\r\n\t
\r\n\tThis book is intended to provide the reader with a comprehensive overview of the current state-of-the-art about surface water waves, including forecasting and hindcasting of wind waves and storm surge, coastal risk analysis, and wave-structure-soil interaction.
Grapevine (Vitis vinifera L.) is the most economically important and widely cultivated fruit crop in the world and it is one of the oldest crops and the only Mediterranean representative of the Vitis genus (Clarke, 2001; Mullins, 1992; Galet, 2000). Its domestication produced cultivars suited to a wide diversity of climates and tastes (Levadoux, 1956; Royo, 1997). In effect this genus shows a wide morphological and genetic variability that is causing confusions and ambiguity for biotypes and clones identification, in particular considering varieties that are widely distributed and cultivated for centuries (Tessier, 1999). Ampelography, ampelometry, and biochemical traits analysis have been traditionally employed to identify the different biotypes in viticulture (Galet, 1979; Calò & Costacurta, 2004). However, these analyses are based on phenotypic characteristics which can be affected by environmental conditions (Meneghetti, 2011).
\nThe DNA molecular analyses are essential for internationally accepted grapevine identification and the investigation of genetic differences among the Vitis vinifera L. clones (Meneghetti, 2009). Methods based on DNA analysis have been used with varying degrees of success. This might be affected by the variability level of examined grape varieties and by the types of markers systems employed to investigate genetic relationships.
\nSimple sequence repeat (SSR) markers are universally used for the identification of the grape varieties (This, 2004). Di-nucleotide repeats pose some problems for stuttering, adjacent alleles, and binning and a possible SSR development was proposed by using microsatellites with a longer core repeat (Cipriani, 2010).
\nThe molecular approaches are also essential for internationally accepted grapevine identification and to investigate the genetic inter- and intra-varietal variability. Molecular markers have been used on Vitis vinifera in several studies to distinguish among clones of the same cultivars as RAPD random amplified polymorphic DNA, PCR specific analysis ISSR (inter-microsatellites), SNP (single nucleotide polymorphism), S-SAP (specific sequence amplified polymorphism), IRAP (inter-retrotransposon amplified polymorphism), REMAP (retrotransposon microsatellite amplified polymorphism), M-SAP (methylsensitive amplified length polymorphism), chloroplast DNA polymorphisms, SSCP (single-strand conformation polymorphism) (Moreno, 1995; Bavaresco, 2000; Imazio, 2002; Owens, 2003; Labra, 2004; D’onofrio, 2009).
\nA molecular strategy to obtain DNA polymorphisms of Vitis vinifera genotypes from the same cultivar to study the intra- and inter- varietal genetic variability, to discriminate accessions, clones, and biotypes of a same grape variety, and to analyze the relationships between molecular profiles and some environmental parameters (i.e., geographic site) or morphological traits was reported by Meneghetti et al. (2012a; 2012b; 2012c). This approach uses four different molecular marker systems (i.e., AFLP amplified fragment length polymorphism, SAMPL selective amplification of microsatellite polymorphic loci, M-AFLP microsatellites amplified fragment length polymorphism, and ISSR inter simple sequence repeat).
\nExample of Di-nucleotide SSR profile of Sagrantino cultivar generated by the 3130XL capillary sequencer at eight loci using different fluorochromes. The peaks indicate the alleles and its size on Vitis vinifera SSR BinSet (Meneghetti, 2012c).
Simple sequence repeat (SSR) markers are universally used for the identification of the grape varieties (Figure 1). Microsatellites consist of tandemly repeated simple sequence motifs with a high variation in repeat number among individuals. Applications of microsatellite markers include not only cultivar identification but also parentage testing, pedigree reconstruction and studies of population structure.
\nA strategy of grapevine cultivar identification is to analyze eleven di-nucleotide microsatellite loci as VVS2, VVMD5, VVMD7, VVMD27, VVMD28, VrZAG62, VrZAG79, VMC6E1, VMC6F1, VMC6G1 and VMCNG4b9 (Meneghetti, 2012c). PCR reaction mixture at 11 loci was performed by the workstations using SSR forward labeled primers with 6FAM, VIC, NED and PET dyes, and SSR reverse primers unlabeled each at 5 pmol/µl (Applied Biosystems). The PCR was performed in a GeneAmp PCR System 9700 (Applied Biosystems) and SSR polymorphisms were resolved on an ABI-3130XL capillary sequencer (Applied Biosystems) using GeneMapper version 4.1 (Applied Biosystems) with a Vitis vinifera microsatellite BinSet of 11 SSR standard loci. The important molecular polymorphisms were checked by the Sequi-Gen GT Sequencing Cell electrophoresis (Biorad) (Meneghetti, 2012a).
\nExamples of microsatellites with a longer core repeat in grapevine with the locus name and their position on Vitis vinifera genome (Meneghetti, 2012c).
The genus Vitis is characterized by great morphological and genetic variability. It is necessary to increase the number of SSR loci used for cultivars identification to analyze the genetic inter-varietal variability by SSR polymorphisms in Vitis. For example: analyzing other microsatellite markers as VVS1, VVS29, VVMD8, VVMD17, VVMD21, VVMD24, VVMD25, VVMD26, VVMD32, VVMD36, VrZAG47, VrZAG64, VrZAG83, VMC6E4, VMC6H6, VMC1E12, VMC4G6, VMC2H9, VMC2A5, VMC3D7, VMC2G2, VMC6E10 (Bowers, 1999). It is also possible to use the microsatellite primers with a longer core repeat (Cipriani, 2010) (Figure 2) or different molecular markers as M-AFLP (Figure 3).
\nExamples of molecular polymorphisms of Schiava grossa, Raboso veronese, Primitivo, Aleatico and Sanvicetro grape cultivars by M-AFLP technique.
Genetic dissimilarity of SSR (GD) estimates between grapevine cultivars (inter-varietal genetic variability) were calculated by using the following formula:
\n\n \n
PS is the percentage of common microsatellite alleles within the i and j genotypes, according to Dangl (2001).
\nDendrogram was produced by the Unweighted Pair-Group Arithmetic Average Method (UPGMA) clustering algorithm and the Numerical Taxonomy and Multivariate Analysis System (NTSYS-pc) Version 2.10 (Exeter Software Co., NY, USA).
\nIn particular for grapevine SSR variability, an additional study was performed by the BAND Genetic Similarity (GS) coefficient of Lynch (1990) used for SSR data in diploid genomes according to the following formula:
\n\n \n
Nij is the number of bands in common, Ni and Nj are the numbers of bands in the two individuals (i and j) being compared. Thus, GSij = 1 indicates the identity between i and j, whereas GSij = 0 indicates complete diversity. A pair of diploid individuals can have 0, 1, or 2 bands in common at each SSR locus. Dendrogram of the analyses were constructed from the symmetrical GS BAND matrix (NTSYS-pc).
