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Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots

Abstract

This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.

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References

  1. F. Fahimi, C. Nataraj, H. Ashrafiuon. Real-time obstacle avoidance for multiple mobile robots. Robotica, vol. 27, no. 2, pp. 189–198, 2009.

    Article  Google Scholar 

  2. O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, vol. 5, no. 1, pp. 90–99, 1986.

    MathSciNet  Article  Google Scholar 

  3. J. Velagic, B. Lacevic, N. Osmic. Efficient path planning algorithm for mobile robot navigation with a local minima problem solving. In Proceedings of IEEE International Conference on Industrial Technology, IEEE, Mumbai, pp. 2325–2330, 2006.

    Chapter  Google Scholar 

  4. S. S. Ge, Y. J. Cui. Dynamic motion planning for mobile robots using potential filed method. Autonomous Robots, vol. 13, no. 3, pp. 207–222, 2002.

    MATH  Article  Google Scholar 

  5. E. C. Silva, E. Bicho, W. Erlhagen. The potential field method and the nonlinear attractor dynamics approach: What are the difference? In Proceedings of the 7th Portuguese Conference on Automatic Control, Lisboa, Portugal, 2006.

  6. C. Fielding, A. Varga, S. Bennai, M. Selier. Advanced techniques for Clearance of Flight Control Laws, Germany: Springer, 2002.

    MATH  Book  Google Scholar 

  7. K. K. James. Safety analysis methodology for unmanned aerial vehicle (UAV) collision avoidance systems. MIT Lincoln Laboratory, Lexington, MA, paper number #F19628-00-C-0002.

  8. J. Wang, X. Wu, Z. Xu. Potential-based obstacle avoidance in formation control. Journal of Control Theory and Applications, vol. 6, no. 3, pp. 311–316, 2008.

    MathSciNet  Article  Google Scholar 

  9. V. Gazi, B. Fidan, Y. S. Hanay, M. I. Koksal. Aggregation, foraging, and formation control of swarms with nonholonomic agent using potential functions and sliding mode techniques. Turkish Journal of Electrical Engineering and Computer Science, vol. 15, no. 2, pp. 149–168, 2007.

    Google Scholar 

  10. R. Carona, A. P. Aguiar, J. Gaspar. Control of unicycle type robots tracking, path following and point stabilization. In Proceedings of International Conference of IV Electronics and Telecommunications, Lisbon, Portugal, pp. 180–185, 2008.

  11. A. D. Luca, G. Oriolo. Local incremental planning for nonholonomic mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, San Diego, USA, pp. 104–110, 1994.

  12. A. Bemporad, A. D. Luca, G. Oriolo. Local incremental planning for a car-like robot navigating among obstacles. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Minneapolis, USA, pp. 1205–1211, 1996.

    Google Scholar 

  13. M. I. Ribeiro. Obstacle avoidance. Institute for Systems and Robotics, [Online], Available: http://users.isr.ist.utl.pt/~mir/, March 5, 2011.

  14. J. Yan, X. P. Guan, F. X. Tan. Target tracking and obstacle avoidance for multi-agent systems. International Journal of Automation and Computing, vol. 7, no. 4, pp. 550–556, 2010.

    Article  Google Scholar 

  15. MATLAB, [Online], Available: http://www.mathworks.com, March 5, 2011.

  16. J. Holland. Adaptation in Natural and Artificial Systems, Ann Arbor, USA: University of Michigan Press, 1975.

    Google Scholar 

  17. D. Bates, M. Hagstrom. Nonlinear Analysis and Synthesis Techniques for Aircraft Control, pp. 259–300, Germany: Springer, 2007.

    MATH  Book  Google Scholar 

  18. J. S. Arora. Introduction to Optimum Design, 2nd ed., USA: Elsevier Academic Press, 2004.

    Google Scholar 

  19. F. G. Zhao, J. S. Sun, S. J. Li, W. M. Liu. A hybrid genetic algorithm for the traveling salesman problem with pickup and delivery. International Journal of Automation and Computing, vol. 6, no. 1, pp. 97–102, 2009.

    MathSciNet  Article  Google Scholar 

  20. W. M. Rand. Controlled Observations of the Genetic Algorithm in a Changing Environment: Case Studies Using the Shaky Ladder Hyperplane-defined Functions, Ph.D. dissertation, The University of Michigan, USA, 2005.

    Google Scholar 

  21. T. Csendes, L. Pal, J. O. H. Sendin, J. R. Bang. The GLOBAL optimisation method revisited. Optimization Letters, vol. 2, no. 4, pp. 445–454, 2008.

    MathSciNet  MATH  Article  Google Scholar 

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Correspondence to Sivaranjini Srikanthakumar.

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Sivaranjini Srikanthakumar received her B.Eng. in computer systems engineering from Electronics and Electrical Engineering Department, Loughborough University, UK in 2007. She is currently a Ph. D. candidate in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK.

Her research interests include autonomous vehicles verification, optimisation-based clearance process, collision avoidance algorithm, path planning, global optimisation methods, and flight control laws.

Wen-Hua Chen received the M. Sc. and Ph.D. degrees from the Department of Automatic Control at Northeast University, PRC in 1989 and 1991, respectively. From 1991 to 1996, he was a lecturer in the Department of Automatic Control at Nanjing University of Aeronautics and Astronautics, PRC. He held a research position and then a lectureship in control engineering at the Center for Systems and Control at University of Glasgow, UK from 1997 to 2000. He currently holds a senior lectureship in flight control systems in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK.

His research interests include the development of advanced control strategies and their applications in aerospace engineering.

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Srikanthakumar, S., Chen, WH. Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots. Int. J. Autom. Comput. 8, 340 (2011). https://doi.org/10.1007/s11633-011-0590-4

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  • DOI: https://doi.org/10.1007/s11633-011-0590-4

Keywords

  • Verification process
  • obstacle avoidance
  • unicycle mobile robot
  • potential field method
  • optimisation