Dynamic Collision Avoidance Using VelocityObstacle-based Control Barrier Functions

2025-03-01

AI Summary

Real-time obstacle avoidance for single-wheel robots under acceleration control remains challenging in high-speed dynamic environments. Method: This paper proposes Velocity-Obstacle-based Control Barrier Functions (VOCBFs), which directly enforce safety constraints in velocity space—bypassing the modeling complexity and computational bottlenecks of high-order CBFs. We introduce the first CBF construction paradigm within the velocity-obstacle (VO) framework, enabling distributed multi-robot coordination and reactive interaction-aware avoidance. Integrating a Control Lyapunov Function (CLF) for navigation with VOCBF for safety, we formulate a mixed-integer quadratic programming (QP) optimization framework. Results: Simulations demonstrate that VOCBF achieves significantly higher obstacle-avoidance success rates and lower computational latency compared to high-order CBFs (HOCBFs). The method supports millisecond-level real-time execution, scalable distributed deployment across multiple robots, zero collisions, and stable end-to-end closed-loop convergence.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions(CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub-optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast-moving and close to the robot. Furthermore, we extend our method to distributed multi-robot systems.

Problem

• Design safety-critical controllers for unicycle robots
• Enhance dynamic collision avoidance using VOCBFs
• Improve computational efficiency in multi-robot systems

Innovation

• Velocity obstacle-based CBFs enhance collision avoidance
• MIQP controller integrates CLFs and VOCBFs for safety
• Decision network reduces MIQP computational costs