电子学报

• 科研通信 • 上一篇    下一篇

动态环境下基于多人工鱼群算法和避碰规则库的机器人路径规划

徐晓晴, 朱庆保   

  1. 南京师范大学计算机科学与技术学院, 江苏南京 210097; 江苏省信息安全保密技术工程研究中心, 江苏南京 210097
  • 收稿日期:2011-04-24 修回日期:2011-12-23 出版日期:2012-08-25 发布日期:2012-08-25
  • 作者简介:徐晓晴 女,1988年出生于江苏省宜兴市.南京师范大学计算机科学与技术学院硕士在读,从事智能计算、机器人路径规划研究. 朱庆保 男,1955 年出生于山东省淄博市.南京师范大学计算机科学学院教授,博士生导师,主要从事人工智能和机器人技术的研究. E-mail:zhuqingbao@njnu.edu.cn
  • 基金资助:

    国家自然科学基金(No.60673102,No.61073118/F020508);江苏省普通高校自然科学研究计划(No.0KJD520004)

Multi-Artificial Fish-Swarm Algorithm and a Rule Library Based Dynamic Collision Avoidance Algorithm for Robot Path Planning in a Dynamic Environment

XU Xiao-qing, ZHU Qing-bao   

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210097, China; Jiangsu Research Center of Information Security & Privacy Technology, Nanjing, Jiangsu 210097, China
  • Received:2011-04-24 Revised:2011-12-23 Online:2012-08-25 Published:2012-08-25

摘要: 为了提高机器人路径规划的速度、环境适应能力和高效动态避碰问题,提出了一种基于多人工鱼群的机器人路径规划算法和基于避碰规则库的动态避障算法.该算法中,人工鱼以其与目标点的距离为食物浓度,两个邻近栅格的距离为步长,其觅食行作为默认行为,在一定条件下执行聚群或追尾动作,并采用两鱼群双向搜索机制在静态环境下规划出较优路径.在此基础上,机器人查询动态避障规则库获得避碰方法,从而实现与动态障碍的避碰.大量仿真实验结果表明,该方法具有较高的收敛速度和较强的搜索能力,能在非常复杂的动静态障碍环境中,迅速规划出一条安全避碰的优化路径.

关键词: 动态环境, 机器人路径规划, 多人工鱼群, 避障规则库

Abstract: In order to improve the convergence speed and the environmental adaptability of the path planning algorithm,a robot path planning algorithm based on multi-artificial fish-swarm is proposed.We present also a dynamic obstacle avoidance algorithm based on the rule-base of collision avoidance in dynamic environment to avoid collisions with the moving obstacles.In our approach,the distance between a fish and a goal is defined as food concentration and the distance between two neighbor grids is defined as step length.The preying behavior of fishes is regard as default behavior and perform clusters act or rear-end act is activated in some certain condition.Then the optimal path in static environment is planned by the search mechanism of bi-directional fish-swarms.After that,the effective collision avoidance behavior is obtained,from the obtained dynamic obstacle information through sensors.Many simulation experiments have shown that the algorithm has a fast convergence speed and strong search capability.Even in complex environments which have static and dynamic obstacles,it can avoid collision safely and plan an optimal path rapidly at the same time.

Key words: dynamic environments, robot path planning, multi-artificial fish-swarm, the rule-base of collision avoidance

中图分类号: