Sivaranjini Srikanthakumar_ROBOTICA_2014.pdf (595.47 kB)
Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles
journal contribution
posted on 2015-07-24, 13:46 authored by Sivaranjini Srikanthakumar, Wen-Hua ChenWen-Hua ChenThis 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
RoboticaVolume
33Issue
4Pages
807 - 827Citation
SRIKANTHAKUMAR, S. and CHEN, W-H, 2015. Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles. Robotica, 33 (4), pp. 807 - 827Publisher
© Cambridge University PressVersion
- 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
2015Notes
This article was published in the journal Robotica [© Cambridge University Press] and the definitive version is available at: http://dx.doi.org/10.1017/S0263574714000642ISSN
0263-5747eISSN
1469-8668Publisher version
Other identifier
S0263574714000642Language
- en