Mobile Robot Path Planning Based on Enhanced Dynamic Window Approach and Improved A ∗ Algorithm

IF 1.4 Q4 ROBOTICS
Hong Yang, Xingqiang Teng
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引用次数: 9

Abstract

Path planning is one of the most popular researches on mobile robots, and it is the key technology to realize autonomous navigation of robots. Aiming at the problem that the mobile robot may collide or fail along the planned path in an environment with random obstacles, a robot path planning scheme that combines the improved A ∗ algorithm with an enhanced dynamic window method is proposed. In the improved A ∗ algorithm, in order to improve the algorithm efficiency, so that a single planning path can pass through multiple target points, the search point selection strategy and evaluation function are optimized. In order to achieve local obstacle avoidance and pursuit of dynamic target points in dynamic and complex environments, an online path planning method combining enhanced dynamic window algorithm and global path planning information is proposed. The preview deviation angle tracking method is used to successfully capture moving target points. It also improves the efficiency of path planning and ensures that on the basis of the global optimal path, the random obstacle can be avoided in real time so that the robot can reach the target point smoothly. The simulation results show that compared with other methods, the proposed method achieves excellent global and local path planning performance, the planned trajectory is smoother, and the search efficiency is higher in complex environments.
基于增强动态窗口法和改进A *算法的移动机器人路径规划
路径规划是移动机器人研究的热点之一,是实现机器人自主导航的关键技术。针对移动机器人在随机障碍物环境中沿规划路径可能发生碰撞或失效的问题,提出了一种将改进的a *算法与增强的动态窗口法相结合的机器人路径规划方案。在改进的A *算法中,为了提高算法效率,使单个规划路径能够通过多个目标点,对搜索点选择策略和评价函数进行了优化。为了在动态复杂环境中实现局部避障和动态目标点的追踪,提出了一种结合增强型动态窗口算法和全局路径规划信息的在线路径规划方法。采用预览偏差角跟踪方法,成功捕获运动目标点。提高了路径规划的效率,保证在全局最优路径的基础上实时避开随机障碍物,使机器人顺利到达目标点。仿真结果表明,与其他方法相比,所提方法具有优异的全局和局部路径规划性能,规划的轨迹更加平滑,在复杂环境下的搜索效率更高。
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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