Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"661",leadTitle:null,fullTitle:"Coronary Interventions",title:"Coronary Interventions",subtitle:null,reviewType:"peer-reviewed",abstract:"Given the prevalence of heart conditions in modern populations, new techniques for treating patients are highly warranted. 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\r\n\tMinerals are one of the four groups of essential nutrients, beside vitamins, essential fatty acids, and essential amino acids. \r\n\tThis book intends to cover major mineral deficiency problems such as calcium, iron, magnesium, sodium, potassium and zinc. These minerals have very important task either on intracellular or extracellular level as well as regulatory functions in maintaining body homeostasis.
\r\n
\r\n\t \r\n\tBoth macrominerals and trace minerals (microminerals) are equally important, but trace minerals are needed in smaller amounts than major minerals. The measurements of these minerals quite differ. Mineral levels depend on their uptake, metabolism, consumption, absorption, lifestyle, medical drug therapies, physical activities etc.
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He published 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, and edited 32 books.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.jpg",biography:"Gyula Mózsik, MD,PhD, ScD(med) is a professor emeritus of medicine at First Department of Medicine, Univesity of Pécs, Hungary. He was head of the Department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition. His research fields are biochemical and molecular pharmacological studies in gastrointestinal tract, clinical pharmacological and clinical nutritional studies, clinical genetic studies, and innovative pharmacological and nutritional (dietetical) research in new drug production and food production. He published around 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, 32 edited books. He organized 38 national and international (in Croatia ,France, Romania, Italy, U.S.A., Japan) congresses /Symposia. He received the Andre Robert’s award from the International Union of Pharmacology, Gastrointestinal Section (2014). 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1. Introduction
Research on control of non-linear systems over the years has produced many results: control based on linearization, global feedback linearization, non-linear H∞ control, sliding mode control, variable structure control, state dependent Riccati equation control, etc [Khalil, 2002]. This chapter will focus on fuzzy control techniques. Fuzzy control systems have recently shown growing popularity in non-linear system control applications. A fuzzy control system is essentially an effective way to decompose the task of non-linear system control into a group of local linear controls based on a set of design-specific model rules. Fuzzy control also provides a mechanism to blend these local linear control problems all together to achieve overall control of the original non-linear system. In this regard, fuzzy control technique has its unique advantage over other kinds of non-linear control techniques. Latest research on fuzzy control systems design is aimed to improve the optimality and robustness of the controller performance by combining the advantage of modern control theory with the Takagi-Sugeno fuzzy model [7, 8, 9, 10, 13, 14].
In this chapter, we address the non-linear state feedback control design of both continuous-time and discrete-time non-linear fuzzy control systems using the Linear Matrix Inequality (LMI) approach. We characterize the solution of the non-linear control problem with the LMI, which provides a sufficient condition for satisfying various performance criteria. A preliminary investigation into the LMI approach to non-linear fuzzy control systems can be found in [7, 8, 13]. The purpose behind this novel approach is to convert a non-linear system control problem into a convex optimization problem which is solved by a LMI at each time. The recent development in convex optimization provides efficient algorithms for solving LMIs. If a solution can be expressed in a LMI form, then there exist optimization algorithms providing efficient global numerical solutions [3]. Therefore if the LMI is feasible, then LMI control technique provides globally stable solutions satisfying the corresponding mixed performance criteria [4, 6, 15, 16, 17, 18, 19, 20]. We further propose to employ mixed performance criteria to design the controller guaranteeing the quadratic sub-optimality with inherent stability property in combination with dissipative type of disturbance attenuation.
In the following sections, we first introduce the Takagi-Sugeno fuzzy modelling for non-linear systems in both continuous time and discrete time. We then propose the general performance criteria in section 3. Then, the LMI control solutions are derived to characterize the optimal and robust fuzzy control of continuous time and discrete time non-linear systems, respectively. The inverted pendulum system control is used as an illustrative example to demonstrate the effectiveness and robustness of our proposed approaches.
The following notation is used in this work: x∈Rndenotes n-dimensional real vector with norm ∥x∥=(xTx)1/2 where (.)T indicates transpose. A≥0for a symmetric matrix denotes a positive semi-definite matrix. L2and l2 denotes the space of infinite sequences of finite dimensional random vectors with finite energy, i.e. ∫0∞∥xt∥2<∞in continuous-time, and Σk=0∞∥xk∥2<∞ in discrete-time, respectively.
2. Takagi-Sugeno system model
The importance of the Takagi-Sugeno fuzzy system model is that it provides an effective way to decompose a complicated non-linear system into local dynamical relations and express those local dynamics of each fuzzy implication rule by a linear system model. The overall fuzzy non-linear system model is achieved by fuzzy “blending” of the linear system models, so that the overall non-linear control performance is achieved. Both of the continuous-time and the discrete-time system models are summarized below.