\nAn example of these molecular analyses can be explained by the grape Malvasia family. The name Malvasia has ancient origins and refers to a numerous and heterogeneous group of varieties growing in many European countries. Malvasias is spread in Italy from north to south and seventeen Malvasia cultivars are registered in the Italian National Catalogue (Calò & Costacurta, 2004). There are few Malvasia varieties with black berries, mostly grown in the North-Western Italian region of Piedmont (i.e., Malvasia di Casorzo, Malvasia nera lunga and Malvasia di Schierano). Malvasia nera di Brindisi/Lecce contributes to the Salento oenological production in the southern Italian region of Apulia (Lacombe, 2007; Crespan, 2006).
\n\n Figure 4 reports a dendrogram of Malvasia cultivars by SSR molecular polymorphisms. The Malvasia cultivars were divided into three distinct groups: Istrian Malvasia was grouped with Riesling Renano and Chardonnay without the other Malvasias (cluster A). Sultanina was an out-group. The dendrogram showed clearly the genetic divergence of Malvasias family detected using only the SSR approach, in agreement with Calò and Costacurta 2005. The cluster analysis allowed to distinguish some variety groups cultivated in neighboring geographical regions: Cabernet Franc, Cabernet Sauvignon, Sauvignon Blanc, and Chardonnay from France; Malvasia bianca di Candia and M. del Lazio from Central Italy; Primitivo and Aglianicone from Apulia region (Southern Italy); Nero d’Avola and Malvasia delle Lipari from Sicily region (Southern Italy); Malvasia di Casorzo and M. di Schierano from Piedmont region (Northern Italy); Raboso Piave and R. Veronese from Veneto region (Northern Italy); Gellewsa, Gennarua, and Girgentina from Malta. The Istrian Malvasia was positioned in the A group, while Malvasia bianca di Candia, M. del Lazio, M. Bianca lunga (also known as M. del Chianti), M. nera di Brindisi/Lecce and M. delle Lipari accessions were clustered in the B group and M. di Casorzo, M. di Schierano, and M. nera di Bolzano in the C group.
\nDendrogram of ten Malvasia cultivars (i.e., Istrian Malvasia, M. delle Lipari, M. bianca di Candia, M. di Candia Aromatica, M. del Lazio, M. Bianca lunga / M. del Chianti, M. nera di Brindisi/Lecce, M. di Casorzo, M. di Schierano, M. nera di Bolzano) and 19 grapevine varieties (i.e., Cabernet Franc, Cabernet Sauvignon, Sauvignon Blanc, Chardonnay, Riesling Renano, Garganega, Primitivo, Plavac Mali, Aglianicone, Cannonau, Gellewsa, Gennarua, Girgentina, Calabrese / Nero d’Avola), Moscato Bianco, Moscato di Alessandria / Zibibbo, Raboso Piave, Raboso Veronese, Sultanina) based on Dangl’s Genetic Dissimilarity (Meneghetti, 2012b).
The ten Malvasias shown in Figure 5 were further analyzed by Genetic Similarity BAND coefficient using the microsatellite polymorphisms. The dendrogram of Malvasias in Fig. 5 showed the grouping of the Malvasia as in dendrogram in Fig. 4. In fact, Malvasia bianca di Candia, M. del Lazio, M. Bianca lunga, M. nera di Brindisi/Lecce, and M. delle Lipari were clustered into B group while M. di Casorzo, M. di Schierano, and M. nera di Bolzano were grouped into the C group, while the Istrian Malvasia is positioned between the two main groups (Meneghetti, 2012b).
\nDendrogram of ten Malvasias cultivars obtained using the Genetic Similarity BAND coefficient. The Genetic Dissimilarity analyses (Fig. 4) were confirmed by this approach and the three subgroups (A, B and C) were showed also in this dendrogram with Istrian Malvasia positioned between the two main groups (Meneghetti, 2012b).
The genetic variability of accessions from the same grape cultivar can be investigated by means of AFLP, SAMPL, M-AFLP and ISSR molecular markers according to Meneghetti et al. (2012c).
\nThe AFLP, SAMPL and M-AFLP analyses were performed using a Cy5-labeled EcoRI+3 (or PstI+2) primer and an unlabeled MseI+3 primer (three selective nucleotides). The amplification products were resolved on ReproGel High-Resolution pre-made acrylamide–bisacrylamide solutions (8% w/v) (GE Healthcare) in modified TBE buffer and detected on a semi-automated DNA sequencer, the ALFexpress-II DNA Analysis System (Amersham Pharmacia Biotech). Markers were visualized automatically by the ALF-win Fragment Analyses 1.09 software and checked by Quantity One 4.6.7 and PD Quest Basic 8.0.1 software (Biorad) (Meneghetti, 2011).
\nThe Inter-microsatellite analysis was performed using the PCR protocol reported by Meneghetti et al. 2012a, with minor changes. ISSR experiment were carried out using the same procedure of AFLP.
\nA binary presence or absence (1 vs. 0) matrix was created for AFLP, SAMPL, M-AFLP and ISSR markers and for each grapevine accessions. Molecular markers were defined by a standard ladder using the ALF-win Fragment Analyses 1.09 software (Amersham Pharmacia Biotech) and two reference DNA genotypes and visualized automatically by the ALF-win software. The scoring was checked by using Quantity One 4.6.7 and PD Quest Basic 8.0.1 software (Biorad) (Meneghetti, 2012c).
\nGenetic similarity (GS) estimates among individuals were calculated in all possible pair-wise comparisons using the Dice’s coefficient which was based on the probability that a marker from one accession will also be present in another and calculated using the following formula:
\n\n \n
X represents the number of shared amplification products scored between the pair of samples/fingerprints (i and j) considered, Y is the number of products present in i but absent in j, Z is the number of products present in j but absent in i (Dice, 1945).
\nThus, GSij = 1 indicates identity between i and j, whereas GSij = 0 indicates complete diversity.
\nGS was calculated within (GSW) and between (GSB) cultivars and marker systems (AFLP, SAMPL, M-AFLP).
\nThe cluster analysis of GS was performed according to the UPGMA algorithm using the NTSYS software.
\nCentroids of the grapevine accessions were plotted on a 2-dimensional graph according to the principal coordinates extracted from the GS matrices estimated by the three molecular marker systems. All calculations and analyses were conducted using the appropriate routines of the NTSYS Version 2.10 software.
