An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility

TL;DR

This paper introduces a straightforward, incremental sampling-based method for detecting motion planning infeasibility by discretizing the configuration space and analyzing connected components to determine if a path exists.

Contribution

The authors propose a novel incremental sampling and segmentation approach that efficiently detects plan infeasibility in kinematic motion planning.

Findings

Effective in scenarios with up to 5 DOF

Accurately identifies disconnected start and goal configurations

Simple implementation with incremental updates

Abstract

We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot's configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into different connected regions. For a path to exist between the start and goal configurations, they must lie in the same connected region of the free space. Thus, to ascertain plan infeasibility, we merely need to sample adequate points from the obstacle region that isolate start and goal. Accordingly, we progressively construct the configuration space by sampling from the discretized space and updating the bitmap cells representing obstacle regions. Subsequently, we partition this partially built configuration space to identify different connected components within it and assess the connectivity of the start and goal cells. We illustrate this methodology on five different scenarios with configuration spaces having up to 5 degree-of-freedom (DOF).