2.1. Continuous-time Takagi-Sugeno system model
The ith rule of the Takagi-Sugeno fuzzy model can be expressed by the following forms:
Model Rulei:
If\n\t\t\t\t\tφ1(t)
\n\t\t\t\t
isMi1,φ2(t)
\n\t\t\t\t
isMi2,... and φp(t) isMip,
Then the input-affine continuous-time fuzzy system equation is:
where x(t)∈Rnis the state vector, u(t)∈Rmis the control input vector, y(t)∈Rqis the performance output vector, w(t)∈Rsis L2 type of disturbance, ris the total number of model rules, Mijis the fuzzy set. The coefficient matrices areAi∈Rn×n,Bi∈Rn×m,Fi∈Rn×s,Ci∈Rq×n,Di∈Rq×m,Zi∈Rq×s. And φ1,...,φp are known premise variables, which can be functions of state variables, external disturbance and time.
It is assumed that the premises are not the function of the input vectoru(t), which is needed to avoid the defuzzification process of fuzzy controller. If we use φ(t)to denote the vector containing all the individual elementsφ1(t),φ2(t),...,φp(t), then the overall fuzzy system is
where x(k)∈Rnis the state vector, u(k)∈Rmis the control input vector, y(k)∈Rqis the performance output vector, w(k)∈Rsis l2 type of disturbance, ris the total number of model rules, Mijis the fuzzy set. The coefficient matrices areAi∈Rn×n,Bi∈Rn×m,Fi∈Rn×s,Ci∈Rq×n,Di∈Rq×m,Zi∈Rq×s. And φ1,...,φp are known premise variables which can be functions of state variables, external disturbance and time.
It is assumed that the premises are not the function of the input vectoru(k), which is needed to avoid the defuzzification process of fuzzy controller. If we use φ(k)to denote the vector containing all the individual elementsφ1(k),φ2(k),...,φp(k), then the overall fuzzy system is
In this section, we propose the general performance criteria for non-linear control design, which yields a mixed Non-Linear Quadratic Regular (NLQR) in combination with H∞ or dissipative performance index. The commonly used system performance criteria, including bounded-realness, positive-realness, sector boundedness and quadratic cost criterion, become special cases of the general performance criteria. Both the continuous-time and discrete-time general performance criteria are given below:
By properly specifying the value of weighing matrices Q,R,Ci,Di,Ziandα,β,γ, mixed performance criteria can be used in non-linear control design, which yields a mixed Non-linear Quadratic Regulator (NLQR) in combination with dissipative type performance index with disturbance reduction capability. For example, if we takeα=1,β=0,γ<0, (25) yields
Other possible performance criteria which can be used in this framework with various design parameters α,β,γare given in Table.1. Design coefficients αand γcan be maximized or minimized to optimize the controller behavior. It should also be noted that the satisfaction of any of the criteria in Table 1 will also guarantee asymptotic stability of the controlled system.
By properly specifying the value of weighing matrices Q,R,Ci,Di,Ziandα,β,γ, mixed performance criteria can be used in non-linear control design, which yields a mixed Non-linear Quadratic Regulator (NLQR) in combination with dissipative type performance index with disturbance reduction capability. For example, if we takeα=1,β=0,γ<0, (29) yields
which is a mixed NLQR-H∞Design [16, 17, 18]. In (19), γcan be minimized to achieve a smaller l2-l2 or H∞ gain for the closed loop system.
Other possible performance criteria which can be used in this framework with various design parameters α,β,γare given in Table.1. Design coefficients αand γcan be maximized or minimized to optimize the controller behavior. It should also be noted that the satisfaction of any of the criteria in Table 1 will also guarantee asymptotic stability of the controlled system.
α
β
γ
Performance Criteria
1
0
<0
NLQR-H∞Design
1
0
NLQR-PassivityDesign
1
"/>0
NLQR-InputStrictPassivityDesign
"/>0
1
0
NLQR-OutputStrictPassivityDesign
"/>0
1
"/>0
NLQR-VeryStrictPassivity
Table 1.
Various performance criteria in a general framework
4. Fuzzy LMI control of continuous time non-linear systems with general performance criteria
The main results of this chapter are summarized in section 4 and section 5. The following theorem provides the fuzzy LMI control to the continuous time non-linear systems with general performance criteria.
Theorem 1 Given the system model and performance output (2) and control input (8), if there exist matrices S=P-1>0for allt≥0, such that the following LMI holds:
Hence, if the LMI (49) holds, inequality (24) is satisfied. This concludes the proof of the theorem.
