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This article considers collision avoidance (COLAV) for both static and movingobstacles using the branching-course model predictive control (BC-MPC)algorithm, which is designed for use by autonomous surface vehicles (ASVs). TheBC-MPC algorithm originally only considered COLAV of moving obstacles, so inorder to make the algorithm also be able to avoid static obstacles, weintroduce an extra term in the objective function based on an occupancy grid.In addition, other improvements are made to the algorithm resulting intrajectories with less wobbling. The modified algorithm is verified throughfull-scale experiments in the Trondheimsfjord in Norway with both virtualstatic obstacles and a physical moving obstacle. A radar-based tracking systemis used to detect and track the moving obstacle, which enables the algorithm toavoid obstacles without depending on vessel-to-vessel communication. Theexperiments show that the algorithm is able to simultaneously avoid both staticand moving obstacles, while providing clear and readily observable maneuvers.The BC-MPC algorithm is compliant with rules 8, 13 and 17 of the theInternational Regulations for Preventing Collisions at Sea (COLREGs), andfavors maneuvers following rules 14 and 15.

Short-term ASV Collision Avoidance with Static and Moving Obstacles