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Modeling, Simulation, and Visualization of Agricultural and Field Robotic Systems

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Fundamentals of Agricultural and Field Robotics

Part of the book series: Agriculture Automation and Control ((AGAUCO))

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Abstract

Agricultural and field robots operating autonomously require several layers of technologies. To achieve the functionality and complexity needed, robot development takes place with simulation and visualization tools that can reduce physical prototyping and compliment field testing. Models are needed to implement simulation process and validate various modeling scopes. The degree of fidelity in reproducing the physical systems must be considered and decided as a part of the development process. Simulations of agricultural and field robots need dynamic system models of the robot, virtual world models, and sensor models, as well as three-dimensional solid models to visualize simulations. Agricultural and field robots often interact with the crop fields or plant environments, and so any simulation development process must also include interaction models between machine systems, soil, biological entities (e.g., plants, pests), and operators. This chapter provides an overview of modeling, simulation, and visualization aspects needed for the development of agricultural and field robotic systems. Different tool set scenarios are also described. In addition, a case study showing the use of Gazebo to model a phenotyping robot is presented.

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References

  • ABAQUS, Version 6.4. (2013) ABAQUS theory manual. Providence (RI, USA): ABAQUS, Inc.

    Google Scholar 

  • ASABE (2018) Soil cone penetrometer. ASAE Standard S313.3 FEB1999 (R2018)

    Google Scholar 

  • Ã…strand B, Baerveldt AJ, Astrand B, Baerveldt AJ (2002) An agricultural mobile robot with vision-based perception for mechanical weed control. Auton Robot 13(1):21–35

    Article  Google Scholar 

  • Autodesk (2012) DXF Reference. Available at: images.autodesk.com/adsk/files/autocad_2012_pdf_dxf-reference_enu.pdf

  • Bipin K (2018) Robot operating system cookbook. Packt Publishing, Birmingham

    Google Scholar 

  • Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley

    Google Scholar 

  • Bradley D, Seward D (1998) The development, control and operation of an autonomous robotic excavator. J Intell Robot Syst 21:73–97

    Article  Google Scholar 

  • Cellier FE (1991) Continuous system modeling. Springer, New York

    Book  Google Scholar 

  • Chen WF, Mizuno E (1990) Non-linear analysis in soil mechanics: theory and implementation. Dev Geotech Eng 53

    Google Scholar 

  • Chiroux RC, Foster WA Jr, Johnson CE, Shoop SA, Raper RL (2005) Three-dimensional finite element analysis of soil interaction with rigid wheel. Appl Math Comput 2005(162):707–722

    Google Scholar 

  • Choi S, Seo KM, Kim T (2017) Accelerated simulation of discrete event dynamic systems via a multi-fidelity modeling framework. Appl Sci 7(10):1056

    Article  Google Scholar 

  • Coumans, E. (2015). Bullet physics simulation: introduction to rigid body dynamics and collision detection

    Book  Google Scholar 

  • Cundall PA, Strack ODL (1979) A discrete numerical model for granular assemblies. Geotechnique 29(1):47–65

    Article  Google Scholar 

  • DeBord M (2018) Waymo just crossed 10 million self-driving miles—but the company has a secret weapon that gives it even more of an edge. Business Insider. Accessed 6 June 2019

    Google Scholar 

  • Du Y, Dorneich MC, Steward BL (2018) Modeling expertise and adaptability in virtual operator models. Autom Constr 90:223–234

    Article  Google Scholar 

  • Du Y, Dorneich MC, Steward BL (2019) Development of a learning capability in virtual operator models. SAE Int J Commer Veh 12(2)

    Google Scholar 

  • Dymola (2018) Dymola user manual volume 1, March 2018, Version 2019, p 609

    Google Scholar 

  • Dyrmann M, Christiansen P, Midtiby HS (2018) Estimation of plant species by classifying plants and leaves in combination. J Field Robot 35(2):202–212

    Article  Google Scholar 

  • EDEM (2011) EDEM theory reference guide. DEM Solutions, Edinburgh

    Google Scholar 

  • Elezaby AA (2011) Virtual autonomous operator model for construction equipment applications. Dissertation, University of Illinois – Chicago, Illinois

    Google Scholar 

  • Enes AR (2010) Shared control of hydraulic manipulators to decrease cycle time. Ph.D. thesis, Georgia Tech

    Google Scholar 

  • Erez T, Tassa Y, Todorov E (2015, May) Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx. In 2015 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4397–4404

