Visibility-Aware RRT* for Safety-Critical Navigation of Perception-Limited Robots in Unknown Environments

TL;DR

This paper introduces Visibility-Aware RRT*, a planning algorithm that integrates safety and visibility considerations to enable safe navigation for perception-limited robots in unknown environments.

Contribution

It proposes a novel sampling-based planning method that combines Control Barrier Functions with visibility awareness to improve safety and efficiency in partially known spaces.

Findings

Outperforms baseline methods in safety metrics.

Ensures collision avoidance with unknown obstacles.

Enhances navigation efficiency in limited sensing scenarios.

Abstract

Safe autonomous navigation in unknown environments remains a critical challenge for robots with limited sensing capabilities. While safety-critical control techniques, such as Control Barrier Functions (CBFs), have been proposed to ensure safety, their effectiveness relies on the assumption that the robot has complete knowledge of its surroundings. In reality, robots often operate with restricted field-of-view and finite sensing range, which can lead to collisions with unknown obstacles if the planner is agnostic to these limitations. To address this issue, we introduce the Visibility-Aware RRT* algorithm that combines sampling-based planning with CBFs to generate safe and efficient global reference paths in partially unknown environments. The algorithm incorporates a collision avoidance CBF and a novel visibility CBF, which guarantees that the robot remains within locally collision-free regions, enabling timely detection and avoidance of unknown obstacles. We conduct extensive experiments interfacing the path planners with two different safety-critical controllers, wherein our method outperforms all other compared baselines across both safety and efficiency aspects.