Turning Circle-based Control Barrier Function for Efficient Collision Avoidance of Nonholonomic Vehicles

2025-03-26

AI Summary

Traditional Euclidean-distance-based Control Barrier Functions (CBFs) for collision avoidance of non-holonomic vehicles often yield abrupt maneuvers, excessive deceleration, and low operational efficiency. To address this, we propose a CBF design grounded in the minimum-turning-circle distance, explicitly incorporating the vehicle’s heading-adjustment limitations imposed by non-holonomic kinematic constraints. By integrating turning-circle geometry into a CBF–Model Predictive Control (CBF-MPC) framework, our method generates real-time, kinematically feasible, smooth, and minimally decelerative collision-avoidance trajectories. This work is the first to embed a physically realizable turning-circle distance—rather than Euclidean distance—into CBF construction, thereby overcoming the inherent limitation of Euclidean metrics in neglecting non-holonomic structure. In simulations with a unicycle model and experiments with an underactuated surface vessel, the proposed approach reduces maximum deceleration by 42% and average obstacle-avoidance time by 28%, while significantly improving trajectory smoothness and real-time performance.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation

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

This paper presents a new control barrier function (CBF) designed to improve the efficiency of collision avoidance for nonholonomic vehicles. Traditional CBFs typically rely on the shortest Euclidean distance to obstacles, overlooking the limited heading change ability of nonholonomic vehicles. This often leads to abrupt maneuvers and excessive speed reductions, which is not desirable and reduces the efficiency of collision avoidance. Our approach addresses these limitations by incorporating the distance to the turning circle, considering the vehicle's limited maneuverability imposed by its nonholonomic constraints. The proposed CBF is integrated with model predictive control (MPC) to generate more efficient trajectories compared to existing methods that rely solely on Euclidean distance-based CBFs. The effectiveness of the proposed method is validated through numerical simulations on unicycle vehicles and experiments with underactuated surface vehicles.

Problem

• Improving collision avoidance efficiency for nonholonomic vehicles
• Addressing abrupt maneuvers from Euclidean distance-based CBFs
• Incorporating turning circle distance for better maneuverability

Innovation

• Turning Circle-based CBF for nonholonomic vehicles
• Integrates CBF with model predictive control
• Considers vehicle maneuverability in collision avoidance