Passage-traversing optimal path planning with sampling-based algorithms

TL;DR

This paper presents PTOPP, a novel sampling-based path planning paradigm that optimizes paths based on traversed passages to maximize accessible free space, with improved efficiency and configurability over existing methods.

Contribution

The paper introduces PTOPP, a new framework for optimal path planning focusing on passage traversal and free space optimization, including a novel passage detection method and compatible algorithms.

Findings

PTOPP outperforms clearance-based methods in solution optimality.

PTOPP demonstrates higher efficiency in environment decompositions.

PTOPP offers greater configurability for free space optimization.

Abstract

This paper introduces a new paradigm of optimal path planning, i.e., passage-traversing optimal path planning (PTOPP), that optimizes paths' traversed passages for specified optimization objectives. In particular, PTOPP is utilized to find the path with optimal accessible free space along its entire length, which represents a basic requirement for paths in robotics. As passages are places where free space shrinks and becomes constrained, the core idea is to leverage the path's passage traversal status to characterize its accessible free space comprehensively. To this end, a novel passage detection and free space decomposition method using proximity graphs is proposed, enabling fast detection of sparse but informative passages and environment decompositions. Based on this preprocessing, optimal path planning with accessible free space objectives or constraints is formulated as PTOPP problems compatible with sampling-based optimal planners. Then, sampling-based algorithms for PTOPP, including their dependent primitive procedures, are developed leveraging partitioned environments for fast passage traversal check. All these methods are implemented and thoroughly tested for effectiveness and efficiency validation. Compared to existing approaches, such as clearance-based methods, PTOPP demonstrates significant advantages in configurability, solution optimality, and efficiency, addressing prior limitations and incapabilities. It is believed to provide an efficient and versatile solution to accessible free space optimization over conventional avenues and more generally, to a broad class of path planning problems that can be formulated as PTOPP.