Multi-Objective Path-Based D* Lite

TL;DR

This paper introduces MOPBD*, an incremental multi-objective path planning algorithm that efficiently finds Pareto-optimal solutions by pruning dominated paths, significantly outperforming existing methods in speed.

Contribution

The paper proposes a novel path-based incremental search algorithm for multi-objective path planning, including a sub-optimal variant for improved efficiency.

Findings

MOPBD* outperforms search-from-scratch methods in efficiency.

The sub-optimal variant approximates Pareto fronts effectively.

Algorithm runs up to ten times faster than existing incremental methods.

Abstract

Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks. In this article, we are interested in developing incremental graph search algorithms for path finding problems to simultaneously optimize multiple objectives such as travel risk, arrival time, etc. This is challenging because in a multi-objective setting, the number of "Pareto-optimal" solutions can grow exponentially with respect to the size of the graph. This article presents a new multi-objective incremental search algorithm called Multi-Objective Path-Based D* Lite (MOPBD*) which leverages a path-based expansion strategy to prune dominated solutions. Additionally, we introduce a sub-optimal variant of MOPBD* to improve search efficiency while approximating the Pareto-optimal front. We numerically evaluate the performance of MOPBD* and its variants in various maps with two and three objectives. Results show that our approach is more efficient than search from scratch, and runs up to an order of magnitude faster than the existing incremental method for multi-objective path planning.