OA-ECBVC: A Cooperative Collision-free Encirclement and Capture Approach in Cluttered Environments (2312.01326v1)

Abstract: This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms.

• The paper introduces the OA-ECBVC algorithm that modifies Voronoi partitioning to ensure collision-free encirclement and capture of an evader.
• It employs a decentralized strategy where robots form a convex hull around the target, yielding faster capture times and improved safety ratios.
• Real-world experiments and simulations validate the algorithm’s robustness and real-time applicability in dynamic, obstacle-rich settings.

Introduction

In the field of robotics and autonomous systems, one of the challenging problems is the pursuit-evasion scenario, where multiple robots (pursuers) attempt to track down and capture an evader, particularly in environments filled with obstacles. This type of scenario is crucial in applications like area surveillance, search and rescue operations, and wildlife monitoring.

Problem Formulation

Pursuit-evasion problems involve complex dynamics, as the pursuers must collaborate in real-time to intercept the evader while avoiding collisions with each other and with obstacles. Traditional approaches have struggled with the computational complexity, especially when multiple autonomous agents are involved. This paper addresses these complexities with a decentralized algorithm that allows multiple robots to encircle and eventually capture an evader in cluttered and unbounded environments.

Approach to Encirclement and Capture

The authors propose an algorithm that constructs obstacle-aware, evader-centered bounded Voronoi cells (OA-ECBVC). This method ensures collision avoidance by modifying the standard Voronoi cell partitioning technique for each pursuer, factoring in the relative positions of other agents and potential obstacles. The approach is twofold: firstly, it involves encirclement, where the pursuers form a convex hull around the evader, restricting the evader's freedom of movement without collisions. Secondly, capture strategy comes into play once the evader is encircled, and involves shrinking the Voronoi cells to reduce the evader's accessible area to facilitate capture.

Validation and Performance

The proposed algorithm's effectiveness is demonstrated through various simulations that include dynamic systems of robots and environments with a range of obstacles. The simulations provided a side-by-side comparison with existing methods, highlighting the proposed algorithm's ability to achieve faster capture times and maintain higher safety ratios. Moreover, the paper presents real-world experiments, further ascertaining the robustness and real-time applicability of the algorithm across different robot platforms.

Conclusion

The OA-ECBVC algorithm is a significant contribution to decentralized multi-robot systems, particularly in pursuit-evasion problems within obstacle-rich environments. It ensures safety through coordinated encirclement and capture while effectively managing the dynamic challenges inherent in real-life applications. Future endeavors may extend the current framework to handle multiple evaders and operate with local, rather than global, positioning information.