\nThe information content of each marker system in discriminating the accessions of the same variety was calculated using the marker index (Powell, 1996). The efficiency of dendrograms was tested by cophenetic correlation. The reliability of clusters was evaluated by the bootstrapping procedure using 100 random samples of molecular markers. The software used was PHYLIP 6.6 (http://evolution.genetics.washington.edu/phylip.html).
\nHence it was reported and discussed using the molecular results of six grape cultivars (i.e., Garnacha tinta, Primitivo, Malvasia nera di Brindisi/Lecce, Negroamaro, Malvasia di Candia and Istrian Malvasia) on a few different aspects: genetic similarity, genotypes discrimination, biotypes discriminations and clones identification. There were correlations between geographic origins of materials and DNA fingerprinting plus relationships between morphological traits and molecular polymorphisms.
\nGarnacha is one of the most widely planted grape varieties in the world (240,000 ha). It is known by local names in Mediterranean regions: Garnacha tinta and Grenache noir are the Spanish and French name, while in Italy this variety is known as Cannonao, Alicante, and Tocai rosso (three Italian synonymous) but also as Cannonau (Sardinia) and Gamay perugino (Tuscany) (Galet, 2000; Calò, 1990).
\nFifty-three Garnacha accessions were investigated: 28 Italian accessions, 19 Spanish accessions, and 6 French accessions. The Italian accessions were 6 Tocai rosso from the Vicenza area, 8 Alicante from Sicily and Elba island, 4 Gamay perugino from Perugia province and 10 Cannonau from Sardinia. In order to verify the varietal identity, the analyses based on 11 SSR loci confirmed that only one SSR profile was obtained for the 53 accessions (Figure 6) (Meneghetti, 2011).
\nThe study of intra-varietal genetic variability was performed using AFLP, SAMPL and M-AFLP molecular markers. The bi-dimensional plotting of centroids reported in Figure 6 showed six different groups: 1) Italian Alicante accessions from Sicily; 2) Italian Tocai rosso accessions from Vicenza area (Colli Berici); 3) Italian Gamay perugino accessions from Tuscany and Umbria; 4) Spanish Garnacha accessions from Andalucia, Aragón, Cataluña, Castilla y León, Madrid; 5) French Grenache noir accessions; 6) Italian Cannonau accessions from Sardinia. The first coordinate allowed to distinguish clearly Spanish, French and Italian accessions while the second one separated the 4 Italian geographic origins (Figure 6).
\nGenetic similarity of 53 Garnacha samples was calculated within groups (GSW) and also between groups (GSB) (Meneghetti et al. 2012c). The PCA analysis confirmed the high genetic variability within Italian genotypes on the base of their provenance, on the contrary the 19 Spanish accessions were clustered in a more homogeneous group that showed a high genetic similarity (GSW= 0.9872).
\nCentroids of Garnacha tinta from Spain, Grenache noir from France, Alicante from Sicily (Italy), Tocai rosso from Vicenza area (Italy), Gamay perugino from Tuscany (Italy), Cannonao from Sardinia (Italy). The Genetic Similarity analyses were confirmed by this approach and the materials with same SSR profile were distinct according to the six geographic origins and the three Countries (Meneghetti, 2011).
The molecular approach discriminates all genotypes of this cultivar. Italian samples showed a high genetic variability within genotypes (GSW = 0.9481), while Spanish samples showed a high GS (GSW = 0.9872). GSW of Italian accessions (0.9481) was very similar to GSB (0.9480), but the four Italian origins are clearly separated by these molecular markers (Meneghetti, 2011).
\nAFLP, SAMPL, and M-AFLP were able to clearly distinguish the 53 Garnacha accessions from Italy, Spain, and France. The large number of molecular markers and their high degree of polymorphism make them important tools for many genetic studies.
\nProvenance-specific molecular polymorphisms were reported in Figure 7 and AFLP analyses shown in Figure 8.
\nProvenance-specific polymorphisms by SAMPL molecular markers obtained for the 10 Sardinian Cannonau (CAN-09/18) and six French Grenache (GRE-30/34) using Silver Staining technique. The first four line (09-12) corresponding to Cannonau from Jerzu (CAN-09/12) with a specific amplification product (Meneghetti, 2011).
An example of a digitalized electropherogram of the AFLP profiles obtained for the 19 Spain Garnacha accessions (GAR-35/53) using an ALFexpress-II DNA Automated Sequencer. The majority of AFLP markers were monomorphic but there were some clearly differences: the line 36 and 46 are very similar (two G. blanca genotypes) and line 37 and 49 showed only a different marker (two G. peluda genotypes). Genotypes from left to right: 35= Garnacha tinta, 36= G. blanca, 37= G. peluda, 38= G. roja; from 39 to 45= Garnacha tinta; 46= G. blanca, 47= G. erguida, 48= G. roya, 49= G. peluda; from 50 to 53= G. tinta (Meneghetti, 2011).
Primitivo is a grapevine variety very important for Apulian viticulture and according to tradition it was first planted by Benedictine monks in Gioia del Colle (Bari, Apulia, Italy). Primitivo di Gioia is the best known variety used in Gioia del Colle DOC wine and is genetically equivalent to the Croatian Crljenak Kaštelanski and the American Zinfandel (Calò & Costacurta, 2004; Calò, 2008).
\nFifty-nine different vines have been selected based on discriminating traits (i.e., shape, size, density, color of the skin of the bunch and of the berry). Five typologies called A, B, C, D, and E have been identified by means of ampelographic and phyllometric analyses. The morphological traits of the five biotypes (i.e., leaves, bunch, and berry) were maintained after repeated propagation of these biotypes in experimental vineyards. Thus, the morphological traits could have been fixed and stabilized during several centuries of cultivation at Gioia del Colle (Meneghetti, 2012a).
\nThe identical SSR profiles of Primitivo biotypes are shown by a Reference Primitivo clone from Taranto (Apulia, Italy) and two Zinfandel accessions from USA.
\nDice’s GS matrix was used to perform the Principal Coordinate Analysis of all Primitivo accessions.
\nMolecular markers discriminated the five biotypes from Gioia del Colle (Bari, Italy) to those From Pulsano (Taranto, Italy) and Zinfandel accessions from USA (Figure 9).
\nGeographic map of the five Primitivo biotypes from Gioia del Colle (Apulia, Italy) and the reference clone from Pulsano (TA). Centroids discriminated the genotypes according to the different geographic origins (i.e., Gioia, Pulsano, USA) (Meneghetti 2012a).
