Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective.

Autor: Pu, Xingcheng, Song, Xinlin, Tan, Ling, Zhang, Yi
Zdroj: Evolutionary Intelligence; Jun2024, Vol. 17 Issue 3, p1313-1326, 14p
Abstrakt: In order to solve the problem of path planning of a single robot and multi-Robots with multi-objective, an improved ant colony algorithm is proposed.In the improved ant colony algorithm, both local and global optimal searching are considered. In local optimal searching, gradient distribution is used to initialize the pheromone concentration to improve the convergence speed of the algorithm. The self-adaptive impact factor is used to enhance better fitness of the algorithm in different environments. In global optimal searching, a reward and punishment mechanism has been established to improve the pheromone update strategy. Furthermore, a node rearrangement strategy is adopted to increase the diversity of solutions. According to the improved colony algorithm, the problem of path planning of a single robot and multi-Robots with multi-objective can be solved. In two different scenarios, compared experiments show that the improved colony algorithm can obtain better path length, convergence speed and running time than traditional ant colony algorithm (ACO) and the improved ant colony algorithm (IACO-SFLA) in path planning of a single robot. Simulation results also show that the improved ant colony algorithm has good ability in multi-objective path planning. Especially, all robots can safely reach the targets by moving simultaneously and obstacles can be effectively avoided too. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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