides the robot to follow the crop rows accurately using the crop position acquired at seeding time or real-time crop-detection systems. Following these three stages, a step forward in safety for agricultural robots would be the integration of a two-level safety system relying on the following:
A low-level safety system that detects short-range obstacles with the purpose of avoiding imminent collisions. This level should be implemented within the robot controller and based on commercial components.
A high-level safety system that detects and discriminates obstacles at an adequate distance to allow the robotic system to make decisions (i.e., re-planning a trajectory). This level should include vision, infrared, and hyperspectral cameras that provide information about the surroundings. Optical flow methods should be applied to detect obstacles in motion and compute their speed and direction to predict potential collisions [43]. Hence, optical sensors should track obstacles and their movements, dynamically compute safe zones, and adjust a robot’s speed and direction of movement according to the given situation.
Regardless of the exact approach, standards on safety machinery must be taken into consideration [42] to ensure that systems will meet regulations and will be able to achieve certification.
Environmentally friendly impact: Both intervention mechanisms (implements) and mobile robots must be environmentally friendly (e.g., use fewer chemicals and cause less soil compaction) while improving the efficiency of the agricultural processes (i.e., reduce chemical costs while equaling or improving production). In addition, current agricultural vehicles use fossil fuels that emit large amounts of pollutants into the air such as carbon dioxide (CO2), nitrogen oxide (NOX), carbon monoxide (CO), and hydrocarbon (HC) [44]. Furthermore, fuel can be spilled onto the ground, which is a long-term pollutant. These elements alter the environment and damage the ecosystem. One possible solution—envisaged as the likely future solution—is the use of electric vehicles.
Implements: The use of the conventional three-point hitch to attach implements to tractors should be changed as robots are introduced into agriculture. Instead, implements should be aligned with the robot’s center of gravity to optimize the payload distribution and minimize compaction. Mechanical attachment and electrical connection to the implement should be automated. The definition of these types of interfaces is a pending issue; nevertheless, an intermediate solution allowing the use of both new and conventional attachment devices (three-point hitch) will facilitate the gradual introduction of robotic systems into the agricultural sector. Obviously, developing new robots and adapting existing implements to a new attachment/connection system is the only way to introduce the robots to real applications.
HMI: An HMI for operators to communicate with robots should be implementable on portable equipment (smartphones, tablets, etc.). Operators will use such devices to send commands and receive responses and data. Moreover, an additional device—an emergency button that works using radio signals—must be provided to stop the robots from malfunctioning or unsafe situations. These interfaces must be true user-friendly devices to be operated by farmers rather than by engineers, which is a vital aspect for the introduction of robotics into agriculture, as it is for industry and services.
Autonomy: Two basic types of autonomies will be needed in smart farms: behavioral autonomy and operational autonomy. Behavioral autonomy is primarily associated with autonomous robots and relies on artificial intelligence techniques. It refers to the robot’s ability to deal with uncertainty in its environment to accomplish a mission. Operational autonomy is associated with the tasks the robot has to accomplish autonomously to become a UGV, i.e., the tasks required for the robot to work continuously without human intervention: refueling or recharging (energetic autonomy, see Table 5), herbicide/pesticide refilling, implement attaching, and crop offloading. These tasks, which can be solved using current automatic techniques, are currently being done with human intervention and should be fully automated in the smart farms.
Based on the existing agricultural vehicles and robot prototypes, robots to be deployed in smart farms should meet also the characteristics presented in Table 5.
The world population is increasing rapidly, causing a demand for more efficient production processes that must be both safe and respect the ecosystem. Industry has already planned to meet production challenges in the coming decades by defining the concept of the smart factory; the agriculture sector should follow a similar path to design the concept of the smart farm: a system capable of optimizing its performance across a wide network, learning from new conditions in real time and adapting the system to them and executing the complete production process in an autonomous manner. Smart factory and smart farm concepts have many commonalities and include some common solutions, but some specific aspects of smart farms should be studied separately. For example, the design of UGVs for outdoor tasks in agriculture (field robots) presents specific characteristics worthy of explicit efforts.