A total of 2,223 reproducible amplification products were obtained using four molecular marker systems (i.e., 837 AFLPs, 713 SAMPLs, 616 M-AFLPs and 57 ISSRs) and 1,156 products (52.0%) were polymorphics.
\nThe molecular analysis displays a high genetic variability within Primitivo genotypes which is in agreement with the non-homogenous geographical areas of cultivation. The GS was 0.8129 among Primitivo biotypes from Gioia del Colle; the GSw was 0.9477 within American accessions; the GSB was 0.7489 between Gioia del Colle biotypes and the Reference clone from Pulsano and the GSB was 0.7013 between the five Apulian biotypes and the two Zinfandel accessions from USA.
\nDice’s GS matrix was used to perform the Principal Coordinate Analysis using all Primitivo accessions (Figure 9).
\nThe molecular markers discriminated the Gioia del Colle biotypes from the Pulsano Reference biotype and to the two American Zinfandel accessions (Figure 9).
\nThe first coordinate of the centroids allowed to distinguish the five different biotypes of Gioia del Colle. The second coordinate allowed to separate the biotypes of Gioia del Colle from the two American Zinfandel accessions and the Primitivo reference clone of Pulsano (Figure 9).
\nThus, we could discriminate both the Primitivo accessions (i.e., the 5 biotypes from Gioia del Colle, the clone from Pulsano, the two American Zinfandel accessions) and the different geographical origins of the plants.
\nMalvasias belong to a numerous and heterogeneous population of varieties growing in many European countries and their history is an intriguing enigma. Several types of grape varieties have been traditionally considered under the generic term of Malvasia, often with a complementing name related to geographic origin (Crespan, 2008; Calò & Costacurta, 2004).
\nIn Italy, at the present time, Malvasias are spread from North to South and 17 Malvasia cultivars are registered in the Italian National Catalogue. Apulian Malvasia nera is a cultivar with black berries and belongs to the Apulian ampelographic assortment: this grape is very widespread in the Salento peninsula, from the Taranto area right across to the provinces of Brindisi and Lecce.
\nThe Malvasia nera of Lecce and Brindisi, originated from the cross between Malvasia bianca lunga or Malvasia del Chianti and Negroamaro. It represents an important variety in the Apulia region. In the past, Malvasia nera of Brindisi and Malvasia nera of Lecce were considered two different cultivars, but this presumed synonymy has been ascertained with SSR markers and therefore these two Malvasia nera would be considered to be the same variety (Meneghetti, 2012a). Morphological analysis allows to differentiate accessions of this cultivar when we compare biotypes cultivated from the Lecce region with others from the Brindisi region. For this reason deeper molecular analyses have been conducted to investigate differential molecular traits between these two Malvasia cultivars with different geographical origin.
\nThirteen accessions of Italian Malvasia nera from Brindisi (Salento, Apulia) and thirteen accessions of Italian Malvasia nera from Lecce (Salento, Apulia) were analyzed. All the accessions show the same SSR profile and were identified as Malvasia nera of Lecce and Brindisi. AFLP, SAMPL, M-AFLP and ISSR analyses were performed in order to study the intra-varietal variability.
\nA total of 2,049 reproducible amplification products were obtained with the four molecular marker systems, 756 AFLPs, 615 SAMPLs, 626 M-AFLPs and 52 ISSRs.
\nThe discrimination among the 26 genotypes of Malvasia nera of Lecce and Brindisi from the two different geographic origins of Salento (Lecce and Brindisi) was possible using the four marker types as reported in Figure 10 where as MLB is Malvasia nera of Lecce and Brindisi.
\nThe MLB genotypes with the numbers 1 to 13 were from Brindisi while samples with the numbers 14 to 26 were from Lecce.
\nGeographic map of Lecce and Brindisi (Apulia, Italy) and dendrogram of genotypes of Malvasia nera di Brindisi/Lecce from Lecce and from Brindisi regions (Meneghetti, 2012a).
The cluster analysis clearly grouped the 26 accessions according to the two geographical origins, Lecce and Brindisi. Two accessions from Brindisi (number 2 and 7) showed the same molecular profile (i.e., identical genotype).
\nGenetic similarity (Dice, 1945) estimated within and between the two origins, Brindisi and Lecce, was confirmed that these two groups were not genetically identical. The GSTOT was 0.8269, the GSW within the 13 accessions from Brindisi was 0.9544 and the GSW relating of the 13 accessions from Lecce was 0.9589; GSB between the two origins was 0.7572.
\nThe molecular approach was efficient to discriminate the Apulian Malvasia nera accessions from these two different provinces of the Salento area.
\nNegroamaro is a grape variety native to Southern Italy and is grown almost exclusively in Apulia (Calò & Costacurta, 2004).
\nThis grapevine cultivar is considered to have an even older origins in Apulia (i.e., possibly brought by ancient Greek settlers that colonized Southern Italy) and it is one of the most important popular wine varieties of the Salento area.
\nThis variety produces the famous regional red and rosé wines ‘Negroamaro Cannellino’ that comes from a distinct biotype which is listed separately in the Italian Register of Grapevine Cultivar (Calò, 2000). Although the SSR markers don\'t distinguish it from the Negroamaro variety, the somatic mutation that allows a characteristic precocity of maturation (15 days) of ‘Negroamaro Cannellino’ affects a fundamental physiological distinctive trait. Therefore, it is not possible to consider these two Negroamaro biotypes from the same cultivar (Meneghetti, 2012a).
\nForty-four accessions of Negroamaro from Apulia (Italy) analyzed at 11 microsatellite loci showed a microsatellite profile in agreement with the Negroamaro grapevine variety.
\nIn order to define the intravarietal variability AFLP-based molecular markers and inter-microsatellites were used.
\nThe Negroamaro accessions were from eight different geographic origins of Salento (Apulia, Italy): Alezio, Tuglie, Copertino, Veglie, Leverano, San Pancrazio, Cellino San Marco and Ceglie Messapica.
\nA total of 2,282 reproducible amplification products were obtained with the four molecular marker systems, 856 AFLPs, 756 SAMPLs, 620 M-AFLPs and 50 ISSR and 1,022 (44.8%) of these were polymorphics.
\nThe Negroamaro accessions were separated according to their specific origins and according to a gradient “lowland-hill” or “North-South Apulia” as shown in Figure 11. The Negramaro accession from the Northern hilly origin, Ceglie Messapica, is shown as an outgroup.
\nGeographic map of the 8 Negroamaro origins (Apulia, Italy) and Dendrogram of genotypes of Negroamaro from the different Salento areas (Meneghetti, 2012a).
The genetic variability among the Negroamaro materials showed an high correlation between the geographic origins (environmental variability) and the molecular profiles; this is important for the choice of the Negroamaro clones to be propagated in the Salento area.