Remark 1: For the chosen performance criterion, the LMI (49) need to be solved at each time to find matricesS,M, by using relation (33), we can find the feedback control gain, therefore, the feedback control can be found to satisfy the chosen criterion.
5. Fuzzy LMI control of discrete time non-linear systems with general performance criteria
This section summarizes the main results for fuzzy LMI control of discrete time non-linear systems with general performance criteria:
Theorem 2: Given the closed loop system and performance output (13), and control input (19), if there exist matrices S=P-1>0for allk≥0, such that the following LMI holds:
By factoring out the ∑i∑jhi(φk)hj(φk) term, we have
Ω11Ω12Ω13Ω14Ω15Ω22Ω23Ω240*P-100**I0***R-1≥0E128
\n\t\t\t
where
Ω11=P-QE129
Ω12=β4[Hji+Hij]TE130
Ω13=12(Gji+Gij))TE131
Ω14=12α1/2(Hij+Hji)TE132
Ω15=12(Ki+Kj)TE133
Ω22=-γI+β2(Zi+Zj)TE134
Ω23=12(Fi+Fj)TE135
Ω24=12α1/2(Zi+Zj)TE136
\n\t\t\t
By pre-multiplying and post-multiplying the matrix with the block diagonal matrixdiag(S,I,I,I,I), whereS=P-1, and applying Schur complement again, the following LMI result is obtained
whereS(k)=P-1(k), then (28) is satisfied with the feedback control gain being found by
K(k)=Y(k)P(k)E147
(69)
6. Application to the inverted pendulum system
The inverted pendulum on a cart problem is a benchmark control problem used widely to test control algorithms. A pendulum beam attached at one end can rotate freely in the vertical 2-dimensional plane. The angle of the beam with respect to the vertical direction is denoted at angleθ. The external force uis desired to set angle of the beam θ(x1) and angular velocity θ˙ (x2) to zero while satisfying the mixed performance criteria. A model of the inverted pendulum on a cart problem is given by [1, 9]:
where x1 is the angle of the pendulum from vertical direction, x2is the angular velocity of the pendulum, gis the gravity constant, mis the mass of the pendulum, Mis the mass of the cart, Lis the length of the center of mass (the entire length of the pendulum beam equals2L), uis the external force, control input to the system, wis the L2 type of disturbance, a=1m+Mis a constant, and ε1.ε2 is the weighing coefficients of disturbance.
Due to the system non-linearity, we approximate the system using the following two-rule fuzzy model:
and the initial condition ofx1(0)=π/6,x2(0)=-π/6. The membership function of Rule 1 and Rule 2 is shown below in Fig.1.
Figure 1.
Membership functions of Rule 1 and Rule 2.
Figure 2.
Angle trajectory of the inverted pendulum.
Figure 3.
Angular velocity trajectory of the inverted pendulum.
Figure 4.
Control input applied to the inverted pendulum.
The feedback control gain can be found from (31)(51) by solving the LMI at each time. The following design parameters are chosen to satisfy:
Mixed NLQR-H∞criteria:
C=[11],D=[1],Q=diag[1001],R=1,α=1,β=0,γ=-5E160
Mixed NLQR-passivitycriteria:
C=[11],D=[1],Q=diag[1001],R=1,α=1,β=5,γ=0E161
The mixed criteria control performance results are shown in the Figs.2-4. From these figures, we find that the novel fuzzy LMI control has satisfactory performance. The mixed NLQR-H∞criteria control has a smaller overshoot and a faster response than the one with passivity property. The new technique controls the inverted pendulum very well under the effect of finite energy disturbance. It should also be noted that the LMI fuzzy control with mixed performance criteria satisfies global asymptotic stability.
7. Summary
This chapter presents a novel fuzzy control approach for both of continuous time and discrete time non-linear systems based on the LMI solutions. The Takagi-Sugeno fuzzy model is applied to decompose the non-linear system. Multiple performance criteria are used to design the controller and the relative weighting matrices of these criteria can be achieved by choosing different coefficient matrices. The optimal control can be obtained by solving LMI at each time. The inverted pendulum is used as an example to demonstrate its effectiveness. The simulation studies show that the proposed method provides a satisfactory alternative to the existing non-linear control approaches.
