Autonomous trajectory tracking control method for an agricultural robotic vehicle
Keywords:
trajectory tracking, autonomy control, agricultural robotic vehicle, online PSO continuously tuned PID, dynamic pure pursuit algorithmAbstract
To address the nonlinearities and external disturbances in unstructured and complex agricultural environments, this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles. Firstly, this paper presents the design and implementation of a lightweight, modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels. The vehicle comprises drive wheel modules, passive wheel modules, battery modules, a vehicle frame, a sensor system, and a control system. Secondly, a novel robust trajectory tracking method was proposed, utilizing an improved pure pursuit algorithm. Additionally, an Online Particle Swarm Optimization Continuously Tuned PID (OPSO-CTPID) controller was introduced to dynamically search for optimal control gains for the PID controller. Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy. To validate the performance, the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment. Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance. The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error (RMSE) of the curve and straight lines, given a suitable set of parameters, meeting the requirements of agricultural applications. The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots. Keywords: trajectory tracking, autonomy control, agricultural robotic vehicle, online PSO continuously tuned PID, dynamic pure pursuit algorithm DOI: 10.25165/j.ijabe.20241701.7296 Citation: Yan J, Zhang W G, Liu Y, Pan W, Hou X Y, Liu Z Y. Autonomous trajectory tracking control method for an agricultural robotic vehicle. Int J Agric & Biol Eng, 2024; 17(1): 215-224.References
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[29] Wang Y B, Peng X, Wei B Z. A new particle swarm optimization based auto-tuning of PID controller. In: 2008 International Conference on Machine Learning and Cybernetics, Kunming: IEEE, 2008; pp.1818-1823. doi: 10.1109/ICMLC.2008.4620701.
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[32] Quigley M, Gerkey B, Conley K, Faust J, Foote T, Leibs J, et al. ROS: An open-source Robot Operating System. ICRA Workshop on Open Source Software, 2009; 3: 5.
[33] Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, et al. Stanley: The robot that won the DARPA grand challenge. Journal of Field Robotics, 2006; 23(9): 661–692.
[34] Tu X Y, Gai J Y, Tang L. Robust navigation control of a 4WD/4WS agricultural robotic vehicle. Computers and Electronics in Agriculture, 2019; 164: 104892.
[2] Ferrández-Pastor F J, Ferrández-Pastor J M, Nieto-Hidalgo M, Mora-Pascual J, Ferrández-Pastor J. Developing ubiquitous sensor network platform using Internet of Things: Application in precision agriculture. Sensors, 2016; 16(7): 1141.
[3] Bannerjee G, Sarkar U, Das S, Ghosh I. Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 2018; 7(3): 1–6.
[4] Bhatnagar V, Singh G, Kumar G, Gupta R. Internet of things in smart agriculture: Applications and open challenges. International Journal of Students’ Research in Technology & Management, 2020; 8(1): 11–17.
[5] Gu H L, Wang H W. Innovative design of modern agricultural industry chain under 5G era. Value Engineering, 2019; 38(16): 69–71.
[6] Bell T. Automatic tractor guidance using carrier-phase differential GPS. Computers and Electronics in Agriculture, 2000; 25(1-2): 53–66.
[7] Matveev A S, Hoy M, Katupitiya J, Savkin A. Nonlinear sliding mode control of an unmanned agricultural tractor in the presence of sliding and control saturation. Robotics and Autonomous Systems, 2013; 61(9): 973–987.
[8] Zhang S, Liu J Y, Du Y F, Zhu Z X, Mao E R, Song Z H. Method on automatic navigation control of tractor based on speed adaptation. Transactions of the CSAE, 2017; 33(23): 48–55. (in Chinese)
[9] Ruckelshausen A, Biber P, Dorna M, Gremmes H, Klose R Linz A, et al. BoniRob: An autonomous field robot platform for individual plant phenotyping. Precision Agriculture ’09, 2009; pp.841-847.
[10] Scholz C, Kohlbrecher M, Ruckelshausen A, Kinski D, Mentrup D. Camera-based selective weed control application module (“Precision Spraying App”) for the autonomous field robot platform BoniRob. Proceedings InInternational Conference of Agricultural Engineering, Zurich, 2014; Paper No. C0598. Available:
[11] Bakker T, van Asselt, Bontsema J, Müller J, van Straten G. Autonomous navigation using a robot platform in a sugar beet field. Biosystems Engineering, 2011; 109(4): 357–368.
[12] Bakker ., Van K ., Bontsema ., Müller J, Van G S. Systematic design of an autonomous platform for robotic weeding. Journal of Terramechanics, 2010; 47(2): 63–73.
[13] Bawden O, Ball D, Kulk J, Perez T, Russell R. A lightweight, modular robotic vehicle for the sustainable intensification of agriculture. In: Proceedings of the 16th Australasian Conference on Robotics and Automation Australian Robotics and Automation Association (ARAA), 2014; pp.1-9.
[14] Grimstad L, Skattum K, Solberg E, Loureiro G, From P J. Thorvald II configuration for wheat phenotyping. In: Proceedings of the IROS Workshop on Agri‐Food Robotics: Learning from Industry. 2017; 4. Available: https://agrifoodroboticsworkshop.files.wordpress.com/2017/09/agrob_2017_paper_7.pdf. Accessed on [2021-06-05].
[15] Hossain T, Habibullah H, Islam R. Steering and speed control system design for autonomous vehicles by developing an optimal hybrid controller to track reference trajectory. Machines, 2022; 10(6): 420.
[16] Wang R, Li Y, Fan J H, Wang T, Chen X T. A novel pure pursuit algorithm for autonomous vehicles based on salp swarm algorithm and velocity controller. IEEE Access, 2020; 8: 166525–166540.
[17] Wallace R S, Stentz A, Thorpe C E, Moravec H, Whittaker W, Kanade T, et al. First results in robot road-following. In: IJCAI, 1985; pp.1089–1095.
[18] Cao S X, Jin Y, Trautmann T, Liu K. Design and experiments of autonomous path tracking based on dead reckoning. Applied Sciences, 2022; 13(1): 317.
[19] Ohta H, Akai N, Takeuchi E, Kato S, Edahiro M. Pure pursuit revisited: field testing of autonomous vehicles in urban areas. In: 2016 IEEE 4th International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), Nagoya: IEEE, 2016; pp.7–12. doi: 10.1109/CPSNA.2016.10.
[20] Park M W, Lee S W, Han W Y. Development of lateral control system for autonomous vehicle based on adaptive pure pursuit algorithm. In: 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), Korea: IEEE, 2014; pp.1443–1447. doi: 10.1109/OCCAS.2014.6987787.
[21] Yu L L, Yan X X, Kuang Z X, Chen B F, Zhao Y Q. Driverless bus path tracking based on fuzzy pure pursuit control with a front axle reference. Applied Sciences, 2020; 10(1): 230.
[22] Gámez Serna C, Lombard A, Ruichek Y, Abbas-Turki A. GPS-based curve estimation for an adaptive pure pursuit algorithm. Advances in Computational Intelligence: 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Cancún: Springer International Publishing, 2017; pp.497-511. doi: 10.1007/978-3-319-62434-1_40.
[23] Wang W J, Hsu T M, Wu T S. The improved pure pursuit algorithm for autonomous driving advanced system. In: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). Hiroshima: IEEE, 2017; pp.33-38. doi: 10.1109/IWCIA.2017.8203557.
[24] Bakker T, van Asselt K, Bontsema J, Müller J, van Straten G. A path following algorithm for mobile robots. Autonomous Robots, 2010; 29: 85–97.
[25] Han G N, Fu W P, Wang W, Wu Z S. The lateral tracking control for the intelligent vehicle based on adaptive PID neural network. Sensors, 2017; 17(6): 1244.
[26] Al-Mayyahi A, Wang W, Birch P. Path tracking of autonomous ground vehicle based on fractional order PID controller optimized by PSO. In: 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl’any: IEEE, 2015; pp.109-114.
[27] Poultangari I, Shahnazi R, Sheikhan M. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm. ISA Transactions, 2012; 51(5): 641–648.
[28] Kashyap A K, Parhi D R. Particle swarm optimization aided PID gait controller design for a humanoid robot. ISA Transactions, 2021; 114: 306–330.
[29] Wang Y B, Peng X, Wei B Z. A new particle swarm optimization based auto-tuning of PID controller. In: 2008 International Conference on Machine Learning and Cybernetics, Kunming: IEEE, 2008; pp.1818-1823. doi: 10.1109/ICMLC.2008.4620701.
[30] Salamat B, Tonello A M. Adaptive nonlinear PID control for a quadrotor UAV using particle swarm optimization. In: 2019 IEEE Aerospace Conference, Gig Sky: IEEE, 2019; pp.1-12. doi: 10.1109/AERO.2019.8741829.
[31] Rajinikanth V, Latha K. Tuning and retuning of PID controller for unstable systems using evolutionary algorithm. International Scholarly Research Notices, 2012; 2012: 693545.
[32] Quigley M, Gerkey B, Conley K, Faust J, Foote T, Leibs J, et al. ROS: An open-source Robot Operating System. ICRA Workshop on Open Source Software, 2009; 3: 5.
[33] Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, et al. Stanley: The robot that won the DARPA grand challenge. Journal of Field Robotics, 2006; 23(9): 661–692.
[34] Tu X Y, Gai J Y, Tang L. Robust navigation control of a 4WD/4WS agricultural robotic vehicle. Computers and Electronics in Agriculture, 2019; 164: 104892.
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Published
2024-03-31
How to Cite
Yan, J., Zhang, W., Liu, Y., Pan, W., Hou, X., & Liu, Z. (2024). Autonomous trajectory tracking control method for an agricultural robotic vehicle. International Journal of Agricultural and Biological Engineering, 17(1), 215–224. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7296
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Information Technology, Sensors and Control Systems
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