Toward Online Probabilistic Path Replanning in Dynamic Environments

This paper presents work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and modeling are combined with a smooth navigation function to perform on-line path planning in cluttered dynamic environments. The SLIP algorithm, an extension of Iterative Closest Point, combines motion detection from a mobile platform with position estimation. This information is used via probabilistic prediction to estimate a traversal risk function that unifies dynamic and static obstacles. The risk is fed to E* and leads to smooth paths that trade off collision risk versus detours.