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Takagi-Sugeno system model",level:"1"},{id:"sec_2_2",title:"2.1. Continuous-time Takagi-Sugeno system model",level:"2"},{id:"sec_3_2",title:"2.2. Discrete-time Takagi-Sugeno system model",level:"2"},{id:"sec_5",title:"3. General performance criteria",level:"1"},{id:"sec_5_2",title:"3.1. Continuous-time general performance criteria",level:"2"},{id:"sec_6_2",title:"3.2. Discrete-time general performance criteria",level:"2"},{id:"sec_8",title:"4. Fuzzy LMI control of continuous time non-linear systems with general performance criteria",level:"1"},{id:"sec_9",title:"5. Fuzzy LMI control of discrete time non-linear systems with general performance criteria",level:"1"},{id:"sec_10",title:"6. Application to the inverted pendulum system",level:"1"},{id:"sec_11",title:"7. Summary",level:"1"}],chapterReferences:[{id:"B1",body:'Baumann W.T, Rugh W.J1986Feedback Control of Non-linear Systems by Extended Linearization. IEEE Trans. Automatic Control. AC-3114046'},{id:"B2",body:'BasarT.BernhardP.1995H-infinity Optimal Control and Related Minimax Design Problems, A Dynamic Game Approach,nd Ed.,Birkhauser, 1995.'},{id:"B3",body:'BoydS.GhaouiL. E.FeronE.BalakrishnanV.1994Linear Matrix Inequalities in System and Control TheorySIAM Studies in Applied Mathematics, SIAM, Philadelphia.'},{id:"B4",body:'HuangY.Lu-MW.1996Non-linear Optimal Control: Alternatives to Hamilton-Jacobi Equation, Proc. of 35th Conf. on Decision and Control, Kobe, Japan, 39423947'},{id:"B5",body:'Khalil H.K2002Non-linear Systems, 3rd Ed., Prentice Hall, N.J.'},{id:"B6",body:'MohseniJ.YazE.OlejniczakK.1998State Dependent LMI Control of Discrete-Time Non-linear Systems, Proc. of the 37th IEEE Conference on Decision and Control, Tampa, FL, 46264627'},{id:"B7",body:'TakagiT.SugenoSugenoM(1985Fuzzy Identification of Systems and Its Applications to Model and Control, IEEE Trans. Syst. Man. Cyber., 15116132'},{id:"B8",body:'TanakaK.SugenoM.1990Stability Analysis of Fuzzy Systems Using Lyapunov’s Direct Method, Proc. NAFIPS90, 133136'},{id:"B9",body:'TanakaK.IkedaT.WangH. O.1996Design of Fuzzy Control Systems Based on Relaxed LMI Stability Conditionsthe 35th IEEE Conference on Decision and ControlKobe, 1598603'},{id:"B10",body:'TanakaK.WangH. O.2001Fuzzy Control Systems Design and Analysis, A Linear Matrix Inequality Approach,Wiley.'},{id:"B11",body:'Van der Shaft A.J1993Non-linear State Space H1 control Theory, in Perspectives in control, H. J. Trentelman and J. C.Willems, Eds. Birkhauser.'},{id:"B12",body:'VidyasagarM.2002Non-linear System Analysis, 2nd Ed., SIAM.'},{id:"B13",body:'Wang L.X1994Adaptive Fuzzy Systems and Control: Design and Stability Analysis,Prentice Hall, Englewood Cliffs, NJ.'},{id:"B14",body:'WangH. O.TanakaK.GriffinM.1996An Approach to Fuzzy Control of Non-linear Systems: Stability and Design Issues, IEEE Trans. Fuzzy Syst., 411423'},{id:"B15",body:'WangX.YazE. E.2009The State Dependent Control of Continuous-Time Non-linear Systems with Mixed Performance Criteria, Proc. of IASTED Int. Conf. on Identi cation Control and Applications, Honolulu, HI, 98102'},{id:"B16",body:'WangX.YazE. E.2010Robust multi-criteria optimal fuzzy control of continuous-time non-linear systems, Proc. of the 2010 American Control Conference, Baltimore, MD, USA, 64606465'},{id:"B17",body:'WangX.YazE. E.JeongC. S.2010Robust non-linear feedback control of discrete-time non-linear systems with mixed performance criteria, Proc. of the 2010 American Control Conference, Baltimore, MD, USA,63576362'},{id:"B18",body:'WangX.YazE. E.2010Robust multi-criteria optimal fuzzy control of discrete-time non-linear systems, Proc. of the 49th IEEE Conference on Decision and Control, Atlanta, Georgia, USA, 42694274'},{id:"B19",body:'WangX.YazE. E.YazY. I.2010Robust and resilient state dependent control of continuous-time non-linear systems with general performance criteria, Proc. of the 49th IEEE Conference on Decision and Control, Atlanta, Georgia, USA, 603608'},{id:"B20",body:'WangX.YazE. E.YazY. I.Robustresilientstate.dependentcontrol.ofdiscrete.timenon-linear.systemswith.generalperformance.criteriaProc.of discrete time non-linear systems with general performance criteria, Proc. of the 18th IFAC Congress, Milano, Italy, 1090410909'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Xin Wang",address:null,affiliation:'
Oregon Institute of Technology, Department of Electrical and Renewable Energy Engineering, Klamath Falls, Oregon, USA
