Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T23:25:26.840Z Has data issue: false hasContentIssue false
  • English
  • Français

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Published online by Cambridge University Press:  15 April 2014

Sivaranjini Srikanthakumar  and
Wen-Hua Chen
Sivaranjini Srikanthakumar*
Affiliation:
Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU, UK
Wen-Hua Chen
Affiliation:
Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire LE11 3TU, UK
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

Keywords

Collision avoidanceOptimizationPotential field methodMonte Carlo simulationRobustness analysis
Type
Articles
Information
Robotica , Volume 33 , Issue 4 , May 2015 , pp. 807 - 827
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential filed method,” Auton. Robots 13, 207–222 (2002).CrossRefGoogle Scholar
2. Raja, P. and Pugazhenthi, S., “Path planning for a mobile robot in dynamic environments,” Int. J. Phys. Sci. 6 (20), 4721–4731 (2011).Google Scholar
3. Park, J. W., Oh, H. D. and Tahk, M. J., “UAV Collision Avoidance Based on Geometric Approach,” SICE Annual Conference, The University of Elctro-Communications, Tokyo, Japan (Aug. 2008) pp. 2122–2126.Google Scholar
4. Masehian, E. and Sedighizadeh, D., “Classic and Heuristic Approaches in Robot Motion Planning – A Chronological Review,” Proceedings of World Academy of Science, Engineering and Technology (Aug. 2007) pp. 101–106.Google Scholar
5. Kuchar, J. K., “Safety Analysis Methodology for Unmanned Aerial Vehicle (UAV) Collision Avoidance Systems,” 6th USA/Europe Seminar on Air Traffic Management Research and Development, Baltimore, MD (June, 2005) (MIT Lincoln Laboratory, Lexington, MA, United States Air Force, #F19628-00-C-0002).Google Scholar
6. Althoff, M., Stursberg, O. and Buss, M., “Online Verification of Cognitive Car Decisions,” Proceedings of the IEEE Intelligent Vehicles Symposium, Istanbul, Turkey (June, 2007) pp. 728–733.Google Scholar
7. Fraichard, T., “A Short Paper About Motion Safety,” IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 2007) pp. 1140–1145.Google Scholar
8. Luongo, S., Corraro, F., Ciniglio, U. and Di Vito, V., “A Novel 3D Analytical Algorithm for Autonomous Collision Avoidance Considering Cylindrical Safety Bubble,” Proceedings of the IEEE Aerospace Conference, Big Sky, MT (6–13 Mar. 2010) pp. 1–13.Google Scholar
9. De La Cruz, C. and Carelli, R., “Dynamic Modeling and Centralized Formation Control Of Mobile Robots,” Proceedings of the 32nd Annual Conference on IEEE Industrial Electronics (IECON'06), Paris, France (Nov. 2006) pp. 3880–3885.Google Scholar
10. Srikanthakumar, S., Liu, C. and Chen, W. H., “Optimization-based safety analysis of obstacles avoidance systems for unmanned aerial vehicles,” J. Intell. Robot. Syst. 65 (1–4), 219–231 (Jan. 2012).Google Scholar
11. Luca, A. D. and Oriolo, G., “Local Incremental Planning for Nonholonomic Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, San Diego, CA (8–13 May 1994) pp. 104–110.Google Scholar
12. Holland, J., Adaption in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence (University of Michigan Press, Ann Arbor, MI, 1975).Google Scholar
13. Bates, D. and Hagstrom, M., Nonlinear Analysis and Synthesis Techniques for Aircraft Control LNCIS, Vol. 365 (Springer, New York, NY, 2007) pp. 259–300.Google Scholar
14. Arora, J. S., Introduction to Optimum Design 2nd edn. (Elsevier, San Diego, CA, 2004).CrossRefGoogle Scholar
15. Rand, W. M., “Controlled Observations of the Genetic Algorithm in a Changing Environment: Case Studies Using the Shaky Ladder Hyperplane-Defined Functions,” PhD Thesis, The University of Michigan, 2005.Google Scholar
16. Csendes, T., Pal, L., Sendin, J. O. H. and Banga, J. R., “The GLOBAL optimization method revisited,” Optim. Lett. 2 (4), 445–454 (2008).CrossRefGoogle Scholar
17. Boender, C. G. E., Rinnooy Kan, A. H. G., Timmer, G. T. and Stougie, L., “A stochastic method for global optimization,” Math. Program. 22, 125–140 (1982).CrossRefGoogle Scholar
18. Jones, D. R., “DIRECT global optimization algorithm,” In: Encyclopedia of Optimization (Floudas, C. and Pardalos, P., eds.) (Kluwer, Dordrecht, Netherlands, 2001) pp. 431–440.CrossRefGoogle Scholar
19. Finkel, D. E. and Kelley, C. T., Convergence Analysis of the Direct Algorithm (Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, Jul. 2004).Google Scholar
20. Finkel, D. E., DIRECT Optimization Algorithm User Guide (Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, Mar. 2003).Google Scholar
21. ISO/CEI, GUIDE 98-3/SUPP. 1, Uncertainty of Measurement Part 3/Supplement 1: Propagations of Distributions Using a Monte Carlo Method (ISO/CEI, Switzerland, 2008).Google Scholar