Fast Path Planning for Autonomous Ships in Restricted Waters

Abstract

Presently, there is increasing interest in autonomous ships to reduce human errors and support intelligent navigation, where automatic collision avoidance and path planning is a key problem, especially in restricted waters. To solve this problem, a path-guided hybrid artificial potential field (PGHAPF) method is first proposed in this paper. It is essentially a reactive path-planning algorithm that provides fast feedback in a changeable environment, including dynamic target ships (TSs) and static obstacles, for steering an autonomous ship safely. The proposed strategy, which is a fusion of the potential field and gradient methods, consists of potential-based path planning for arbitrary static obstacles, gradient-based decision-making for dynamic TSs, and their combination with consideration of the prior path and waypoint selection optimization. A three-degree-of-freedom dynamic model of a Mariner class vessel and a low-level controller have been incorporated together in this method to ensure that the vessel's positions are updated at each time step in order to acquire a more applicable and reliable trajectory. Simulations show that the PGHAPF method has the potential to rapidly generate adaptive, collision-free and International Regulations for Preventing Collisions at Sea (COLREGS)-constrained trajectories in restricted waters by deterministic calculations. Furthermore, this method has the potential to perform path planning on an electronic chart platform and to overcome some drawbacks of traditional artificial potential field (APF) methods.

Comparison of Computational Intelligence Methods Based on Fuzzy Sets and Game Theory in the Synthesis of Safe Ship Control Based on Information from a Radar ARPA System

“…The traditional artificial neural network does not perform the minimum and maximum operations of a finite set. Appropriate neurons were proposed by Rocha, which will be presented in the next sections [14][15][16][17][18].…”

Beam Search Algorithm for Anti-Collision Trajectory Planning for Many-to-Many Encounter Situations with Autonomous Surface Vehicles

“…APF, described in [24,27], is presented in order to locally optimize the path planning and dynamic obstacle avoidance tasks. APF refinement is a path-guided hybrid artificial potential field (PGHAPF) method, which is a fusion of the potential field and gradient methods [28]. The method can avoid static obstacles of any shape.…”

Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing

“…Therefore, as stated by Bist [3], the ARPA is a source of support for the work of the navigator, increasing navigational safety. The main task of ARPA is to prepare for maneuvering decisions made by the navigator, especially in situations of concentrated object traffic in restricted waters [4][5][6].…”