'},{corresp:null,contributorFullName:"Edwin E. Yaz",address:null,affiliation:'
Marquette University, Department of Electrical and Computer Engineering, Haggerty Hall of Engineering, Milwaukee, Wisconsin, USA
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Saldanha",slug:"pedro-luiz-da-c.-saldanha"},{id:"182207",title:"D.Sc.",name:"Jonathan",middleName:"Marcello",surname:"Pinto",fullName:"Jonathan Pinto",slug:"jonathan-pinto"},{id:"182465",title:"MSc.",name:"Ian",middleName:null,surname:"Gomes",fullName:"Ian Gomes",slug:"ian-gomes"},{id:"182572",title:"MSc.",name:"Eustério",middleName:null,surname:"Furieri",fullName:"Eustério Furieri",slug:"eusterio-furieri"}]},{id:"51994",title:"Force Estimation for Teleoperating Industrial Robots",slug:"force-estimation-for-teleoperating-industrial-robots",signatures:"Enrique del Sol Acero",authors:[{id:"182748",title:"Dr.",name:"Enrique",middleName:null,surname:"Del Sol Acero",fullName:"Enrique Del Sol Acero",slug:"enrique-del-sol-acero"}]},{id:"51207",title:"Human Movement Control",slug:"human-movement-control",signatures:"David Balderas and Mario Rojas",authors:[{id:"183076",title:"M.Sc.",name:"David",middleName:null,surname:"Balderas Silva",fullName:"David Balderas Silva",slug:"david-balderas-silva"},{id:"184877",title:"MSc.",name:"Mario",middleName:null,surname:"Rojas",fullName:"Mario Rojas",slug:"mario-rojas"}]},{id:"51186",title:"Aircraft Landing Control Using the H-inf Control and the Dynamic Inversion Technique",slug:"aircraft-landing-control-using-the-h-inf-control-and-the-dynamic-inversion-technique",signatures:"Romulus Lungu and Mihai Lungu",authors:[{id:"181904",title:"Prof.",name:"Romulus",middleName:null,surname:"Lungu",fullName:"Romulus Lungu",slug:"romulus-lungu"}]},{id:"52178",title:"Adaptive Building Envelope: An Integral Approach to Indoor Environment Control in Buildings",slug:"adaptive-building-envelope-an-integral-approach-to-indoor-environment-control-in-buildings",signatures:"Mitja Košir",authors:[{id:"182476",title:"Dr.",name:"Mitja",middleName:null,surname:"Košir",fullName:"Mitja Košir",slug:"mitja-kosir"}]},{id:"51867",title:"Automatic Prompt Gamma‐Ray Analysis System: Automation of an Existing Large‐Scale Analytical Device",slug:"automatic-prompt-gamma-ray-analysis-system-automation-of-an-existing-large-scale-analytical-device",signatures:"Takahito Osawa",authors:[{id:"104085",title:"Dr.",name:"Takahito",middleName:null,surname:"Osawa",fullName:"Takahito Osawa",slug:"takahito-osawa"}]},{id:"51070",title:"Fuzzy PD Controller in NAO System's Platform",slug:"fuzzy-pd-controller-in-nao-system-s-platform",signatures:"Edgar Omar López‐Caudana and César Daniel González Gutiérrez",authors:[{id:"26464",title:"Dr.",name:"Edgar",middleName:"Omar",surname:"Lopez-Caudana",fullName:"Edgar Lopez-Caudana",slug:"edgar-lopez-caudana"},{id:"185936",title:"Mr.",name:"César Daniel",middleName:null,surname:"González Gutiérrez",fullName:"César Daniel González Gutiérrez",slug:"cesar-daniel-gonzalez-gutierrez"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"73718",title:"Development of a Versatile Modular Platform for Aerial Manipulators",doi:"10.5772/intechopen.94027",slug:"development-of-a-versatile-modular-platform-for-aerial-manipulators",body:'\n
\n
1. Introduction
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The 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].
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The 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.
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Multi-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].
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The 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.
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Pertaining 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.
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In 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.
Power 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\n\n\n2\n×\n22\n\n\n11.7\n×\n8\n\n\n×\n60\nmin\n=\n26\nmin\n\n.
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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\n45\n°\n−\n80\n°\n\n angle window using a Pulse Width Modulation (PWM) signal, provided by the FCU’s rail pins, with a 50 Hz switching frequency. The landing gear operation is achieved via the MAVlink protocol command set [12].
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Figure 2.
Landing gear detail (left) and payload assembly with battery holder (right).
