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. Keywords: Hybrid collision avoidance, Autonomous surface vehicle (ASV), COLREGs, COLREGs compliant, Model predictive control (MPC), Energy-optimized control arXiv:1907.00198v2 [eess.SY] 14 Jul 20192018, Falco navigated autonomously between two ports in Finland 2 . Reports state that in excess of 75 % of maritime accidents are due to human errors (Chauvin, 2011;Levander, 2017), indicating that there is also a potential for increased safety in addition to the economical and environmental benefits.An obvious prerequisite for autonomous ship operations is the development of robust and wellfunctioning collision avoidance (COLAV) systems. In addition to generating collision-free maneuvers, a COLAV system must adhere to the "rules of the road" of the oceans, i.e. the International Regulations for Preventing Collisions at Sea (COLREGs) (Cockcroft and Lameijer, 2004). These rules are written for human ship operators and include qualitative requirements on how to perform safe and readily observable maneuvers. Part B of the COLREGs concern steering and sailing, and includes the following rules that are the most relevant to a motion control system: Rule 8Requires maneuvers to be readily observable and to be done in ample time. Rules 13-15 Describe the maneuvers to perform in cases of overtaking, head-on and crossing situations. Participants in crossing situations are defined by the terms give-way and stand-on vessels. Rule 16Requires that a give-way vessel must take early and substantial action to keep clear of the stand-on vessel. Rule 17Consists of two main parts. The first part requires a stand-on vessel to maintain its course and speed, whi...

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We present improvements to a recently developed method for trajectory planning for autonomous surface vehicles (ASVs) in terms of run time. The original method combines two types of planners: An A implementation that quickly finds the global shortest piecewise linear path on a uniformly discretized map, and an optimal control-based trajectory planner which takes into account ASV dynamics. Firstly, we propose an improvement to the discretization of the map by switching to a Voronoi diagram rather than the uniform discretization, which offers a far more sparse search tree for the A implementation. Secondly, modifications to the path refinement are made, as suggested in a paper by Bhattacharya and Gavrilova. The changes result in a reduction to the run time of the first part of the method of 85 % for an example scenario while maintaining the same level of optimality.

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We propose a method for energy-optimized trajectory planning for autonomous surface vehicles (ASVs), which can handle arbitrary polygonal maps as obstacle constraints. The method comprises two stages: The first is a hybrid A search that finds a dynamically feasible trajectory in a polygonal map on a discretized configuration space using optimal motion primitives. The second stage uses the resulting hybrid A trajectory as an initial guess to an optimal control problem (OCP) solver. In addition to providing the OCP with a warm start, we use the initial guess to create convex regions encoded as halfspace descriptions, which converts the inherent nonconvex obstacle constraints into a convex and smooth representation. The OCP uses this representation in order to optimize the initial guess within a collision-free corridor. The OCP solves the trajectory planning problem in continuous state space. Our approach solves two challenges related to optimization-based trajectory planning: The need for a dynamically feasible initial guess that can guide the solver away from undesirable local optima and the ability to represent arbitrary obstacle shapes as smooth constraints. The method can take into account external disturbances such as wind or ocean currents. We compare our method to two similar trajectory planning methods in simulation and have found significant computation time improvements. Additionally, we have validated the method in full-scale experiments in the Trondheim harbor area.