Collision Cone Control Barrier Functions for Kinematic Obstacle Avoidance in UGVs | IEEE Conference Publication | IEEE Xplore

Collision Cone Control Barrier Functions for Kinematic Obstacle Avoidance in UGVs


Abstract:

In this paper, we propose a new class of Control Barrier Functions (CBFs) for Unmanned Ground Vehicles (UGVs) that help avoid collisions with kinematic (non-zero velocity...Show More

Abstract:

In this paper, we propose a new class of Control Barrier Functions (CBFs) for Unmanned Ground Vehicles (UGVs) that help avoid collisions with kinematic (non-zero velocity) obstacles. While the current forms of CBFs have been successful in guaranteeing safety/collision avoidance with static obstacles, extensions for the dynamic case have seen limited success. Moreover, with the UGV models like the unicycle or the bicycle, applications of existing CBFs have been conservative in terms of control, i.e., steering/thrust control has not been possible under certain scenarios. Drawing inspiration from the classical use of collision cones for obstacle avoidance in trajectory planning, we introduce its novel CBF formulation with theoretical guarantees on safety for both the unicycle and bicycle models. The main idea is to ensure that the velocity of the obstacle w.r.t. the vehicle is always pointing away from the vehicle. Accordingly, we construct a constraint that ensures that the velocity vector always avoids a cone of vectors pointing at the vehicle. The efficacy of this new control methodology is later verified by Pybullet simulations on TurtleBot3 and F1 Tenth.
Date of Conference: 18-20 December 2023
Date Added to IEEE Xplore: 27 February 2024
ISBN Information:
Conference Location: Visakhapatnam, India

I. Introduction

Advances in autonomy have enabled robot application in all kinds of environments and in close interactions with humans, including autonomous navigation. In dynamic en-vironments, i.e., containing moving obstacles, the collision avoidance system must accommodate an unpredictable in-formation picture providing only a limited time to react to a collision. As a result, the effectiveness of planning-based algorithms is reduced significantly, highlighting the need for developing real-time reactive methods that ensure safety. Thus, designing controllers with formal real-time safety guarantees has become an essential aspect of such safety-critical applications and an active research area in recent years. Researchers have developed many tools to handle this problem, such as reachability analysis [1] [2] and artificial potential fields [3]. To obtain formal guaran-tees on safety (e.g., collision avoidance with obstacles), a safety critical control algorithm encompassing the trajectory tracking/planning algorithm is required that prioritizes safety over tracking. Control Barrier Functions [4] (CBFs) based approach is one such strategy in which a safe state set defined by inequality constraints is designed for the vehicle, and its quadratic programming (QP) formulation ensures forward invariance of these sets for all time.

References

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