Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

In this study, a differential wheeled mobile robot was controlled in real time using pure pursuit algorithm (PPA). The robot was obtained in a simulation environment by using Gazebo simulator which offer the ability to accurately and efficiently simulate various robots in complex indoor/outdoor environments. This simulator was operated with robot operating system (ROS) that allows the use of Python, C++, MATLAB or various programming languages. In this paper, MATLAB/Simulink environment was used to control the robot with communication interface between MATLAB and ROS. Thus, it is possible to study more comprehensively by using multiple the features of MATLAB. The robot was traveled around a 4m x 4m area with random waypoints. The position of the robot was measured based on odometer sensor in order to determine the robot’s location. The performance of the control algorithm was analyzed using various information of the robot such as robot’s velocity, motors’ speed, the position of the robot, etc.

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  • [1] F. Rubio, F. Valero, and C. Llopis-Albert, “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” Int. J. Adv. Robot. Syst., vol. 16, no. 2, p. 1729881419839596, 2019, doi: 10.1177/1729881419839596.
  • [2] T. Namba and Y. Yamada, “Risks of Deep Reinforcement Learning Applied to Fall Prevention Assist by Autonomous Mobile Robots in the Hospital,” Big Data Cogn. Comput., vol. 2, no. 2, 2018, doi: 10.3390/bdcc2020013.
  • [3] G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, pp. 405–426, 2021, doi: https://doi.org/10.1016/j.ejor.2021.01.019.
  • [4] X. Gao et al., “Review of Wheeled Mobile Robots’ Navigation Problems and Application Prospects in Agriculture,” IEEE Access, vol. 6, pp. 49248–49268, 2018, doi: 10.1109/ACCESS.2018.2868848.
  • [5] S. Tanwar, S. Tyagi, and S. Kumar, “The Role of Internet of Things and Smart Grid for the Development of a Smart City,” in Lecture Notes in Networks and Systems, vol. 19, Springer, 2018, pp. 23–33.
  • [6] A. Bärtschi et al., “Collaborative delivery with energy-constrained mobile robots,” Theor. Comput. Sci., vol. 810, pp. 2–14, 2020, doi: https://doi.org/10.1016/j.tcs.2017.04.018.
  • [7] G. Tuna, V. C. Gungor, and K. Gulez, “An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters,” Ad Hoc Networks, vol. 13, pp. 54–68, 2014, doi: https://doi.org/10.1016/j.adhoc.2012.06.006.
  • [8] H. Fan, V. Hernandez Bennetts, E. Schaffernicht, and A. J. Lilienthal, “Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose,” Sensors, vol. 19, no. 3, 2019, doi: 10.3390/s19030685.
  • [9] E. Jochum, P. Millar, and D. Nuñez, “Sequence and chance: Design and control methods for entertainment robots,” Rob. Auton. Syst., vol. 87, pp. 372–380, 2017, doi: https://doi.org/10.1016/j.robot.2016.08.019.
  • [10] K. Schilling and C. Jungius, “Mobile robots for planetary exploration,” Control Eng. Pract., vol. 4, no. 4, pp. 513–524, 1996, doi: https://doi.org/10.1016/0967-0661(96)00034-2.
  • [11] S. Mellah, G. Graton, E. M. El Adel, M. Ouladsine, and A. Planchais, “Actuator Health State Monitoring amp; Degradation Impact Study on a 4-Mecanum Wheeled Mobile Robot Behaviour,” in 2021 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 1076–1081, doi: 10.1109/MED51440.2021.9480231.
  • [12] Z. Sun, H. Xie, J. Zheng, Z. Man, and D. He, “Path-following control of Mecanum-wheels omnidirectional mobile robots using nonsingular terminal sliding mode,” Mech. Syst. Signal Process., vol. 147, p. 107128, 2021, doi: https://doi.org/10.1016/j.ymssp.2020.107128.
