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

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journal contribution
posted on 24.07.2015, 13:46 by Sivaranjini Srikanthakumar, Wen-Hua ChenWen-Hua Chen
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.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Robotica

Volume

33

Issue

4

Pages

807 - 827

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

Publisher

© Cambridge University Press

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2015

Notes

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

ISSN

0263-5747

eISSN

1469-8668

Other identifier

S0263574714000642

Language

en