Research on Path Planning in 3D Complex Environments Based on Improved Ant Colony Algorithm

• Published in: Symmetry (Basel) Vol. 14; no. 9; p. 1917
• Main Authors: Zhou, Hang, Jiang, Ziqi, Xue, Yuting, Li, Weicong, Cai, Fanger, Li, Yunchen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Accuracy; Algorithms; Ant colony optimization; Coordinate transformations; Design; Efficiency; Flight operations; Path planning; Planning; Run time (computers)
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym14091917

Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment information, which can record the flight environment and avoid the inefficiency of planning. Secondly, a multi-objective function of distance and angle is established to improve the efficiency and safety of the path. Then, a target-guided heuristic function is proposed, and an anti-deadlock mechanism is introduced to improve the efficiency of the ant colony algorithm. Next, the node pheromone update rules are improved to further improve the efficiency of the algorithm. Finally, experiments prove the effectiveness of the improved algorithm, TGACO, and its efficiency in complex environments has obvious advantages. In the 20×20×20 environment, compared with the ant colony algorithm (ACO), the improved algorithm (TGACO) in this paper improves the path length, total turning angle, and running time by 17.8%, 78.4%, and 95.3%, respectively.