Eriksen Bjørn-Olav H, Bitar Glenn, Breivik Morten, Lekkas Anastasios M
Department of Engineering Cybernetics, Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway.
Front Robot AI. 2020 Feb 11;7:11. doi: 10.3389/frobt.2020.00011. eCollection 2020.
This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicles, compliant with rules 8 and 13-17 of the International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV system consists of a high-level planner producing an energy-optimized trajectory, a model-predictive-control-based mid-level COLAV algorithm considering moving obstacles and the COLREGs, and the branching-course model predictive control algorithm for short-term COLAV handling emergency situations in accordance with the COLREGs. Previously developed algorithms by the authors are used for the high-level planner and short-term COLAV, while we in this paper further develop the mid-level algorithm to make it comply with COLREGs rules 13-17. This includes developing a state machine for classifying obstacle vessels using a combination of the geometrical situation, the distance and time to the closest point of approach (CPA) and a new CPA-like measure. The performance of the hybrid COLAV system is tested through numerical simulations for three scenarios representing a range of different challenges, including multi-obstacle situations with multiple simultaneously active COLREGs rules, and also obstacles ignoring the COLREGs. The COLAV system avoids collision in all the scenarios, and follows the energy-optimized trajectory when the obstacles do not interfere with it.
本文提出了一种用于自主水面舰艇的三层混合避碰（COLAV）系统，该系统符合《国际海上避碰规则》（COLREGs）第8条以及第13 - 17条规则。COLAV系统由生成能量优化轨迹的高级规划器、考虑移动障碍物和COLREGs的基于模型预测控制的中级COLAV算法，以及用于根据COLREGs处理紧急情况的短期COLAV的分支航向模型预测控制算法组成。作者之前开发的算法用于高级规划器和短期COLAV，而在本文中，我们进一步开发中级算法，使其符合COLREGs第13 - 17条规则。这包括开发一个状态机，通过结合几何情况、到最近会遇点（CPA）的距离和时间以及一种类似CPA的新度量来对障碍物船只进行分类。通过数值模拟对混合COLAV系统的性能进行了测试，模拟了三种代表一系列不同挑战的场景，包括存在多个同时有效的COLREGs规则的多障碍物情况，以及无视COLREGs的障碍物情况。COLAV系统在所有场景中都能避免碰撞，并且在障碍物不干扰时遵循能量优化轨迹。
