A Pareto Front-Based Multiobjective Path Planning Algorithm

TL;DR

This paper introduces A*-PO, a Pareto front-based multiobjective path planning algorithm that enhances A* search to handle multiple objectives efficiently, demonstrated through simulations and a planetary rover case study.

Contribution

The paper presents a novel A*-PO algorithm integrating Pareto optimality into A* for multiobjective path planning, improving solution quality over existing methods.

Findings

A*-PO outperforms standard A* variants in simulations.

The algorithm effectively handles multiobjective optimization.

Successful application demonstrated on planetary exploration rover case study.

Abstract

Path planning is one of the most vital elements of mobile robotics. With a priori knowledge of the environment, global path planning provides a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost. Path planning optimization typically looks to minimize the distance traversed from start to goal, yet many mobile robot applications call for additional path planning objectives, presenting a multiobjective optimization (MOO) problem. Past studies have applied genetic algorithms to MOO path planning problems, but these may have the disadvantages of computational complexity and suboptimal solutions. Alternatively, the algorithm in this paper approaches MOO path planning with the use of Pareto fronts, or finding non-dominated solutions. The algorithm presented incorporates Pareto optimality into every step of A* search, thus it is named A*-PO. Results of simulations show A*-PO outperformed several variations of the standard A* algorithm for MOO path planning. A planetary exploration rover case study was added to demonstrate the viability of A*-PO in a real-world application.