  • [13] A. Stefek, T. Van Pham, V. Krivanek, and K. L. Pham, “Energy Comparison of Controllers Used for a Differential Drive Wheeled Mobile Robot,” IEEE Access, vol. 8, pp. 170915–170927, 2020, doi: 10.1109/ACCESS.2020.3023345.
  • [14] R. P. M. Chan, K. A. Stol, and C. R. Halkyard, “Review of modelling and control of two-wheeled robots,” Annu. Rev. Control, vol. 37, no. 1, pp. 89–103, 2013, doi: https://doi.org/10.1016/j.arcontrol.2013.03.004.
  • [15] S. Peng and W. Shi, “Adaptive Fuzzy Output Feedback Control of a Nonholonomic Wheeled Mobile Robot,” IEEE Access, vol. 6, pp. 43414–43424, 2018, doi: 10.1109/ACCESS.2018.2862163.
  • [16] M. Begnini, D. W. Bertol, and N. A. Martins, “A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results,” Control Eng. Pract., vol. 64, pp. 27–43, 2017, doi: https://doi.org/10.1016/j.conengprac.2017.04.006.
  • [17] P. Petrov and V. Georgieva, “Adaptive Velocity Control for a Differential Drive Mobile Robot,” in 2018 20th International Symposium on Electrical Apparatus and Technologies (SIELA), 2018, pp. 1–4, doi: 10.1109/SIELA.2018.8447091.
  • [18] M. Oliveira, A. Castro, T. Madeira, E. Pedrosa, P. Dias, and V. Santos, “A ROS framework for the extrinsic calibration of intelligent vehicles: A multi-sensor, multi-modal approach,” Rob. Auton. Syst., vol. 131, p. 103558, 2020, doi: https://doi.org/10.1016/j.robot.2020.103558.
  • [19] R. Wang, Y. Li, J. Fan, T. Wang, and X. Chen, “A Novel Pure Pursuit Algorithm for Autonomous Vehicles Based on Salp Swarm Algorithm and Velocity Controller,” IEEE Access, vol. 8, pp. 166525–166540, 2020, doi: 10.1109/ACCESS.2020.3023071.
  • [20] J. Morales, J. L. Martínez, M. A. Martínez, and A. Mandow, “PurePursuit Reactive Path Tracking for Nonholonomic Mobile Robots with a 2D Laser Scanner,” EURASIP J. Adv. Signal Process., vol. 2009, no. 1, p. 935237, 2009, doi: 10.1155/2009/935237.
  • [21] Z. Wang, Y. Bai, J. Wang, and X. Wang, “Vehicle Path-Tracking Linear-Time-Varying Model Predictive Control Controller Parameter Selection Considering Central Process Unit Computational Load,” J. Dyn. Syst. Meas. Control, vol. 141, no. 5, 2019, doi: 10.1115/1.4042196.
  • [22] Q. Yao and Y. Tian, “A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking,” Appl. Sci., vol. 9, no. 22, 2019, doi: 10.3390/app9224739.
  • [23] R. Marino, S. Scalzi, and M. Netto, “Nested PID steering control for lane keeping in autonomous vehicles,” Control Eng. Pract., vol. 19, no. 12, pp. 1459–1467, 2011, doi: https://doi.org/10.1016/j.conengprac.2011.08.005.
  • [24] A. Bemporad, M. Morari, V. Dua, and E. N. Pistikopoulos, “The explicit linear quadratic regulator for constrained systems,” Automatica, vol. 38, no. 1, pp. 3–20, 2002, doi: https://doi.org/10.1016/S0005-1098(01)00174-1.
  • [25] G. V Raffo, G. K. Gomes, J. E. Normey-Rico, C. R. Kelber, and L. B. Becker, “A Predictive Controller for Autonomous Vehicle Path Tracking,” IEEE Trans. Intell. Transp. Syst., vol. 10, no. 1, pp. 92– 102, 2009, doi: 10.1109/TITS.2008.2011697.