\nThe white Malvasia di Candia SS (i.e., Simple Savor, not aromatic) is a cultivar of the great and heterogeneous Malvasia family and represents one of the principal varieties of the Frascati DOC area. It is also known as ‘Red Malvasia’ due to the red shoots color (Calò & Costacurta, 2004).
\nMany biotypes of Malvasia di Candia with large sized berry bunches are present in the Frascati area after 1950. Thirty accessions of this cultivar were selected from 150 old vineyards from this area in an earlier study. Morphological and molecular analyses were performed to indentify the most interesting biotypes which revealed a large variability at morphological and molecular levels. The 30 accessions were identified as white Malvasia of Candia (SS) by SSR markers.
\nAmpelography and ampelometry analyses clustered four biotypes called AA, A, B, and AB (Figure 12).
\nBunches of the four biotypes of Malvasia di Candia (Meneghetti, 2012c).
Biotype AA shows medium sized, long bunches with evident wings; medium irregular berry size (Figure 12). Biotype A was similar to biotype AA with smaller sized bunches and wings. Biotypes B has smaller, shorter, less compact bunches than biotypes AA and A. Biotype AB showed bunch with intermediary characteristics between biotypes A and B. AFLP, M-AFLP, and SAMPL molecular markers were used to analyze the intra-varietal genetic variability (Meneghetti, 2012a).
\nCentroids of Malvasia di Candia accessions that discriminated the 30 genotypes according to the different morphological traits of bunches (i.e., A, AA, B, AB).
Cluster analyses showed a correlation between molecular profile and morphological traits of bunches relating to Malvasia di Candia biotypes (Figure 13).
\nBiotypes B (smaller fruit size) were clearly discriminated from the remaining typologies (larger fruit size) even if the accessions with A and AA bunch types were grouped in the same cluster (A/AA) (Figure 13).
\nIstrian Malvasia is a cultivar from Northern Italy and the Istrian Peninsula (Calò & Costacurta, 2004; Crespan, 2006). It is known in Croatia as Malvazija istarska (Crespan, 2008). This cultivar is the most commercially important and widely cultivated grapevine variety in Istria (Croatia).
\nSeveral biotypes of this grapevine cultivar were selected in Italy during clonal selections by research institutes.
\nA study was carried out on 30 Istrian Malvasia genotypes consisting of eight Italian clones (i.e., ISV 1, ISV F6, VCR 4, VCR 113, VCR 114, VCR 115, ERSA 120, ERSA 121) and 22 autochthonous grapevine accessions grown in Istrian Peninsula (Croatia); the morphological and genetic intra-varietal variability of this cultivar was evaluated.
\nAmpelographic characterizations of accessions were performed using 20 OIV descriptors relative to young shoot, shoot, young leaf, mature leaf, inflorescence, bunch and berry (2nd edition of the OIV descriptor list for grape varieties and Vitis species). Dendrogram based on morphological data was performed using the absolute mean distances (Manhattan - City Block) and the Complete Linkage (Fabbris, 1997).
\nThe microsatellite analyses confirmed the varietal identity of the 30 genotypes analyzed. SSR profile of Istrian Malvasia was reported in Figure 14.
\nSSR profile at 11 loci of Istrian Malvasia cultivar by 3130XL Genetic Analyzer.
Malvasia dendrogram of morphological data in Figure 15 showed two distinct main groups: first consisted of the 22 autochthonous accessions from Croatia and second comprised the eight Italian clones.
\n\n Figure 16 reports the 16 geographic origins of the analyzed Istrian Malvasia accessions or clones.
\nDendrogram of morphological data of the 30 Istrian Malvasia accessions, of which 8 Italian clones of Istrian Malvasia (i.e., ISV 1 and ISV F6 clones, ERSA 120 and ERSA 121 clones, VCR113, VCR114, VCR115 and VCR 4 clones) and the 22 autochthonous accessions from Croatia.
Geographic map of the geographic origins of the 30 Istrian Malvasia accessions from Italy (1-6) and Croatia (7-16) (Meneghetti, 2012b).
The morphological analyses performed using the OIV ampelographic descriptors (Figure 15) discriminated the Italian clones in accordance with the three different selectors: the two clones of the ISV (i.e., ISV 1 and ISV F6), the two clones of ERSA (i.e., ERSA 120 and ERSA 121) and the four clones of VCR (i.e., VCR 113, VCR 114, VCR 115 and VCR 4). Italian clones and Croatian accessions were separated by morphological traits.
\nIn order to study the intra-varietal genetic variability of 30 mentioned accessions AFLP, SAMPL and M-AFLP molecular analyses were performed.
\nA total of 1,754 reproducible amplification products were obtained (i.e., 682 DNA fragments from AFLPs, 597 DNA fragments from SAMPLs and 475 DNA fragments from M-AFLPs). Results revealed 931 (70.1%) polymorphic molecular markers: 308 AFLPs, 302 SAMPLs and 321 M-AFLPs.
\nThe GSTOT values of the three marker types showed that all molecular systems applied were efficient to show molecular polymorphisms between the Istrian Malvasia genotypes.
\nThe observed GSTOT was 0.8974, the GSW within the eight Italian clones was 0.8376 and the GSW within the 22 Istrian samples was 0.9552. This result showed that the Istrian accessions were genetically more similar to each other than the Italian clones. GSB was 0.8667 between Italian and Croatian accessions.
\nThe GSW values were 0.9302, 0.9478 and 0.9278 within ERSA, ISV and VCR clones respectively. The GSB values were 0.8066, 0.8162 and 0.7806 between ERSA and ISV, ERSA and VCR, and ISV and VCR clones respectively.
\nDice’s GS matrix was used to perform the cluster analysis (Figure 17).
\nDendrogram of the 30 Istrian Malvasia genotypes reveals a different molecular profile between Italian and Croatian samples. The number reported identify the different geographic origins (map of Figure 16) (Meneghetti, 2012b).
\n Figure 17 reports two distinctive groups: Croatian accessions and Italian clones. Results of the AFLP, SAMPL and M-AFLP analysis did not show a complete correlation with morphological observations. In fact the dendrogram obtained by molecular data (Figure 17) was not exactly equivalent with that of morphological observations (Figure 15). However, both cluster analyses showed a clear correlation between accessions and their selectors or country.
\nFurthermore, the Croatian accessions were distinct in ten sub-groups in agreement with their geographic origins (i.e., 7= Umag, 8= Brtonigla, 9= Tar-Vabriga, 10= Kaštelir-Labinci, 11= Višnjan; 12= Poreč; 13= Sveti-Lovreč, 14= Kanfanar; 15= Bale, 16= Vodjan).
