Abstract
An intelligent motion planning based on fuzzy rules for the idea of artificial potential fields using analytic harmonic functions is presented. The purpose of the combination of a fuzzy controller and a robust controller is to design a realistic controller for nonlinear electromechanical systems such as an electric motor actuating an arm robot. This control algorithm is applied to the three basic navigation problems of intelligent robot systems in unstructured environments: autonomous planning, fast nonstop navigation without collision with obstacles, and dealing with structured and/or unstructured uncertainties. To achieve this degree of independence, the robot system needs a variety of sensors to be able to interact with the real world. Sonar range data is used to build a description of the robot's surroundings. The proposed approach is simple, computationally fast, and applies to whole-arm collision avoidance. The stability of the overall closed loop system is guaranteed by the Lyapunov theory. Simulation results are provided to validate the theoretical concepts, and a comparative analysis demonstrates the benefits of the proposed obstacle avoidance algorithm.
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References
Althöfer, K. and Fraser, D. A.: Fuzzy obstacle avoidance for robotic manipulators, Neural Network World 6(2) (1996), 133–142.
Althöfer, K., Seneviratne, I. D., Zavlangas, P., and Krekelberg, B.: Fuzzy navigation for robotic manipulators, Internat. J. Uncertainty Fuzziness Knowledge-based Systems 6(2) (1998), 179–188.
Berghuis, H.: Model-based robot control: From theory to practice, PhD Thesis, Department of Electrical Engineering, University of Twente, Enschede, The Netherlands, 1993.
Cheng, G. and Zhang, D.: Back-driving a truck with suboptimal distance trajectories: A fuzzy logic control approach, IEEE Trans. Fuzzy Systems 5(1997), 369–380.
Cheung, E. and Lumelsky, V. J.: A sensitive skin system for motion control of robot arm manipulators, J. Robotics Autonom. Systems 10(1992), 9–32.
Cheung, E. and Lumelsky, V. J.: Real time path planning procedure for whole-sensitive robot arm manipulator, Robotica 10(1992), 339–349.
Connolly, C. I.: Harmonic functions as a basis for motor control and planning, PhD Dissertation, Department of Computer Science, University of Massachusetts Amherst, MA, USA, 1994.
Corke, P. I.: Visual control of robot manipulators-A review, in: K. Hashimoto (ed.), Visual Servoing, World Scientific, Singapore, 1993, pp. 1–32.
Dawson, D. M., Qu, Z., and Carrol, J. J.: Tracking control of rigid-link electrically-driven robot manipulators, Internat. J. Control 56(5) (1992), 991–1006.
Ding, H. and Li, H. X.: Fuzzy avoidance control strategy for redundant manipulators, Engrg. Appl. Artificial Intelligence 12(1999), 513–521.
Hutchinson, S., Hager, G. D., and Corke, P. I.: A tutorial on visual servo control, IEEE Trans. Robotics Automat. 12(5) (1996), 338–349.
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots, Internat. J. Robotics Res. 5(1986), 90–98.
Kim, J.-O. and Khosla, P. K.: Real-time obstacle avoidance using harmonic potential functions, IEEE Trans. Robotics Automat. 8(3) (1992), 338–349.
Kwan, C., Lewis, F. L., and Dawson, D. M.: Robust neural-network control of rigid-link electrically driven robots, IEEE Trans. Neural Networks 9(4) (1998), 581–588.
Lumelsky V. J. and Cheung, E.: Real-time collision avoidance in teleoperated whole-sensitive robot arm manipulators, IEEE Trans. Systems Man Cybernet. 23(1) (1993), 194–203.
Mamdani, E. H. and Assilian, S.: Applications of fuzzy algorithms for control of simple dynamic plant, in: Proc. IEE 121(1974), 1585–1588.
Murray, R. M., Li, Z., and Sastry, S. S.: A Mathematical Introduction to Robotic Manipulation, CRC Press, Boca Raton, FL, 1994.
Ohya, A., Kosaka, A., and Kak, A.: Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing, IEEE Trans. Robotics Automat.14(6) (1998), 338–349.
Peiper, D. L.: The kinematics of manipulators under computer control, PhD Dissertation, Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, Stanford, CA, 1968.
Qu, Z. and Dawson, D. M.: Robust Tracking Control of Robot Manipulators, IEEE Press, New York, 1996.
Takagi, T. and Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems Man Cybernet. 15(1) (1985), 116–132.
Tarn, T.-J., Bejczy, A. K., Yun, X., and Li, Z.: Effect of motor dynamics on nonlinear feedback robot arm control, IEEE Trans. Robotics Automat. 7(1) (1991), 114–122.
Taylor, D.: Composite control of direct-drive robots, in: Proc. of IEEE Internat. Conf. on Decision and Control, 1989, pp. 1670–1675.
Tsoukalas, L. H., Houstis, E. N., and Jones, G. V.: Neuro-fuzzy motion planner for intelligent robots, J. Intelligent Robotic Systems 19(1997), 339–356.
Wang, M., Huang, X., and Hu, J.: An integrated proximity sensor research, J. of Huazhong University of Science and Technology 23(9) (1995), 33–36 (in Chinese).
Zavlangas, P., Tzafestas, S., and Althoefer, K.: Navigation for robotic manipulators employing fuzzy logic, in: Proc. of the 3rd World Conf. on Integrated Design and Process Technology, Vol. 6, Berlin, Germany, July 6–9, 1998, pp. 278–283.
Zhang, D., Chen, G., and Malki, H. A.: Fuzzy-logic control of multi-link flexible-joint robotic manipulators, Internat. J. Intelligent Control Systems 2(1) (1998), 111–138.
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Wei, W., Mbede, J.B. & Zhang, Q. Fuzzy Sensor-Based Motion Control among Dynamic Obstacles for Intelligent Rigid-Link Electrically Driven Arm Manipulators. Journal of Intelligent and Robotic Systems 30, 49–71 (2001). https://doi.org/10.1023/A:1008190612246
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DOI: https://doi.org/10.1023/A:1008190612246