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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]
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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 

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