Multi-Underwater Gliders Coverage Path Planning Based on Ant Colony Optimization

Underwater gliders (UGs) are widely applied to regional exploration to find potential targets. However, the complex marine environment and special movement patterns make it difficult to plan their coverage path. In this paper, a novel multi-underwater gliders coverage path planning algorithm based on ant colony optimization (MGCPP-ACO) is proposed. First, according to the detection radius of the sonar and the motion process of the UGs, we establish a detection coverage model. Then, considering the motion constraints of the UGs and optimization objectives, we redesign the feasible region, transition probability, pheromone update rule and heuristic function of the ACO algorithm. Finally, we carry out three groups of experiments. The simulation results show that the MGCPP-ACO can cover almost the entire sea area and adapt to different initial positions and heading angles. In addition, compared with the traditional scan-line (SCAN) algorithm, the MGCPP-ACO has a higher coverage efficiency and lower coverage cost.

Automated summary

This paper proposes a novel multi-underwater gliders coverage path planning algorithm based on ant colony optimization, which effectively addresses the challenges posed by complex marine environments and special movement patterns. Experimental results show that the algorithm has high coverage efficiency and low coverage cost.