Landmark-based safe path planning for car-like robots

Abstract

Addresses path planning with uncertainty for a car-like robot subject to configuration uncertainty. The robot estimates its configuration 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 configuration space, (2) the 'best' features for localization in this region, and (3) a perception uncertainty field that measures how well a feature is perceived at each configuration in the region. The landmarks are used within an efficient roadmap-based path planning algorithm that returns a safe motion plan that alternates motion along safe paths and localization, operations.