Planning Safe Paths for Nonholonomic Car - Like RobotsNavigating Through Computed

This paper addresses path planning with uncertainty for a car-like robot subject to connguration uncertainty. The robot estimates its connguration with odometry and an absolute localization device based on environmental feature matching. The issue is to compute safe paths that guarantee that the goal will be reached in spite of the uncertainty. The solution proposed relies upon the automatic construction of a set of landmarks characterized by (1) a region of the connguration space, (2) thèbest' features for localization in this region, and (3) a perception uncertainty eld that measures how well a feature is perceived at each connguration in the region. The landmarks are used within an eecient roadmap-based path planning algorithm that returns a safe motion plan that alternates motion along safe paths and localization operations. Abstract This paper addresses path planning with uncertainty for a car-like robot subject to connguration uncertainty. The robot estimates its connguration with odometry and an absolute localization device based on environmental feature matching. The issue is to compute safe paths that guarantee that the goal will be reached in spite of the uncertainty. The solution proposed relies upon the automatic construction of a set of landmarks characterized by (1) a region of the connguration space, (2) thèbest' features for localization in this region, and (3) a perception uncertainty eld that measures how well a feature is perceived at each connguration in the region. The landmarks are used within an eecient roadmap-based path planning algorithm that returns a safe motion plan that alternates motion along safe paths and localization operations.