Hybrid Collision Avoidance for ASVs Compliant with COLREGs Rules 8 and 13-17
arXiv:1907.00198 · doi:10.3389/frobt.2020.00011
Abstract
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.
arXiv commentSubmitted to Frontiers in Robotics and AI
References in corpus (2)
Cited by in corpus (6)
- The Branching-Course MPC Algorithm for Maritime Collision Avoidance
- A Guidance and Maneuvering Control System Design with Anti-collision Using Stream Functions with Vortex Flows for Autonomous Marine Vessels
- A COLREGs-Compliant Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments
- Short-term ASV Collision Avoidance with Static and Moving Obstacles
- Asynchronous distributed collision avoidance with intention consensus for inland autonomous ships
- Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning