Abstract
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.
Highlights
In marine applications, we see efforts to increase the level of autonomy in research, defense, and commercial applications
CONTRIBUTIONS We have developed a method that plans energy-optimized trajectories in an environment defined by polygonal obstacles for an autonomous surface vehicle (ASV) under the influence of external disturbances
Our method is based on continuous optimal control, and the optimal control problem (OCP) solver is warm-started by the solution of a hybrid A search algorithm
Summary
We see efforts to increase the level of autonomy in research, defense, and commercial applications. In 2018, both Wärtsilä and Rolls-Royce Marine (acquired by Kongsberg Maritime) demonstrated autonomous capabilities with the ferries Folgefonn and Falco, respectively.. In 2018, both Wärtsilä and Rolls-Royce Marine (acquired by Kongsberg Maritime) demonstrated autonomous capabilities with the ferries Folgefonn and Falco, respectively.1 Both tests included automatic transit and docking. Another example of commercial use of maritime autonomous technology is when the Japanese shipping company NYK completed the world’s first maritime autonomous surface ship trial in 2019.2. An essential part of an autonomous marine system is path and trajectory planning, where the goal is to plan how the vessel will move from its start location to the goal location. 1 https://www.maritime- executive.com/article/rolls- royce- and- wartsilain-close-race-with-autonomous-ferries (accessed September 14, 2020). Bitar et al.: Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory & Experiments we focus on trajectory planning
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