Gong Chikun, Yang Yuhang, Yuan Lipeng, Wang Jiaxin
College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
School of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin 15001, China.
Math Biosci Eng. 2022 Aug 25;19(12):12405-12426. doi: 10.3934/mbe.2022579.
To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.
为提高蚁群优化算法（ACO）的路径优化效果和搜索效率，提出了一种改进的蚁群算法。基于已知环境信息生成一条约束路径，以避免在早期规划时进行盲目搜索。引入终点和转折点的影响，以改进启发式信息，实现较高的搜索效率。引入信息素强度值的自适应调整，以优化信息素更新策略。给出了多种更新参数的控制策略，以平衡收敛性和全局搜索能力。然后，针对不同形状和运动状态的动态障碍物，提出了改进的避障策略，克服了现有避障策略的缺点。在不同仿真环境下与其他改进算法进行比较，结果表明本文算法在复杂大环境中更有效、更稳健。另一方面，在动态环境中与其他避障策略进行比较表明，本文设计的策略在局部避障后具有更高的路径质量、对传感器性能的要求更低且安全性更高。