    Google Scholar 

  • Filla R (2005) Operator and machine models for dynamic simulation of construction machinery. Thesis. Linkoping University, Linkoping, Sweden

    Google Scholar 

  • Filla R, Ericsson A, Palmberg JO (2005) Dynamic simulation of construction machinery: towards an operator model. International fluid power exhibition 2005 technical conference, Las Vegas, (NV), USA, pp 429–438

    Google Scholar 

  • FMI (2014) Functional mock-up interface for model exchange and co-simulation. Version 2.0. July 25, 2014. Accessed at fmi-standard.org/docs/2.0.1-develop/

  • Foley, J. D., van Dam, A., Feiner, S. K., Hughes, J. F., Hughes, J. & Angel, E. (1996). Computer graphics: principles and practice in C (2nd). Addison-Wesley Professional Upper Saddle River

    Google Scholar 

  • Fritzson P (2015) Principles of object-oriented modeling and simulation with Modelica 3.3: a cyber-physical approach. IEEE Press/Wiley, Piscataway

    Google Scholar 

  • Gai, J., Tang, L., & Steward, B. L. (2019). Automated crop plant detection based on the fusion of color and depth images for robotic weed control J Field Robot 21897

    Google Scholar 

  • Gazebo (2019) Tutorial: using a URDF in gazebo. From gazebosimorg/tutorials/?tut=ros_urdf. Accessed on 2 Aug 2019

  • Gazebo (2020) Gazebo tutorials: sensors. From http://gazebosim.org/tutorials?cat=sensors. Accessed on 23 Apr 2020

  • Gill WR, Vanden Berg GE (1968) Soil dynamics in tillage and traction. Agriculture handbook no. 316. USDA-Agricultural Research Service, Washington, DC

    Google Scholar 

  • Godwin RJ, Spoor G (1977) Soil failure with narrow tines. J Agric Eng Res 22(3):213–228

    Google Scholar 

  • Grimm (2004) User’s guide to rapid prototyping. Society of Manufacturing Engineers, Dearborn

    Google Scholar 

  • Han SF, Steward BL, Tang L (2015) Intelligent agricultural machinery and field robots. In: Zhang Q (ed) Precision agriculture for crop farming. CRC Press, Boca Raton, pp 133–176

    Chapter  Google Scholar 

  • Higuti VAH, Velasquez AEB, Magalhaes DV, Becker M, Chowdhary G (2018) Under canopy light detection and ranging-based autonomous navigation. J Field Robot

    Google Scholar 

  • Imperoli M, Potena C, Nardi D, Grisetti G, Pretto A (2018) An effective multi-cue positioning system for agricultural robotics. IEEE Robot Autom Lett 3(4):3685–3692

    Article  Google Scholar 

  • ISO (2016) ISO standard ISO 10303-21:2016. Industrial automation systems and integration—product data representation and exchange—part 21: Implementation methods: clear text encoding of the exchange structure

    Google Scholar 

  • Janosi Z (1962) Theoretical analysis of the performance of tracks and wheels operating on deformable soil. Trans ASABE 5(64):133–134

    Article  Google Scholar 

  • Karkee M, Steward BL (2010) Study of the open and closed loop characteristics of a tractor and a single axle towed implement system. J Terrramech 47(6):379–393

    Article  Google Scholar 

  • Kavan L (2003) Rigid body collision response. Vectors 1000:2

    Google Scholar 

  • Koolen AJ, Kuipers H (1983) Agricultural soil mechanics. Advanced series in agricultural sciences 13. Springer, Berlin/Heidelberg

    Google Scholar 

  • Kyllo KP (2003) NASA funded research on agricultural remote sensing, Department of Space Studies, University of North Dakota

    Google Scholar 

  • Lee J, Grey M, Ha S, Kunz T, Jain S, Ye Y et al (2018) Dart: dynamic animation and robotics toolkit. J Open Source Softw 3(22):500

    Article  Google Scholar 

  • Li J, Tang L (2018) Crop recognition under weedy conditions based on 3D imaging for robotic weed control. J Field Robot 35(4):596–611

    Article  Google Scholar 

  • Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors (Switzerland). MDPI AG

    Google Scholar 

  • Liu L, Mei T, Niu R, Wang J, Liu Y, Chu S (2016) RBF-based monocular vision navigation for small vehicles in narrow space below maize canopy. Appl Sci 6(6):182