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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\nX\nT\n30\n,\nX\nT\n60\n\n connectors protrude to provide 24, 19 and 12 V respectively to the end-user.
\n
Figure 3.
Enhanced power distribution board (left) and i7-minicomputer (right).
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\n
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2.2 Flight command unit and related software
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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.
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The \n\nHere\n+\n\n Global Navigation Satellite System (GNSS) [14] with Real-time kinematic (RTK) capabilities was selected for outdoor navigation and placed on top of a carbon fiber pole at a height of 35 cm from the main frame’s top plane. For immunity to electromagnetic interference, the primary magnetometer of the flight controller is selected to be the build-in magnetometer module of the GNSS receiver.
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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.
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On 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.
\n
The developed drone without any payload can be visualized in Figure 4.
\n
Figure 4.
Drone prototype.
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\n
\n
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3. Drone localization
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3.1 Drone outdoor localization using RTK GNSS
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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).
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Although 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.
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Figure 5.
Drone’s EKF 3D-position output with (red) and without (blue) RTK correction.
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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.
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The standard deviation was computed equal to 0.74 m, 0.47 m and 0.27 m for \n\nX\n\n, \n\nY\n\n and \n\nZ\n\n respectively when no RTK correction was applied. Contrary to this, the same values with RTK injection were computed to equal 0.05 m, 0.02 m and 0.23 m respectively. It should be noted that there is no significant improvement in the \n\nZ\n\n-direction, indicating the need to use either a barometer or a laser sensor for ground clearance measurements.
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3.2 Drone indoor localization
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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].
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For 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\n15\n×\n5\n×\n8\n=\n\n\nL\n×\nW\n×\nH\n\n\n\n m. The utilized system allows simultaneous tracking of 100 objects at 120 Hz with sub-millimeter accuracy. Despite MoCaS’s high refresh rate, the ArduCopter flight stack at the FCU accepts external positioning data at a 4 Hz streaming rate.
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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\n\nt\nd\n\n=\n\nt\nV\nC\n\n+\n\nt\nC\n\nF\nC\nU\n\n\n+\n\nt\n\nF\nC\nU\n\n\n\n, where \n\n\nt\nV\nC\n\n\n\nt\nC\n\nF\nC\nU\n\n\n\n\n is the delay of data streaming to the companion computer (FCU), and \n\n\nt\n\nF\nC\nU\n\n\n\n the delay of processing the data on the FCU. In the developed system typical measured values are \n\n\nt\nV\nC\n\n≃\n5\nms\n\n, \n\n\nt\nC\n\nF\nC\nU\n\n\n=\n20\nms\n\n and \n\n\nt\n\nF\nC\nU\n\n\n=\n40\nms\n\n, resulting in \n\n\nt\nd\n\n≃\n75\nms\n\n.
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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\nX\n\n\n\n\nY\n\n and \n\nZ\n\n axes respectively.
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Figure 6.
Drone’s EKF position error when MoCaS’ measurements are not injected.
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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.
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4. Drone awareness of surrounding environment
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An 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.
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High 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.
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4.1 Environment awareness using a spherical camera
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Rather 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\nN\n=\n12\n\n tiles, the resulting rectified partitions are visualized in Figure 8.
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Figure 7.
Spherical flat image.
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Figure 8.
Rectified \n\nN\n=\n12\n\n partitions for a single “spherical” frame.
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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.
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The 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.
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Figure 9.
Experimental setup for 360° camera relative localization.
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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.
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Figure 10.
3D-relative path inferred through the MoCaS and the visual method between two collaborating drones.
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4.2 Visual object tracking using a pan-tilt-zoom camera
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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.
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The 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.
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Figure 11.
PTZ-camera for visual object tracking.
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The objective is to provide the bounding box \n\n\np\nt\n\n\n of the approaching drone from the attached camera to the “Tracking drone” as shown in Figure 12. The IMU’s sensors are sent to an embedded EKF to compute the camera’s pan and tilt angles in the global coordinate system (and their angular velocities) at a 100 Hz rate. The angular velocities are used to compute the optical flow, and the angles are used for VOT purposes.
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Figure 12.
Sample drone tracking setup.
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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.
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Figure 13.
PTZ-camera hardware tracking and control schematic.
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Figure 14.
Pan-tilt-zoom/IMU and optical flow VOT algorithm.
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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.
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Figure 15.
Background/foreground estimation using Homography-based VOT.
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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.
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Figure 16.
Homography-based VOT.
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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.
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One 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.
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5. Aerial manipulation
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A 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.
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For 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.
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Figure 17.
Universal mount of robotic manipulators on aerial platform.
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Figure 18.
Aerial manipulation system with PTZ-camera.
