Collision avoidance in heterogeneous fleets of uncrewed vessels is challenging because the decision-making processes and controllers often differ between platforms, and it is further complicated by the limitations on sharing trajectories and control values in real-time. This paper presents a pragmatic approach that addresses these issues by adding a control filter on each autonomous vehicle that assumes worst-case behavior from other contacts, including crewed vessels. This distributed safety control filter is developed using control barrier function (CBF) theory and the application is clearly described to ensure explainability of these safety-critical methods. This work compares the worst-case CBF approach with a Collision Regulations (COLREGS) behavior-based approach in simulated encounters. Real-world experiments with three different uncrewed vessels and a human operated vessel were performed to confirm the approach is effective across a range of platforms and is robust to uncooperative behavior from human operators. Results show that combining both CBF methods and COLREGS behaviors achieves the best safety and efficiency.

翻译：异构无人水面艇编队中的碰撞规避具有挑战性，因为不同平台间的决策过程与控制器通常存在差异，且实时共享轨迹与控制值的限制进一步加剧了复杂性。本文提出一种实用方法，通过为每艘自主航行器添加一个控制过滤器来解决这些问题，该过滤器假设其他接触目标（包括有人驾驶船只）具有最坏行为。该分布式安全控制过滤器基于控制屏障函数理论开发，并对其应用进行了清晰阐述，以确保这些安全关键方法的可解释性。本研究在最坏情况CBF方法与基于《国际海上避碰规则》行为的模拟遭遇场景中进行了对比。通过使用三种不同无人水面艇与一艘人工操作船只进行真实世界实验，证实了该方法在多种平台上均有效，并对人类操作员的非合作行为具有鲁棒性。结果表明，结合CBF方法与COLREGS行为能实现最佳安全性与效率。