    Article  Google Scholar 

  • Mathworks (2019a) Basic principles of modeling physical networks. Accessed 10 June 2019 at https://www.mathworks.com/help/physmod/simscape/ug/basic-principles-of-modeling-physical-networks.html

  • Mathworks (2019b) Lane-following control with monocular camera perception. Accessed 8 Aug 2019 at https://www.mathworks.com/help/mpc/ug/lane-following-control-with-monocular-camera-perception.html

  • Mathworks (2020a) Simulation of a bouncing ball. Accessed 23 Apr 2020 at https://www.mathworks.com/help/simulink/slref/simulation-of-a-bouncing-ball.html

  • Mathworks (2020b) UAV competition example. Accessed 23 Apr 2020 at https://www.mathworks.com/help/sl3d/examples/uav-competition-example.html

  • Modelica (2019) Modelica tools. Accessed www.modelica.org/tools on 13 Sept 2019

  • Mondesire SC, Stevens DBMJ, Zielinski S, Martin GA (2016) Physics engine benchmarking in three-dimensional virtual world simulation. MODSIM World:5–8

    Google Scholar 

  • Moore M, Wilhelms J (1988) Collision detection and response for computer animation. ACM Siggraph Comput Graph 22(4):289–298

    Article  Google Scholar 

  • Mousazadeh H (2013, June 1) A technical review on navigation systems of agricultural autonomous off-road vehicles. J Terramech. Elsevier Ltd

    Google Scholar 

  • Nelson R (2018) Simulation and test drive vehicle success. EE-Evaluat Eng 57(10):6–13

    Google Scholar 

  • Norris WR, Zhang Q, Sreenivas R, Lopez-Dominguez JC (2003) A design tool for operator-adaptive steering controllers. Trans Am Soc Agric Eng 46(3):883–892

    Article  Google Scholar 

  • ODE (2019) ODE manual. Accessed at http://ode.org/wiki/index.php?title=Manual#Collision_handling on 15 July

  • Pinto FAC, Reid JF, Zhang Q, Noguchi N (2000) Vehicle guidance parameter determination from crop row images using principal component analysis. J Agric Eng Res

    Google Scholar 

  • Pratt MJ (2001) Introduction to ISO 10303—the STEP standard for product data exchange. J Comput Inf Sci Eng 1(1):102–103

    Article  Google Scholar 

  • Raper RL, Johnson CE, Bailey AC, Burt EC, Block WA (1995) Prediction of soil stresses beneath a rigid wheel. J Agric Eng Res 61:57–62

    Article  Google Scholar 

  • Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J (2019, January 1) Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and electronics in agriculture. Elsevier B.V.

    Google Scholar 

  • ROS (2019) ROS/Introduction – ROS Wiki. Retrieved September 8, 2019, from http://wiki.ros.org/ROS/Introduction

  • Rosique F, Navarro PJ, Fernández C, Padilla A (2019) A systematic review of perception system and simulators for autonomous vehicles research. Sensors 19(3):648

    Article  PubMed Central  Google Scholar 

  • Sakaguchi E, Ozaki E, Igarashi T (1993) Plugging of the flow of granular materials during the discharge from a silo. Int J Mod Phys B 7:1949–1963

    Article  Google Scholar 

  • Shabana AA (2020) Dynamics of multibody systems, 5th edn. Cambridge University Press, New York

    Book  Google Scholar 

  • Shamshiri R, Hameed IA, Pitonakova L, Weltzien C, Balasundram SK, Yule IJ, Grift TE, Chowdhary G (2018) Simulation software and virtual environments for acceleration of agricultural robotics: features highlights and performance comparison. Int J Agric Biol Eng 11(4):15–31

    Google Scholar 

  • Shen J, Kushwaha RL (1998) Soil–machine interaction a finite element perspective. Marcel Dekker, Inc., New York

    Google Scholar 

  • Sherman MA, Seth A, Delp SL (2011) Simbody: multibody dynamics for biomedical research. Procedia Iutam 2:241–261

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith LN, Zhang W, Hansen MF, Hales IJ, Smith ML (2018) Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field. Comput Ind 97:122–131

    Article  PubMed  PubMed Central  Google Scholar 

  • Steward BL, Gai J, Tang L (2019) The use of agricultural robots in weed management and control. In Robotics and Automation for Improving Agriculture. ed. J. Billingsley. Burleigh Dodds Science Publishing, Cambridge, UK

