Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm - Wang, Lina; Wang, Hejing; Yang, Xin; Gao, Yanfeng; Cui, Xiaohong; Wang, Binrui (Frontiers in Neurorobotics, 2022)
Aiming at the problems of slow convergence and easy fall into optimal local solution when the classical ant colony optimization algorithm is applied to mobile robot path planning, an improved ant colony optimization algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and the pheromone content on the guiding path is increased to enhance the guiding effect of the initial pheromone on the ant colony and improve the convergence speed of the algorithm. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap and improve the success rate of ants finding the target node. Thirdly, the artificial potential field method and the concept of distance from the optional node to target node are introduced to optimize the heuristic function to make up for the influence of the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption, and combines the multi-objective optimization idea and dynamic principle to optimize the pheromone update mode, to avoid the algorithm falling into the optimal local solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively.