Coping with uncertainty in control and planning for a mobile robot

Describes a decision theoretic approach to real-time obstacle avoidance and path planning for a mobile robot. The mobile robot navigates in a semi-structured environment in which unexpected obstacles may appear at random locations. Twelve sonar sensors are currently used to report the presence and location of the obstacles. To handle the uncertainty of an obstacle's appearance, the authors adopt a Bayesian approach by assuming a prior distribution for the presence of unknown obstacles. The distribution is changed dynamically according to the information accumulated by sensors. When searching for an optimal path using dynamic programming, the authors take the probability into account in making a decision. Based on prior information and sensor data, they show that the proposed method allows the mobile robot to avoid unexpected obstacles and finds an optimal path to the goal in real time.<<ETX>>