    Google Scholar 

  • Taheri S, Sandu C, Taheri S, Pinto E, Gorsich D (2015) A technical survey on Terramechanics models for tire–terrain interaction used in modeling and simulation of wheeled vehicles. J Terrramech 57:1–22

    Article  Google Scholar 

  • Tang L, Tian LF (2008) Plant identification in mosaicked crop row images for automatic emerged corn plant spacing measurement. Trans ASABE 51(6):2181

    Article  Google Scholar 

  • Tang L, Tian LF, Steward BL (2003) Classification of broadleaf and grass weeds using Gabor wavelets and an artificial neural network. Trans ASAE 46(4):1247

    Article  Google Scholar 

  • Tekeste MZ, Tollner EW, Raper RL, Way TR, Johnson CE (2009) Non-linear finite element analysis of cone penetration in layered sandy loam soil – considering precompression stress state. J Terrramech 46:229–239

    Google Scholar 

  • Tiller MM (2001) Introduction to physical modeling with Modelica. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  • Tsuji Y, Tanaka T, Ishida T (1992) Lagrangian numerical simulation of plug flow ofcohesionless particles in a horizontal pipe. Powder Technol 71:239–250

    Article  CAS  Google Scholar 

  • Upadhyaya SK (2009) Traction prediction equations. In: Upadhyaya SK, Chancellor WJ, Perumpral JV, Wulfsohn D, Way TRW (eds) Advances in soil dynamics, vol 3. ASAE, St. Joseph, pp 117–153

    Google Scholar 

  • Upadhyaya SK, Rosa UA, Wulfsohn D (2002) Application of the finite element method in agricultural soil mechanics. In: Upadhyaya SK, Chancellor WJ, Perumpral JV, Schafer RL, Gill WR, VandenBerg GE (eds) Advances in soil dynamics, vol 2. ASAE, St. Joseph, pp 117–153. [Chapter 2]

    Chapter  Google Scholar 

  • Varghese A, Turner JL, Way RT, Johnson CE, Dorfi HR (2013) Traction prediction of a smooth rigid wheel using coupled Eulerian-Largrangian analysis. Proceedings of 2012 SIMULIA Community Conference, Providence RI, USA

    Google Scholar 

  • Vázquez-Arellano M, Griepentrog HW, Reiser D, Paraforos DS (2016) 3-D imaging systems for agricultural applications—a review. Sensors 16(5)

    Google Scholar 

  • Vrindts E, De Baerdemaeker J, Ramon H (2002) Weed detection using canopy reflection. Precis Agric 3(1):63–80

    Article  Google Scholar 

  • Walter-Shea EA, Norman JM (1991) Leaf optical properties. In Photon-vegetation interactions, pp 229–251

    Google Scholar 

  • Wheeler PN, Godwin RJ (1996) Soil dynamics of single and multiple tines at speeds up to 20 km/h. J Agric Eng Res 63(3):243–249

    Google Scholar 

  • Williams MA, Alleyne AG (2014, January) Variable fidelity modeling in closed loop dynamical systems. In ASME 2014 Dynamic Systems and Control Conference, DSCC 2014

    Google Scholar 

  • Wong JY (2010) Terramechanics and off-road vehicle engineering: terrain behavior, off-road vehicle performance and design, 2nd edn. The Butterworth-Heinemann, Oxford

    Google Scholar 

  • Wood MD (1990) Soil behavior and critical state soil mechanics. Cambridge University Press, Cambridge

    Google Scholar 

  • Wu L (2003) A study on automatic control of wheel loaders in rock/soil loading. Ph.D. thesis, University of Arizona

    Google Scholar 

  • Wu, S. G., Bao, F. S., Xu, E. Y., Wang YX, Chang YF, Xiang QL (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. ISSPIT 2007 – 2007 IEEE international symposium on signal processing and information technology, pp 11–16

    Google Scholar 

  • Xue J, Xu L (2010) Autonomous agricultural robot and its row guidance. In 2010 international conference on measuring technology and mechatronics automation, ICMTMA 2010, 1, pp 725–729

    Google Scholar 

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Steward, B.L., Tekeste, M.Z., Gai, J., Tang, L. (2021). Modeling, Simulation, and Visualization of Agricultural and Field Robotic Systems. In: Karkee, M., Zhang, Q. (eds) Fundamentals of Agricultural and Field Robotics. Agriculture Automation and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-70400-1_12

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