Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm

• Published in: Computers & industrial engineering Vol. 156; p. 107230
• Main Authors: Miao, Changwei, Chen, Guangzhu, Yan, Chengliang, Wu, Yuanyuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021
• Subjects: Ant colony algorithm; Energy consumption; Mobile robot; Multi-objective optimal; Path planning; Energy consumption; Path planning; Multi-objective optimal; Mobile robot; Ant colony algorithm
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107230

•Heuristic distance in ant colony algorithm (ACO) is redefined.•Transition probability is improved by introducing obstacle exclusion and angle steering factors.•Pheromone update in ant colony algorithm (ACO) is improved.•Multi-objective path planning function are introduced. In view of the shortcomings of traditional ant colony algorithm (ACO) in path planning of indoor mobile robot, such as a long time path planning, non-optimal path for the slow convergence speed, and local optimal solution characteristic of ACO, an improvement adaptive ant colony algorithm (IAACO) is proposed in this paper. In IAACO, firstly, in order to accelerate the real-time and safety of robot path planning, angle guidance factor and obstacle exclusion factor are introduced into the transfer probability of ACO; secondly, heuristic information adaptive adjustment factor and adaptive pheromone volatilization factor are introduced into the pheromone update rule of ACO, to balance the convergence and global search ability of ACO; Finally, the multi-objective performance indexes are introduced to transform the path planning problem into a multi-objective optimization problem, so as to realize the comprehensive global optimization of robot path planning. The experimental results of main parameters selection, path planning performance in different environments, diversity of the optimal solution show that IAACO can make the robot attain global optimization path, and high real-time and stability performances of path planning.