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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18328

Title: Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles
Authors: Srikanthakumar, Sivaranjini
Chen, Wen-Hua
Keywords: Collision avoidance
Monte Carlo simulation
Potential field method
Robustness analysis
Issue Date: 2015
Publisher: © Cambridge University Press
Citation: SRIKANTHAKUMAR, S. and CHEN, W-H, 2015. Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles. Robotica, 33 (4), pp. 807 - 827
Abstract: 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.
Description: This article was published in the journal Robotica [© Cambridge University Press] and the definitive version is available at: http://dx.doi.org/10.1017/S0263574714000642
Version: Accepted for publication
DOI: 10.1017/S0263574714000642
URI: https://dspace.lboro.ac.uk/2134/18328
Publisher Link: http://dx.doi.org/10.1017/S0263574714000642
ISSN: 0263-5747
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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