Improving Rover Path Planning in Challenging Terrains: A Comparative Study of RRT-Based Algorithms

Abstract

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions.