Application of Improved Ant Colony Algorithm in Path Planning of Underwater Robot

Abstract

AbstractWith the continuous deepening of scientific development and technological innovation, many algorithms have been derived from the research on robot path planning, including the current research on ant colony algorithm. It is often accompanied by a series of problems such as slow convergence speed, seeking optimal solutions according to local conditions, insufficient optimization ability, and inability to complement the diversity of ant colony species and convergence speed. In view of the current shortcomings, first, the initial pheromone distribution is uneven, which can avoid the useless search behavior trajectory of the ants during the movement process, thereby speeding up the solution speed and saving time; secondly, based on the advantages of the A* algorithm, the introduction into its algorithm The heuristic search function further improves the heuristic function of the traditional ant colony algorithm and maximizes the speed of the search. Considering the influence factors of energy consumption factors on the experiment in actual situations, a new heuristic function is formed by combining with traditional factors, a simulation environment is built, and the proposed method is verified and compared through a set of examples. The results show that the improved ant colony algorithm is in the optimal path. It has better performance in length and number of iterations, and has practical engineering significance for robot path planning.KeywordsAnt colony algorithmA* algorithmPath planningMATLAB simulation