Abstract
Cruise ships are widely used in water quality monitoring but suffer from path planning problems in a surface water environment. Solutions to path planning problems have higher requirements of path planning distance and path planning time and the ability of real-time obstacles avoidance. This paper introduces a hybrid path planning method to solve the path planning problem when using unmanned cruise ships. First, a model of the surface water environment with unknown districts is established by the grid method. Then, the global path is planned by the A* algorithm based on such a model, and in unknown areas of the model, the artificial potential filed (APF) is employed for local path planning. The problem of obtaining unreachable targets and falling into a local minimum introduced by the APF is improved by using the optimized repulsion potential field function and adding the directional random escape strategy. The A* algorithm with self-learning ability is proposed for secondary planning and cases where a local path cannot be generated. Finally, the optimal path combined with the global path and the local path is smoothed. The simulation results show that the proposed algorithm has better performance than other algorithms from the aspects of distance cost and time cost.
Highlights
Unmanned cruise ships, surface mobile devices that can be autonomously cruised, are widely used for water quality sampling in public waters
Path planning research can be divided into global path planning and local path planning from the perspective of target range [2]
Several algorithms are proposed for this area, such as Dijkstra algorithm [3], A∗ algorithm [4], particle swarm optimization (PSO) [5], genetic algorithm (GA) [6], ant colony optimization (ACO) [7], [8], and grey wolf optimization (GWO) algorithm [9], etc
Summary
Surface mobile devices that can be autonomously cruised, are widely used for water quality sampling in public waters. A mixture of A∗ algorithm and ACO shows good obstacle avoidance characteristics in complex environment [14]; an integration of PSO and Dijkstra algorithm obtains less path distance cost [15]; and ACO combined with PSO possess an ability to explore the environment including a small range of unknown regions [16]. The main contributions of this work can be summarized as follows: (1) the hybrid algorithm proposed in this paper combines the advantages of the A∗ algorithm and the APF method that can obtain feasible paths when facing with unknown or partially known environments; (2) the APF method, part of the proposed hybrid algorithm, is modified to deal with problems of obtaining unreachable targets and falling into a local minimum; (3) the hybrid algorithm proposed in this paper has a sensor-based self-learning ability which can better solve path planning problems in complex environments. Where (xn, yn) is the coordinate of the node n in the two-dimensional grid network, and (xtarget , ytarget ) is the coordinate of the target
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