Steering Velocity Bounded Mobile Robots in Environments with Partially Known Obstacles

Authors: Reza Hossseynie, Amir Jafari

Abstract:

This paper presents a method for steering velocity bounded mobile robots in environments with partially known stationary obstacles. The exact location of obstacles is unknown and only a probability distribution associated with the location of the obstacles is known. Kinematic model of a 2-wheeled differential drive robot is used as the model of mobile robot. The presented control strategy uses the Artificial Potential Field (APF) method for devising a desired direction of movement for the robot at each instant of time while the Constrained Directions Control (CDC) uses the generated direction to produce the control signals required for steering the robot. The location of each obstacle is considered to be the mean value of the 2D probability distribution and similarly, the magnitude of the electric charge in the APF is set as the trace of covariance matrix of the location probability distribution. The method not only captures the challenges of planning the path (i.e. probabilistic nature of the location of unknown obstacles), but it also addresses the output saturation which is considered to be an important issue from the control perspective. Moreover, velocity of the robot can be controlled during the steering. For example, the velocity of robot can be reduced in close vicinity of obstacles and target to ensure safety. Finally, the control strategy is simulated for different scenarios to show how the method can be put into practice.

Keywords: Steering, obstacle avoidance, mobile robots, constrained directions control, artificial potential field.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339598

References:

[1] B. Siciliano, and O. Khatib, eds. Springer handbook of robotics. Springer Science & Business Media, 2008.
[2] O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” International of Robotics Research, vol. 5, no. 1, pp. 90-98, Spring 1986.
[3] C. Tingbin and Z. Qisong, "Robot motion planning based on improved artificial potential field," Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on, Dalian, 2013, pp. 1208-1211
[4] Hussien, B. 1989. Robot path planning and obstacle avoidance by means of potential function method. Ph.D Dissertation, University of Missouri-Columbia.
[5] C. C. Kao, C. M. Lin and J. G. Juang, "Application of potential field method and optimal path planning to mobile robot control," 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, 2015, pp. 1552-1554.
[6] P. Vadakkepat, Kay Chen Tan and Wang Ming-Liang, "Evolutionary artificial potential fields and their application in real time robot path planning," Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, La Jolla, CA, 2000, pp. 256-263 vol.1.
[7] Abdalla, Turki Y., Ali A. Abed, and Alaa A. Ahmed. "Mobile robot navigation using PSO-optimized fuzzy artificial potential field with fuzzy control." Journal of Intelligent & Fuzzy Systems Preprint: 1-16.
[8] S. Khanmohammadi, R. Soltani-Zarrin, "Intelligent Path Planning For Rescue Robot", World Academy of Science, Engineering, and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol.5, no.7, 2011, pp. 839-844.
[9] Vadakkepat, Prahlad, Kay Chen Tan, and Wang Ming-Liang. "Evolutionary artificial potential fields and their application in real time robot path planning." Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. Vol. 1. IEEE, 2000.
[10] Kovács, Bence, et al. "A novel potential field method for path planning of mobile robots by adapting animal motion attributes." Robotics and Autonomous Systems 82 (2016): 24-34.
[11] Lijuan Xie, Huanwen Chen, Guangrong Xie, “Artificial Potential Field Based Path Planning for Mobile Robots Using Virtual Water-Flow Method”, Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, vol. 2 of the series Communications in Computer and Information Science, 2007, pp. 588-595.
[12] D. Chwa, "Tracking Control of Differential-Drive Wheeled Mobile Robots Using a Backstepping-Like Feedback Linearization," in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 40, no. 6, pp. 1285-1295, Nov. 2010.
[13] Shouling He, "Feedback control design of differential-drive wheeled mobile robots," ICAR '05. Proceedings. 12th International Conference on Advanced Robotics, 2005, Seattle, WA, 2005, pp. 135-140.
[14] K. Shojaei, A. M. Shahri, A. Tarakameh, and B. Tabibian, "Adaptive trajectory tracking control of a differential drive wheeled mobile robot," Robotica, vol. 29, pp. 391-402, 2011.
[15] K. Shojaei and A. Shahri, "Adaptive robust time-varying control of uncertain non-holonomic robotic systems," IET Control Theory & Applications, vol. 6, pp. 90-102, 2012.
[16] A. K. Khalaji and S. A. A. Moosavian, "Adaptive sliding mode control of a wheeled mobile robot towing a trailer," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 229, pp. 169-183, 2015.
[17] A. Zeiaee, R. Soltani-Zarrin and R. Langari, "A novel approach for tracking control of differential drive robots subject to hard input constraints," 2016 American Control Conference, Boston, 2016, pp. 2098-2103.
[18] Indiveri, Giovanni, Andreas Nuchter, and Kai Lingemann. "High speed differential drive mobile robot path following control with bounded wheel speed commands." In Proceedings IEEE International Conference on Robotics and Automation, 2007, pp. 2202-2207.
[19] R. Soltani-Zarrin and S. Jayasuriya, "Constrained directions as a path planning algorithm for mobile robots under slip and actuator limitations", 2014 IEEE/RSJ International Conf. on Intelligent Robots and Systems, Chicago, 2014, pp. 2395-2400.
[20] R. Soltani-Zarrin, A. Zeiaee, and S. Jayasuriya,” Pointwise Angle Minimization: A Method for Guiding Wheeled Robots Based on Constrained Directions”, in Proc. ASME 2014 Dynamic Systems and Control Conference, San Antonio, 2014, pp. V003T48A004- V003T48A004.
[21] Sankaranarayanan, Velupillai, and Arun D. Mahindrakar. "Configuration constrained stabilization of a wheeled mobile robot—theory and experiment." IEEE Transactions on Control Systems Technology 21, no. 1, 2013, pp. 275-280.
[22] Jiang, Z.P., Lefeber, E. and Nijmeijer, H., Saturated stabilization and tracking of a nonholonomic mobile robot. Systems & Control Letters, 42(5), pp.327-332, 2001.
[23] A. Zeiaee, R. Soltani-Zarrin, S. Jayasuriya, and R. Langari, "A Uniform Control for Tracking and Point Stabilization of Differential Drive Robots Subject to Hard Input Constraints," in Proc. ASME 2015 Dynamic Systems and Control Conference, Columbus, 2015, pp. V001T04A005-V001T04A005.
[24] Y. Koren and J. Borenstein, “Potential field methods and their inherent limitations for mobile robot navigation,” Proc. IEEE Conf. Robotics and Automation, Sacramento, 1991, pp. 1398–1404.
[25] M. Spong, S. Hutchinson, M. Vidyasagar, “Robot modeling and control”, John Wiley 2006.
[26] Ge, Shuzhi Sam, and Yan Juan Cui. "New potential functions for mobile robot path planning." IEEE Transactions on robotics and automation 16.5, 2000, pp. 615-620.