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

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, 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 worstcase analysis. The minimum distance to a moving obstacle is considered as the objective function in the automatic search process. It is demonstrated that a local nonlinear optimization method may be not adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches. Keywords— Collision avoidance, Optimization, Potential field method, Monte Carlo simulation, Robustness analysis.