Multi-Objective Global Path Planning for Lunar Exploration With a Quadruped Robot

TL;DR

This paper presents a multi-objective global path planner based on A* for lunar exploration, which considers various environmental factors to optimize robot trajectories, reducing failure risk and enhancing scientific outcomes.

Contribution

It introduces a novel multi-objective A* based planner with adaptable weights and a statistical analysis tool, tailored for planetary exploration path optimization.

Findings

Optimized paths reduce failure risk significantly.

Paths yield higher scientific value than manual planning.

Planner and tools are open-source for community use.

Abstract

In unstructured environments the best path is not always the shortest, but needs to consider various objectives like energy efficiency, risk of failure or scientific outcome. This paper proposes a global planner, based on the A* algorithm, capable of individually considering multiple layers of map data for different cost objectives. We introduce weights between the objectives, which can be adapted to achieve a variety of optimal paths. In order to find the best of these paths, a tool for statistical path analysis is presented. Our planner was tested on exemplary lunar topographies to propose two trajectories for exploring the Aristarchus Plateau. The optimized paths significantly reduce the risk of failure while yielding more scientific value compared to a manually planned paths in the same area. The planner and analysis tool are made open-source in order to simplify mission planning for planetary scientists.