\nA similar level of distinction could be observed for the three Italian sub-groups.
\nWe could argue that the genetic similarities are in agreement with the distance of the geographic origins.
\nThese results suggest the need to emphasize the environmental role on the selection of genotypes during the centuries. The emphasis on preserving the autochthonous grapevine biotypes is crucial to preserve the richness of the Istrian Malvasia germplasm.
\nThe study confirmed the importance of choosing appropriate propagation material for future cultivation in order to save the genetic variability of local biotypes. The propagation of the same clone in different territories should be also avoided in order to preserve the good interaction among genotypes and their specific environments.
\nFurther intra-varietal studies (i.e., DNA analysis, together with ampelographic investigations), allowed the identification of Italian clones and Croatian autochthonous accessions of Istrian Malvasia.
\nThe results have highlighted the existence of genetic variability among the Istrian Malvasia accessions from different geographical cultivation areas. These molecular approaches allowed the identification of different clones within the Istrian Malvasia cultivar and the characterization of accessions according to their geographic origins.
\nIn summary, the molecular and morphological analyses showed that Vitis vinifera is a species characterized by vast genetic variability.
\nMolecular analyses of DNA are essential for the grapevine identification using SSR markers.
\nThese results showed also the wide genetic variability for the grape cultivars (intra-varietal level) suggesting the need for the preservation of autochthonous grapevine biotypes found in different areas by a proper selection of the grape multiplication materials.
\nIn fact, this genetic variability accumulated during centuries of cultivations and selections, should be both recognized and preserved, being corroborated by scientific experimental results.
\nThe importance of saving the genetic variability of the varieties is crucial in order to avoid to propagate the same clone in different cultivation areas.
\nIt is highly recommended to promote the propagation of the typical autochthon biotypes, which are already wisely selected by grape vine growers.
\nThis study is supported by both ASER and IDENTIVIT research grant from Ministero delle Politiche Agricole, Alimentari e Forestali MiPAAF, Rome, Italy.
\nThe introduction of drones has revolutionized many sectors, including but not limited to cinematography [1], search and rescue [2, 3], maintenance [4], surveillance [5, 6], delivery of goods and transportation [7, 8].
\nThe main components of a drone are its Propelling System and its Flight Control Unit (FCU). The propelling system provides the necessary thrust to change the attitude of the drone, described by its pitch, roll and yaw angles, and thus its three dimensional motion. The dominant propelling system currently is composed by propellers driven by a brushless motor and an Electronic Speed Controller (ESC) combination. The FCU is the “brain” of the drone, since it issues the control commands to the ESCs for changing the attitude and the pose of a drone. It usually contains GPS receiver(s), accelerometer(s), gyroscope(s), magnetometer(s) and barometer(s) coupled to environment sensing devices like laser scanners to extract the current pose of the drone. The output of a FCU is computed by taking into account the current pose and the desired reference.
\nMulti-rotor drones have been very popular among researchers with their naming typically by the rotor count (tricopters, quadcopters, hexacopters, and octacopters). The drone’s thrust increases with the number of rotors allowing the lift of higher payloads at the expense of a reduced flight time, and power tethering systems are usually sought [9].
\nThe majority of the off-the-self drones have a 1-2 kg payload capability with very few drones being capable of lifting an order of higher magnitude [10]. This is primarily due to the FCU’s necessary tuning, the advanced ESCs and the need to abide to the laws imposed by each country’s regulatory authority.
\nPertaining to the described challenges, this chapter presents a drone that based on its mission can be modular in terms of software and hardware while lifting a high payload. The drone can operate either indoors or outdoors and has navigation and mapping capabilities as well as can interact with the environment through an attached robot manipulator.
\nIn Section 2 the mechatronic design of the drone is presented, while in Section 3 the drone’s software for localization is explained and evaluated. The drone’s ability to perform either in a collaborating or an adversarial environment using computer vision is discussed in Section 4. The aerial manipulation concept is addressed in Section 5, followed by Concluding remarks.
\nThe developed octarotor drone has a take-off weight of 40 kg and a 30 min flight time. The drone’s frame was designed and fabricated in collaboration with Vulcan UAV©. The authors’ input on this aspect is related with both extending the bare-bone design of Vulcan to accommodate for payload carriage, as well as fabricating the final prototype and mounting all the additional modalities mentioned in the sequel. The backbone structure consists of three ø 25 mm, 1200 mm length aluminum tubes in a triangular cross sectional configuration. Four 575 mm length aluminum rectangular arms attached at each end of this structure and carry two motors in a coaxial configuration. The arms are fixed to the main frame using a 5 mm thick carbon plate. The resulting “H-frame” configuration can be visualized in Figure 1.
\nDrone’s backbone structure.
Although the lower motor provides 25% less thrust [11] it offers some redundancy against single motor failure. The selected 135 KV KDE© brushless motors coupled with ø71.12 cm custom designed carbon propellers, collectively provide 37.2 kg of thrust at 50% throttle input. The extra thrust can be used for rapid maneuvering of the drone and for exerting forces by the aerial manipulator shown in Section 5.
\nPower is provided by a 12S 22 Ah LiPo battery pair connected in parallel to the Power Distribution Board (PDB). At 50% thrust with full payload while hovering, the octarotor’s motors sink 11.7 A each, resulting in a flight time of \n
Two carbon rods of ø12 mm are fixed at the underside of the mainframe tubes for payload carriage. The maximum payload weight is 30 kg and can be easily dismantled from the main frame using quick release clamps. Similarly, the retractable landing gear assembly is attached with these clamps to the main frame tubes for enhanced modularity, as shown in Figure 2. The gear can retract within a \n
Landing gear detail (left) and payload assembly with battery holder (right).
Additional power for peripherals and sensing modalities can be supplied through a dedicated 750 W buck converter, mounted on the payload carrier assembly, as shown in Figure 3. The converter is contained within a custom 3D printed case and standard Unmanned Aerial Vehicle (UAV) \n
Enhanced power distribution board (left) and i7-minicomputer (right).
The PixHawk Cube FCU was selected [13] featuring triple redundant dampened Inertial Measurement Units (IMUs), with a modular design and industrial standard I/O connectors. Additional telemetry and R/C circuits are deployed to enable monitoring and intervention and comply with flying regulations.
\nThe \n
A high processing power 8th generation Intel NUC i7-computing unit with 32 GB RAM and 1 TB SSD, shown in Figure 3, was mounted symmetrically to the buck converter on the underside of the main frame. This 90 W computing unit allows for online computations on demanding tasks such as the visual object tracking methods of Section 4, as well as the easy development of autonomous flying applications.
