计算机与现代化 ›› 2023, Vol. 0 ›› Issue (07): 48-53.doi: 10.3969/j.issn.1006-2475.2023.07.009

• 人工智能 • 上一篇    下一篇

密集障碍环境下的改进DWA避障算法

  

  1. (青岛科技大学自动化与电子工程学院,山东 青岛 266042)
  • 出版日期:2023-07-26 发布日期:2023-07-27
  • 作者简介:邓云峥(1995—),男,山东德州人,硕士研究生,研究方向:自主导航及智能控制,E-mail: dyz1970109@163.com; 黄翼虎(1970—),男,湖北荆州人, 副教授,博士,研究方向:化工自动化仪表,数字信号处理,检测理论及应用,路径规划等,E-mail: hyhuzi@163.com。

Improved DWA Obstacle Avoidance Algorithm in Dense Obstacle Environment

  1. (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, China)
  • Online:2023-07-26 Published:2023-07-27

摘要: 针对传统动态窗口法(DWA)算法在密集障碍环境中容易绕行障碍物区域和避障性差等问题,提出一种基于A*的改进DWA算法。首先在A*算法的评价函数中引入偏移代价来引导算法快速朝目标方向搜索,改善规划效率低的问题,并对路径点进行优化得到全局最优路径点。其次在DWA算法中通过障碍物方位和距离动态调整评价函数各项权值,解决算法在密集障碍环境的避障性差问题。最后融入全局最优路径点,确保改进DWA算法能够在实现实时避障的同时保证路径最优。仿真结果显示,相比于其他2种算法,改进DWA算法可以有效提高机器人在密集障碍环境下的避障性,路径长度和行进步数均可降低15%以上,且能够有效躲避随机障碍物,安全性更高,鲁棒性更强。

关键词: 密集障碍环境, 避障, DWA算法, 模糊逻辑系统, 路径规划

Abstract: Aiming at the problems of the traditional dynamic window approach(DWA) algorithm in the dense obstacle environment, such as easy to bypass the obstacle area and poor obstacle avoidance, an improved DWA algorithm based on A* is proposed. Firstly, the offset cost is introduced into the evaluation function of A* algorithm to guide the algorithm to search in the target direction quickly, so as to improve the problem of low planning efficiency, and the global optimal waypoint is obtained by optimizing the waypoint. Secondly, the weights of the evaluation function are dynamically adjusted by the orientation and distance of obstacles in the DWA algorithm to solve the problem of poor obstacle avoidance in the dense obstacle environment. Finally, the global optimal waypoint is incorporated to ensure that the improved DWA algorithm can achieve real-time obstacle avoidance and ensure the optimal path. The simulation results show that compared with the other two algorithms, the improved DWA algorithm can effectively improve the robot’s obstacle avoidance in the dense obstacle environment, the path length and the number of steps can be decreased by more than 15%, and can effectively avoid random obstacles, with higher security and stronger robustness.

Key words: dense obstacle environment, obstacle avoidance, DWA algorithm, fuzzy logic system, path planning

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