Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.
This paper introduces an approach for solving a safe ship trajectory planning problem. The algorithm, utilising the concept of a discrete artificial potential field and a path optimisation algorithm, calculates an optimised collision-free trajectory for a ship. The method was validated by simulation tests with the use of real navigational data registered on board the research and training ship Horyzont II. Results of simulation studies demonstrate that the approach is capable of finding a collision-free trajectory in near-real time, and this proves its applicability in commercial collision avoidance systems for ships. The paper contributes to the development of decision support systems for ships and autonomous navigation.
The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents -artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs) compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.
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