摘要
首先针对移动机器人全局静态路径规划问题提出了一种改进A~*蚁群算法的路径规划方法。利用简化A~*算法来优化初始信息素设置,提高算法的工作效率。通过随机选择机制优化状态转移规则避免陷入局部最优。提出了信息素奖惩机制以解决蚁群算法初期搜索收敛速度慢和路径规划中的死锁问题。最后提出了基于此改进A~*蚁群算法的动态环境局部碰撞规划策略。在栅格建模环境下的仿真结果表明,改进A~*蚁群算法在路径长度和迭代次数上有明显改善,并且能够完成在动态环境中的避障任务,快速高效的规划出一条光滑路径,在复杂环境中具有稳定性和很强的适应能力。
Firstly, a path planning method based on A~* ant colony algorithm is proposed for the global static path planning problem of mobile robots. The simplified A~* algorithm is used to optimize the initial pheromone setting and improve the efficiency of the algorithm. Optimize state transition rules by random selection mechanism to avoid falling into local optimum. The pheromone reward and punishment mechanism is proposed to solve the problem of slow convergence of initial search and antlocking in path planning. Finally, a dynamic environment local collision planning strategy based on this improved A~* ant colony algorithm is proposed. The simulation results in the grid modeling environment show that the improved A~* ant colony algorithm can significantly improve the path length and the number of iterations, and can complete the obstacle avoidance task in the dynamic environment, and quickly and efficiently plan a smooth path. Stability and strong adaptability in complex environments.
作者
刘耀
毛剑琳
Liu Yao;Mao Jianlin(College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
2020年第7期82-87,共6页
Electronic Measurement Technology
关键词
路径规划
A~*算法
蚁群算法
动态环境
path planning
A~* algorithm
ant colony algorithm
dynamic environment
