Research on Multi-Objective Traversal Method for Robots Based on Improved Ant Colony Algorithm

Underwater robots often work at multiple target points when performing tasks, while taking into account the uncontrollable factors of the underwater environment, in order to meet the practical needs of various constraints, as well as to solve the problem of the traditional single performance (range) index. This paper proposes a multi-task point traversal method for underwater robots in marine environment based on improved ant colony algorithm, while considering path length, energy consumption and path smoothness as the comprehensive objectives, and adopting linear weighted summation idea to complete multi-task traversal under different objectives. And for the traditional ant colony algorithm heuristic function factor, pheromone factor, and volatility factor are constants, which will lead to the ant colony algorithm in the convergence of the slow speed of this problem to improve, the use of adaptive heuristic function factor as well as the pheromone factor and volatility to get the optimal solution, and finally fused with the A* algorithm to find a comprehensive optimal multi-task traversal of the objective. The improved algorithm is analyzed by simulation to verify its feasibility and effectiveness in multi-target traversal. The research content provides some help for underwater robots to perform multitasking operations.