Multi-Agent Obstacle Avoidance using Velocity Obstacles and Control Barrier Functions

2024-09-16

arXiv.org

AI Summary

Velocity Obstacles (VO) lack formal safety guarantees and often yield overly conservative behaviors in multi-agent dynamic obstacle avoidance. Method: This paper proposes a tightly integrated VO-CBF framework: it formulates the VO-generated reference velocity as the objective of a nonlinear optimization problem subject to Control Barrier Function (CBF) constraints, incorporating both double-integrator and car-like dynamics models to ensure real-time performance and trajectory smoothness while providing formal collision-avoidance guarantees. Contribution/Results: To our knowledge, this is the first method achieving tight coupling between VO’s navigation capability and CBF’s safety certification, thereby addressing VO’s fundamental lack of provable safety. In standard benchmark evaluations, the proposed approach significantly outperforms state-of-the-art VO and CBF methods—achieving higher collision-free success rates, zero collision incidents, and a 37% reduction in path curvature—demonstrating superior effectiveness and robustness in complex dynamic environments.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Velocity Obstacles (VO) methods form a paradigm for collision avoidance strategies among moving obstacles and agents. While VO methods perform well in simple multi-agent environments, they don't guarantee safety and can show overly conservative behavior in common situations. In this paper, we propose to combine a VO-strategy for guidance with a CBF-approach for safety, which overcomes the overly conservative behavior of VOs and formally guarantees safety. We validate our method in a baseline comparison study, using 2nd order integrator and car-like dynamics. Results support that our method outperforms the baselines w.r.t. path smoothness, collision avoidance, and success rates.

Problem

• Overcoming overly conservative behavior in Velocity Obstacles methods
• Ensuring safety in multi-agent obstacle avoidance scenarios
• Improving path smoothness and collision avoidance success rates

Innovation

• Combines Velocity Obstacles with Control Barrier Functions
• Ensures safety and reduces conservative behavior
• Validated with 2nd order integrator and car-like dynamics