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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\n45\n°\n\n angle in an a-priori known position. After taking off the FCU retracts the landing gear (if commanded) and moves the manipulator to its joint angles [180, 90, 180, −30, 90]° respectively. On arrival to the desired setpoint pose, the manipulator’s end tip comes into contact with the surface and the system hovers at the specified pose for some time for performing the area scan. The process is completed with the onboard computer initiating a landing after returning to the initial take-off position.
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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].
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].
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In 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.
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Conflict of interest
The authors declare no conflict of interest.
\n
Nomenclature
\n
\n\n\nUAV\n\n
unmanned aerial vehicle
\n\n\n\nRC\n\n
remote control
\n\n\n\nPDB\n\n
power distribution board
\n\n\n\nIMU\n\n
inertial measurement unit
\n\n\n\nFCU\n\n
flight control unit
\n\n\n\nGNSS\n\n
global navigation satellite system
\n\n\n\nGPS\n\n
global positioning system
\n\n\n\nRTK\n\n
real-time kinematic
\n\n\n\nEKF\n\n
extended Kalman filter
\n\n\n\nROS\n\n
robot operating system
\n\n\n\nDoF\n\n
degree-of-freedom
\n\n\n\nFoV\n\n
field-of-view
\n\n\n\nPTZ\n\n
pan-tilt-zoom
\n\n\n\nVOT\n\n
visual object tracking
\n\n\n\n
\n
\n',keywords:"aerial manipulation, visual localization",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73718.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73718.xml",downloadPdfUrl:"/chapter/pdf-download/73718",previewPdfUrl:"/chapter/pdf-preview/73718",totalDownloads:142,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 23rd 2020",dateReviewed:"September 14th 2020",datePrePublished:"October 22nd 2020",datePublished:"November 26th 2020",dateFinished:null,readingETA:"0",abstract:"The scope of this chapter is the development of an aerial manipulator platform using an octarotor drone with an attached manipulator. An on-board spherical camera provides visual information for the drone’s surroundings, while a Pan-Tilt-Zoom camera system is used to track targets. A powerful computer with a GPU offers significant on-board computational power for the visual servoing of the aerial manipulator system. This vision system, along with the Inertial Management Unit based controller provides exemplary guidance in confined and outdoor spaces. Coupled with the manipulator’s force sensing capabilities the system can interact with the environment. This aerial manipulation system is modular as far as attaching various payloads depending on the application (i.e., environmental sensing, facade cleaning and others, aerial netting for evader-drone geofencing, and others). Experimental studies using a motion capture system are offered to validate the system’s efficiency.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73718",risUrl:"/chapter/ris/73718",signatures:"Nikolaos Evangeliou, Athanasios Tsoukalas, Nikolaos Giakoumidis, Steffen Holter and Anthony Tzes",book:{id:"9902",title:"Service Robotics",subtitle:null,fullTitle:"Service Robotics",slug:"service-robotics",publishedDate:"November 26th 2020",bookSignature:"Volkan Sezer, Sinan Öncü and Pınar Boyraz Baykas",coverURL:"https://cdn.intechopen.com/books/images_new/9902.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"268170",title:"Dr.",name:"Volkan",middleName:null,surname:"Sezer",slug:"volkan-sezer",fullName:"Volkan Sezer"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"242670",title:"Dr.",name:"Anthony",middleName:null,surname:"Tzes",fullName:"Anthony Tzes",slug:"anthony-tzes",email:"anthony.tzes@nyu.edu",position:null,institution:{name:"New York University Abu Dhabi",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"320159",title:"Dr.",name:"Nikolaos",middleName:null,surname:"Evangeliou",fullName:"Nikolaos Evangeliou",slug:"nikolaos-evangeliou",email:"nikolaos.evangeliou@nyu.edu",position:null,institution:{name:"New York University Abu Dhabi",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"320160",title:"Dr.",name:"Athanasios",middleName:null,surname:"Tsoukalas",fullName:"Athanasios Tsoukalas",slug:"athanasios-tsoukalas",email:"athanasios.tsoukalas@nyu.edu",position:null,institution:{name:"New York University Abu Dhabi",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"320161",title:"Mr.",name:"Nikolaos",middleName:null,surname:"Giakoumidis",fullName:"Nikolaos