\nOn the software side, the ArduCopter flight stack [15] was selected to run on the FCU. The pose estimation is carried through a sophisticated Extended Kalman Filter (EKF) at 400 Hz. The Intel NUC companion computer is serially connected to the FCU at a baud rate of 1 Mbps and the communication packages are following the MAVlink protocol. The NUC’s operating system was Ubuntu Linux 16.04 and all applications are developed through the Robot Operating System (ROS) and MAVROS [16] middleware with a 50 Hz refresh rate.
\nThe developed drone without any payload can be visualized in Figure 4.
\nDrone prototype.
The RTK enhancement feature of GPS is used for outdoor localization purposes. This is due to the more precise positioning [17] because the of the GPS satellite measurements’ correction using feedback from an additional stationary GPS module. The disadvantage of such systems is that their use is bounded to a significant pre-flight setup time which is inversely proportional to the achieved accuracy (cm range).
\nAlthough the internal loop of the flight controller operates at 400 Hz, the GPS receiver streams data at a lower rate of 5 Hz. In popular flight software such as ArduPilot, the aforementioned rate needs to be taken into consideration by the underlying EKFs running by the FCU. A typical comparison of the achieved accuracy using a drone in a hovering state can be seen in Figure 5.
\nDrone’s EKF 3D-position output with (red) and without (blue) RTK correction.
The drone was flown in a hovering position with the RTK GPS module injecting measurements to the flight controller and the output of the FCU’s EKF was compared with and without the presence of the injected RTK measurements. The red line represents the EKF’s output based solely on the GPS signal, whilst the blue line indicates the same output when RTK correction (using a 30 min warmup period) is injected on the FCU.
\nThe standard deviation was computed equal to 0.74 m, 0.47 m and 0.27 m for \n
During indoor navigation: a) the lack of GPS guidance, b) pressure changes affecting the barometric sensor, and c) power lines affecting compass accuracy can severely affect the output of a FCU. With only the accelerometers and gyroscopes being unaffected, the injection of an external feedback source to the FCU is considered essential. Such feedback is usually based on visual techniques, such as those presented in [18, 19].
\nFor experimentation purposes, the used Motion Capture System (MoCaS) [20] injects measurements in the ArduCopter flight stack. The system comprises of 24 Vicon cameras uniformly scattered within an orthogonal space of \n
The utilized ROS software at the MoCaS operates at 25 Hz and can efficiently wirelessly stream the measurements to the drone’s FCU. The latency time \n
Because of the MoCaS’s efficiency, its weighing to the EKF is ten times larger compared to the GPS’s weight when flying outdoors. Subsequently, the efficiency of the implementation is assessed by comparing EKF’s position output with and without MoCaS’s feedback injection. In Figure 6 the drone’s position error (in each axis) between the aforementioned two quantities is visualized, where the red, green and blue lines represent the error along the \n
Drone’s EKF position error when MoCaS’ measurements are not injected.
Real time pose tracking is satisfactorily achieved and minor differences are attributed to the EKF’s weighting of the accelerometer and gyroscope measurements during calculations.
\nAn important parameter on aerial navigation is awareness of the surrounding environment including being in close proximity between cooperating or evasive drones [21, 22] to avoid potential contacts.
\nHigh accuracy awareness may not be feasible [23] and can become prohibitive in indoor environments; visual sensors along with Lidars can assist in this aspect. A spherical camera provides an all-around visualization of the surroundings and can detect neighboring targets. A Pan-Tilt-Zoom (PTZ) camera with a limited Field of View (FoV) can then provide a more accurate description of this target. The suggested target relies on the detection of moving objects. Correlation techniques and/or deep learning Visual Object Tracking (VOT) methods [24] are employed for this purpose.
\nRather than using several cameras with a limited Field of View (FoV) to observe the surrounding space, a 360° FoV camera [25] is used. The spherical camera records images in a “spherical format” which is comprised of two wide-angle frames stitched together to form a virtual sphere [25]. The image can be rectified to the classic distortionless rectilinear format of a pinhole camera [26]. However, due to the nature of the “spherical format,” it is preferable to split the image into smaller segments and rectify each one to achieve results closer to the pinhole camera model. Instead of splitting into equal sized square segments [27], each image is split into tiles based on orientation-independent circles. With every tile having a different a-priori known calibration, the rectification can be carried out for each one independently, without high computational cost. By applying the solution and rectifying the image in Figure 7, for a selection of \n
Spherical flat image.
Rectified \n\nN\n=\n12\n\n partitions for a single “spherical” frame.
For the case of collaborating drones, it is assumed that each one carries passive markers for visual recognition. Subsequently, the rectified images are processed for identification of these markers [28, 29, 30, 31] thus estimating the neighboring drone’s pose. For improved pose extraction, the solution of a multi-marker rhombicuboctahedron formation arrangement [32] is assumed to be present in each target.
\nThe experimental setup for evaluation consists of the spherical camera mounted in a 2.7 m protruding stick, which subsequently is mounted to the underside of the octarotor using the generic mount base discussed in Section 5. A rhombicuboctahedron arrangement with markers at its faces is attached to a DJI-Mavic drone. Both UAVs were located within the MoCaS test volume, as shown in Figure 9. The quadrotor drone was flown in a randomized trajectory near the vicinity of the octarotor.
\nExperimental setup for 360° camera relative localization.
In Figure 10 the relative 3D-flight path between the drones is presented. The results recorded from the MoCaS and the visual ones are shown, where for the cases of detecting the marker the relative accuracy these measurements was 2.2 cm respectively.
\n3D-relative path inferred through the MoCaS and the visual method between two collaborating drones.
Having identified the adversary or collaborating drone, a PTZ-camera is utilized to track its motion. This Visual Object Tracking (VOT) problem is challenging when the drone is occluded, thus Long Term Efficient (LTE) algorithms are sought for moving objects. Despite the development of Short Term Efficient (STE) algorithms [33] using either correlation methods or deep learning ones, an initial bounding box containing the target is required. In the authors’ case, the developed VOT algorithm employs two methods relying on a comparison: (a) between the tracking of the points transformed based on the PTZ-parameters and those using an optical flow, and (b) between the homography matrix transformed points and the optical flow.
\nThe first method is based on the PTZ known motion and IMU’s acceleration and gyroscope measurements (Figure 11), in order to estimate the motion of the pixels due to the motion of the camera in relation to the surroundings [34]. An IMU with triple accelerometers, gyroscopes, and magnetometers is attached to the PTZ-camera, as shown in Figure 11. While the enhancement provided by the PTZ camera allows for efficient VOT, the need to control its parameters (pan, tilt, and zoom) while placed on a floating base and at the presence of several occlusions needs to be addressed.
\nPTZ-camera for visual object tracking.
The objective is to provide the bounding box \n
Sample drone tracking setup.
A GPU-based background subtraction technique eliminates the background pixels leaving only the moving object pixels. The bounding box encapsulates all pixels of the moving drone and the pan and tilt angles are adjusted to position the centroid of the moving bounding box at the image’s center while the zoom is adjusted to enlarge this box. The communication between the i7-minicomputer and the PTZ-camera is shown in Figure 13, while the VOT algorithm is shown in Figure 14.
\nPTZ-camera hardware tracking and control schematic.
Pan-tilt-zoom/IMU and optical flow VOT algorithm.
The feature points are recognized in each frame and the transformation matrix between successive frames follows along [35]; the formulas provide the transformation based on the PTZ-parameters and an augmentation is needed to account for the camera’s translation, as provided by the on-board accelerometers. The pixels that correspond to static background objects will follow the predicted motion by the camera motion and coincide to the positions predicted by an optical flow based estimation, while the rest will be classified as belonging to moving objects of interest (Figure 15). The computations for the optical flow parallels that of the Lucas-Kanade method using a pyramidal scheme with variable image resolutions [36]. The basic optical flow premise is to discover the positioning of an image feature in the previous frame, in the current frame captured by the camera.
\nBackground/foreground estimation using Homography-based VOT.
The second method is relying only on visual feedback and homography calculations [37] between two successive frames and does not require either the PTZ or the IMU-measurements, as shown in Figure 16. Initially a set using “strong image features” is identified on the previous camera frame and an optical flow technique is used to estimate the position of the features in the current frame. The method involves the discovery of special image areas with specific characteristics.
\nHomography-based VOT.
The algorithm used for finding the strong corners image features relies on the GPU-enhanced “goodFeaturestoTrack” [38]. Under the assumption that the background is formed by the majority of the pixels, a homography is calculated that transforms the features positions from the previous to the current frame; these correspond to the background pixels. The previous frame features are then transformed using the homography to get their position in the current frame. Herein, it is assumed that the background points transformed with the homography will coincide with the estimated ones by the optical flow, while the moving objects’ features estimated by the optical flow will diverge from the homography transformed pixels.
\nOne downside of the technique is that when the tracked object remains static and blends with the background it is unable to identify it. In this case, a fast correlation-based STE-tracker relying on the MOSSE algorithm [39], is also used in order to estimate the drone’s position until new measurements of a moving drone are available. Several more robust but slower tracking algorithms were evaluated, including the KCF [40], CSRT [41], MIL [42], MedianFlow [43], TLD [44], and the MOSSE-algorithm was selected because of its fast implementation (600 Frames-per-Second (FpS)). A Kalman prediction scheme [45] was used to predict the bounding box and the one obtained from the MOSSE in the presence of noisy measurements of the moving object center, using a 2D-constant acceleration model for the estimated tracking window.
\nA seven Degree-of -Freedom (DoF) robotic arm has been attached for exerting forces on surfaces in aerial manipulation tasks, such as grinding, cleaning or physical contact based inspection [46]. The Kinova Gen 2 Assistive 7DoF robot [47] was attached through a custom mount. This manipulator is characterized by a 2:1 weight to payload ratio, with the available payload at the end-effector being 1.2 kg grasped by the 3-finger gripper. Torque sensing is provided at each joint and these measurements along with the joint angles are communicated to the main computer at 100 Hz under ROS middleware.
\nFor mounting the robot to the drone’s payload attachment rods, a generic payload mount base was designed and manufactured. The base is firmly mounted to the drone’s payload carrying rods utilizing quick attachment clamps. The construction material was selected to be T-6065 aluminum and features four 10 mm openings for attaching the payload. A second rigid base was similarly designed for attaching the robot’s base to the generic payload mount base using 10 mm hex bolts. An exploded view of the entire mounting configuration can be visualized in Figure 17. The aerial manipulator is shown in Figure 18.
\nUniversal mount of robotic manipulators on aerial platform.
Aerial manipulation system with PTZ-camera.
The indoor position hold scheme of Section 3.2 was expanded [48, 49] so as to utilize the manipulator in a surface ultra-sound scanning scenario. The surface is placed at \n
The described scheme is aimed for future use in the Abu Dhabi airport’s Miedfiled Terminal [50], for scanning the integrity of critical structures such as facades and rooftop. Figure 19 presents the hovering pose of the physical prototype while scanning the surface, whilst the full video concept including moments of the experiment is available through the link given in [51].
\nSurface ultra-sound scanning utilizing aerial manipulation.
In this chapter the mechatronic aspects (hardware and software) of a heavy lift drone are presented. This drone can operate either indoors or outdoors in an autonomous manner. Equipped with spherical and PTZ cameras, the drone has environment perception capabilities and can collaborate with other drones. A robot manipulator is attached at the drone for physical interaction purposes. The ability to carry out the aforementioned tasks in an accurate and modular manner depicts the efficiency of the system for future robotic aerial applications of increased complexity. However, many challenges are yet to be examined. The authors’ aim is to focus future research on autonomous navigation in confined environments as well as high interaction forces aerial manipulation [52].
\nIn aerial manipulation, the challenge lies with the forces at the tip of a stiff 7-DoF manipulator being directly transferred to the main UAS frame. Additionally, their orientation can be varying, depending on the pose of the manipulator. Thus, the ability of the aerial manipulator to robustly maintain its position and attitude while performing the task is mandatory. Compared to the depicted experimentation of this book chapter the induced forces from such operation are calculated to be in the area of 10 to 100 N. Subsequently, although the existing position controller of the ArduCopter flight stack is able to withhold a proper pose while ultrasound scanning of inclined areas, advanced control techniques [49] will be utilized in the sequel. The authors intend to test the efficiency of the built-in attitude controller of the ArduCopter flight stack, as well as exploit the adaptive backstepping control strategies in [48, 49] and other (model predictive) control techniques. The implementation of such controllers relies on the ability to directly control the angular velocity of the drone’s motors independently, at rates greater or equal to 1 kHz.
\nThe authors declare no conflict of interest.
\n unmanned aerial vehicle remote control power distribution board inertial measurement unit flight control unit global navigation satellite system global positioning system real-time kinematic extended Kalman filter robot operating system degree-of-freedom field-of-view pan-tilt-zoom visual object tracking
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\nIn the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
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\\n\\nWe started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\nIn the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n2004
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