This chapter focused on reviewing the past and present developments of UGVs for agriculture and anticipated some characteristics that these robots should feature for fulfilling the requirements of smart farms. To this end, this chapter presented and criticized two trends in building UGVs for smart farms based on (i) commercial vehicles and (ii) mobile platforms designed on purpose. The former has been useful for evaluating the advantages of UGV in agriculture, but the latter offers additional benefits such as increased maneuverability, better adaptability to crops, and improved adaptability to the terrain. Clearly, independent-steering and skid-steering systems provide the best maneuverability, but depending on their complexity, wheel-legged structures can provide similar maneuverability and improved adaptability to crops and terrain as well as increased stability on sloped terrain. For example, the 4-DOF articulated wheeled leg (Figure 5a) and the 3-DOF SCARA leg (Figure 5b and 6a) exhibit the best features at the cost of being the most complex. Note that although both structures have the same maneuverability features and adaptability to crops and terrain (ground clearance, body leveling, etc.), the 3-DOF SCARA leg involves one fewer motor per leg, which decreases the price and weight and improves the reliability of the robot. However, the 2-DOF SCARA leg also exhibits useful features regarding maneuverability, adaptability to crops, and adaptability to terrain (ground clearance control and body leveling) while using fewer actuators (Figure 5c and 6b). For agricultural tasks carried out on flat terrain, the 1-DOF leg with a 2-DOF wheeled foot provides sufficient maneuverability and adaptability to crops with very few actuators (leg structure as in Figure 5d).
However, these robots also require some additional features to meet the needs of the smart farm concept, such as the following:
Flexibility to work on very dissimilar scenarios and tasks.
Maneuverability to perform zero-radius turns, crab motion, etc.
Resilience to recover itself from malfunctions.
Efficiency in the minimization of pesticide and energy usage.
Intuitive, reliable, comfortable, and safe HMIs attractive to nonrobotic experts to ease the introduction of robotic systems in agriculture.
Wireless communications to communicate commands and data among the robots, the operator, and external servers for enabling CPSs, IoT, and cloud computing techniques to support services through the Internet.
Safety systems to ensure safe operations to humans, crops, and robots.
Environmental impact by reducing chemicals in the ground and pollutants into the air.
Standards: operational robots have to meet the requirements and specifications of the standards in force for agricultural vehicles.
Implement usage: although specific onboard implements for UGV are appearing, the capability of also using conventional implements will help in the acceptation of new technologies by farmers and, hence, the introduction of new-generation robotic systems.
Autonomy: both behavioral autonomy and operation autonomy. Regarding power supplies, automobiles worldwide will likely be electric vehicles powered by batteries within the next few decades; thus, agricultural vehicles should embrace the same solution.
Regardless of these characteristics, UGVs for smart farms have to fulfill the requirements of multi-robot systems, which is a fast-growing trend [22, 40, 46]. Multi-robot systems based on small-/medium-sized robots can accomplish the same work as a large machine, but with better positioning accuracy, greater fault tolerance, and lighter weights, thus reducing soil compaction and improving safety. Moreover, they can support mission coordination and reconfiguration. These capabilities position small/medium multi-robot systems as prime future candidates for outdoor UGVs in agriculture. Additionally, UGVs for smart farms should exhibit some quantitative physical characteristics founded on past developments and current studies that are summarized in Table 5.
Finally, autonomous robots of any type, working in fleets or alone, are essential for the precision application of herbicides and fertilizers. These activities reduce the use of chemicals generating important benefits: (i) a decrease in the cost of chemical usage, which impacts in the system productivity; (ii) an improvement in safety for operators, who are moved far from the vehicles; (iii) better health for the people around the fields, who are not exposed to the effects of chemical; and (iii) improved quality of foods that will reduce the content of toxic products.
The research leading to these results has received funding from (i) RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; (ii) the Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) under the BMCrop project, Ref. 201750E089; and (iii) the Spanish Ministry of Economy, Industry and Competitiveness under Grant DPI2017-84253-C2-1-R.
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