Giakoumidis",slug:"nikolaos-giakoumidis",email:"giakoumidis@nyu.edu",position:null,institution:{name:"New York University Abu Dhabi",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"320162",title:"Mr.",name:"Steffen",middleName:null,surname:"Holter",fullName:"Steffen Holter",slug:"steffen-holter",email:"steffen.holter@nyu.edu",position:null,institution:{name:"New York University Abu Dhabi",institutionURL:null,country:{name:"United Arab Emirates"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Drone’s mechatronic design",level:"1"},{id:"sec_2_2",title:"2.1 Drone electric power units",level:"2"},{id:"sec_3_2",title:"2.2 Flight command unit and related software",level:"2"},{id:"sec_5",title:"3. Drone localization",level:"1"},{id:"sec_5_2",title:"3.1 Drone outdoor localization using RTK GNSS",level:"2"},{id:"sec_6_2",title:"3.2 Drone indoor localization",level:"2"},{id:"sec_8",title:"4. Drone awareness of surrounding environment",level:"1"},{id:"sec_8_2",title:"4.1 Environment awareness using a spherical camera",level:"2"},{id:"sec_9_2",title:"4.2 Visual object tracking using a pan-tilt-zoom camera",level:"2"},{id:"sec_11",title:"5. Aerial manipulation",level:"1"},{id:"sec_12",title:"6. Conclusions/discussion",level:"1"},{id:"sec_16",title:"Conflict of interest",level:"1"},{id:"sec_13",title:"Nomenclature",level:"1"}],chapterReferences:[{id:"B1",body:'\nMademlis I, Mygdalis V, Nikolaidis N, Pitas I. Challenges in Autonomous UAV Cinematography: An Overview. In: 2018 IEEE International Conference on Multimedia and Expo; 2018. p. 1-6.\n'},{id:"B2",body:'\nNourbakhsh IR, Sycara K, Koes M, Yong M, Lewis M, Burion S. Human-robot teaming for search and rescue. IEEE Pervasive Computing. 2005;4(1):72-79.\n'},{id:"B3",body:'\nHildmann H, Kovacs E. Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety. Drones. 2019;3(3):59.\n'},{id:"B4",body:'\nPrada Delgado J, Ramon Soria P, Arrue BC, Ollero A. Bridge Mapping for Inspection Using an UAV Assisted by a Total Station. In: ROBOT 2017: Third Iberian Robotics Conference. Springer International Publishing; 2018. p. 309-319.\n'},{id:"B5",body:'\nDing G, Wu Q, Zhang L, Lin Y, Tsiftsis TA, Yao Y. An Amateur Drone Surveillance System Based on the Cognitive Internet of Things. IEEE Communications Magazine. 2018;56(1):29-35.\n'},{id:"B6",body:'\nGanesh Y, Raju R, Hegde R. 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Forward-Backward Error: Automatic Detection of Tracking Failures. In: 2010 20th International Conference on Pattern Recognition; 2010. p. 2756-2759.\n'},{id:"B44",body:'\nKalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012 July;34(7):1409-1422.\n'},{id:"B45",body:'\nPaul Z, Musoff H. Fundamentals of Kalman Filtering: A Practical Approach. American Institute of Aeronautics and Astronautics, Inc.; 2015.\n'},{id:"B46",body:'\nJung S, Song S, Youn P, Myung H. Multi-Layer Coverage Path Planner for Autonomous Structural Inspection of High-Rise Structures. In: IEEE/RSJ Int. Conference on Intelligent Robots and Systems; 2018. p. 1-9.\n'},{id:"B47",body:'\nKinova Gen2 assistive manipulator; 2020. Internet. Available from: https://www.kinovarobotics.com/en/products/gen2-robot.\n'},{id:"B48",body:'\nStergiopoulos Y, Kontouras E, Gkountas K, Giannousakis K, Tzes A. Modeling and control aspects of a UAV with an attached manipulator. In: 24th Mediterranean Conference on Control and Automation; 2016. p. 653-658.\n'},{id:"B49",body:'\nChaikalis D, Khorrami F, Tzes A. Adaptive Control Approaches for an Unmanned Aerial Manipulation System. In: International Conference on Unmanned Aerial Systems; 2020. p. 498-503.\n'},{id:"B50",body:'\nUAE Miedfield Terminal Project; 2020. Internet. Available from: http://www.adac.ae/english/mtp/.\n'},{id:"B51",body:'\nUAE Miedfield Terminal structure maintenance promotional video; 2020. Internet. Available from: https://youtu.be/iD51n8OFUbg.\n'},{id:"B52",body:'\nAbbasi F, Mesbahi A, Velni JM. Coverage control of moving sensor networks with multiple regions of interest. In: American Control Conference; 2017. p. 3587-3592.\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Nikolaos Evangeliou",address:"nikolaos.evangeliou@nyu.edu",affiliation:'
Robotics and Intelligent Systems Control (RISC) Lab, Engineering Division, New York University Abu Dhabi, United Arab Emirates
Robotics and Intelligent Systems Control (RISC) Lab, Engineering Division, New York University Abu Dhabi, United Arab Emirates
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
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