Safety-Guided RRT*: Hyperoctant Sampling-based Path Planning with SDF-based Robotic Representation

Abstract

Sampling-based path planning algorithms, such as Rapidly-exploring Random Tree (RRT), are widely used for motion planning in high degree-of-freedom robotic systems due to their efficiency in exploring high-dimensional spaces. However, traditional methods rely on binary collision detection, which only determines whether a sampled configuration is in a collision without quantifying its safety, often resulting in trajectories that are overly close to obstacles and reducing planning success rates, especially in complex environments with narrow passages. To address this issue, we propose Safety-Guided RRT* (SG-RRT*), which integrates a quantitative safety metric based on signed distance functions (SDFs) with a hyperoctant sampling strategy, enabling the planner to prioritize safer configurations and steer tree expansion toward collision-free regions. This approach significantly improves path planning success rates while generating safer trajectories with greater clearance from obstacles. Extensive simulations and real-world experiments demonstrate that SG-RRT* outperforms state-of-the-art methods, including RRT*, Informed-RRT*, TRRT, and Bi-TRRT, by achieving higher success rates and reducing collision risks, with only a slight increase in trajectory length.