We propose a method for performing autonomous docking of marine vessels using numerical optimal control. The task is framed as a dynamic positioning problem, with the addition of spatial constraints that ensure collision avoidance. The proposed method is an allencompassing procedure for performing both docking, maneuvering, dynamic positioning and control allocation. In addition, we show that the method can be implemented as a real-time MPC-based algorithm on simulation results of a supply vessel.
This paper proposes a methodology for solving the curved path following problem for underactuated vehicles under unknown ocean current influence using deep reinforcement learning. Three dynamic models of high complexity are employed to simulate the motions of a mariner vessel, a container vessel and a tanker. The policy search algorithm is tasked to find suitable steering policies, without any prior info about the vessels or their environment. First, we train the algorithm to find a policy for tackling the straight line following problem for each of the simulated vessels and then perform transfer learning to extend the policies to the curved-path case. This turns out to be a much faster process compared to training directly for curved paths, while achieving indistinguishable performance. Index Terms-Deep reinforcement learning, path following, transfer learning, marine control systems, unknown disturbances
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.
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