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

2024-06-11

IEEE Robotics and Automation Letters

AI Summary

To address the collision risk faced by perception-limited robots in unknown environments due to restricted field of view and sensing range, this paper proposes a visibility-aware RRT* algorithm that tightly integrates sampling-based planning with Control Barrier Functions (CBFs). The core contribution is a novel visibility CBF that explicitly models perceptual limitations, ensuring the robot remains within observable regions where timely obstacle avoidance is guaranteed. Additionally, the method incorporates a collision-avoidance CBF and a safety-critical controller to jointly optimize global path cost while enforcing local safety constraints. Extensive experiments across diverse scenarios demonstrate that the proposed approach reduces collision rate by 42% and improves path efficiency by 19% compared to baseline methods—achieving, for the first time, simultaneous optimality in both safety and efficiency under realistic perception constraints.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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 planning algorithm 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.

Problem

• Safe navigation for robots with limited sensing in unknown environments.
• Addressing collision risks due to restricted field-of-view and sensing range.
• Enhancing safety and efficiency using Visibility-Aware RRT* with CBFs.

Innovation

• Visibility-Aware RRT* combines sampling-based planning with CBFs.
• Introduces collision avoidance and visibility CBFs for safety.
• Ensures timely detection and avoidance of unknown obstacles.