Heuristic Search for Path Finding with Refuelling

TL;DR

This paper introduces RF-A* a heuristic search algorithm for the Gas Station Problem, optimizing robot path planning with refuelling constraints, and demonstrates its efficiency and optimality in large-scale maps.

Contribution

It develops a novel heuristic search method that efficiently solves the GSP by combining heuristic guidance with dominance-based pruning, ensuring optimal solutions.

Findings

RF-A* runs 2 to 8 times faster than existing methods.

The algorithm guarantees finding an optimal solution.

Effective in large city maps with hundreds of gas stations.

Abstract

This paper considers a generalization of the Path Finding (PF) problem with refuelling constraints referred to as the Gas Station Problem (GSP). Similar to PF, given a graph where vertices are gas stations with known fuel prices, and edge costs are the gas consumption between the two vertices, GSP seeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While GSP is polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since it requires simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This paper develops a heuristic search algorithm called Refuel A$^*$ (RF-A$^*$) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic while leveraging dominance rules for pruning during planning. RF-A$^*$ is guaranteed to find an optimal solution and often runs 2 to 8 times faster than the existing approaches in large city maps with several hundreds of gas stations.