Path Planning for Mobile Robots Based on Optimal Interaction of Electrostatic Fields

Resumen: This article presents a concise review of interaction laws between electrostatic charges applied to path planning for a mobile robot. %% The robot is represented as a punctual charge, moving freely in a predefined workspace or chart. %% The proposed methodology must help to estimate the free trajectory to reach a pre-established goal. %% The electrostatic interactions between the multiple elements of the environment (obstacles) and robot allow to dynamically determine a smooth trajectory, using minimum energy. %% Besides, the study includes the performance evaluation of different robust optimization techniques to solve the electrostatic charges interaction problem, estimating a fast and stable trajectory. %% The robot navigation is simulated, including the position or speed control parameters to improve displacement with a smoothed trajectory. % In this way, the robot can evolve in complex environments as long as it disposes of the appropriate sensory information, even in dynamic environments.

¿Cómo citar?

Daniel Fernando Zambrano-Gutierrez, Emmanuel Ovalle-Magallanes, Jesus Joaquin Yanez-Borjas, Horacio Rostro-Gonzalez & Juan Gabriel Avina-Cervantes. Path Planning for Mobile Robots Based on Optimal Interaction of Electrostatic Fields. Memorias del Congreso Nacional de Control Automático, pp. 115-120, 2021.

Palabras clave

Path planning, Electrical potential, Optimization methods, Levenberg-Marquardt, Dog-Leg Algorithm

• Alajlan, M., Koubaa, A., Chaari, I., Bennaceur, H., and Ammar, A. (2013). Global path planning for mobile robots in large-scale grid environments using genetic algorithms. In 2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR), 1–8. IEEE, Sousse, Tunisia. doi: 10.1109/ICBR.2013.6729271.
• Chauhan, V.K., Dahiya, K., and Sharma, A. (2018). Trust Region Levenberg-Marquardt Method for Linear SVM. In 2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017, 380–385. IEEE, India. doi:10.1109/ICAPR.2017.8593090.
• Choset, H.M., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L., Thrun, S., and Arkin, R.C. (2005). Principles of robot motion: theory, algorithms, and implementation. MIT press.
• Da Graca Marcos, M., Tenreiro Machado, J.A., and Azevedo-Perdico´ulis, T.P. (2009). Trajectory planning of redundant manipulators using genetic algorithms. Communications in Nonlinear Science and Numerical Simulation, 14(7), 2858–2869. doi: 10.1016/j.cnsns.2008.10.014.
• Das, P.K. and Jena, P.K. (2020). Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators. Applied Soft Computing, 92, 106312. doi: 10.1016/j.asoc.2020.106312.
• Evans, J., Krishnamurthy, B., Pong, W., Croston, R., Weiman, C., and Engelberger, G. (1989). HelpMate™: A robotic materials transport system. Robotics and Autonomous Systems, 5(3), 251–256. doi:10.1016/0921- 8890(89)90049-3.
• Gayle, R., Moss, W., Lin, M.C., and Manocha, D. (2009). Multi-robot coordination using generalized social potential fields. In 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12-17, 2009, 106–113. IEEE. doi: 10.1109/ROBOT.2009.5152765.
• Gong, W. (2021). Probabilistic model based path planning. Physica A: Statistical Mechanics and its Applications, 568, 125718. doi:10.1016/j.physa.2020.125718.
• Ha, J.S., Park, S.S., and Choi, H.L. (2019a). Topologyguided path integral approach for stochastic optimal control in cluttered environment. Robotics and Autonomous Systems, 113, 81–93. doi: 10.1016/j.robot.2019.01.001.
• Ha, Q.P., Yen, L., and Balaguer, C. (2019b). Robotic autonomous systems for earthmoving in military applications. Automation in Construction, 107, 102934. doi:10.1016/j.autcon.2019.102934.
• Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In Proceedings. 1985 IEEE International Conference on Robotics and Automation, volume 2, 500–505. China. doi: 10.1109/ROBOT.1985.1087247.
• Lee, M.C. and Park, M.G. (2003). Artificial potential field based path planning for mobile robots using a virtual obstacle concept. In Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), volume 2, 735–740. Delft, Delft, The Netherlands. doi:10.1109/AIM.2003.1225434.
• Marquardt, D.W. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. doi:10.1137/0111030. Publisher: Society for Industrial and Applied Mathematics.
• Sang, H., You, Y., Sun, X., Zhou, Y., and Liu, F. (2021). The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations. Ocean Engineering, 223, 108709. doi:10.1016/j.oceaneng.2021.108709.
• Seyyedhasani, H., Peng, C., Jang, W.J., and Vougioukas, S.G. (2020). Collaboration of human pickers and crop-transporting robots during harvesting – Part I: Model and simulator development. Computers and Electronics in Agriculture, 172, 105324. doi: 10.1016/j.compag.2020.105324.