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王鹏杰,李丹,付金岗,等. 融合改进RRT和TEB算法的移动焊接机器人路径规划研究[J]. 安徽工业大学学报(自然科学版),2024,41(3):1-7. doi: 10.12415/j.issn.1671-7872.23166
引用本文: 王鹏杰,李丹,付金岗,等. 融合改进RRT和TEB算法的移动焊接机器人路径规划研究[J]. 安徽工业大学学报(自然科学版),2024,41(3):1-7. doi: 10.12415/j.issn.1671-7872.23166
WANG Pengjie, LI Dan, FU Jingang, GONG Xu, ZHAO Wenjie. Research on Path Planning of Mobile Welding Robot Based on Fusion Algorithms of Improved RRT and TEB[J]. Journal of Anhui University of Technology(Natural Science). DOI: 10.12415/j.issn.1671-7872.23166
Citation: WANG Pengjie, LI Dan, FU Jingang, GONG Xu, ZHAO Wenjie. Research on Path Planning of Mobile Welding Robot Based on Fusion Algorithms of Improved RRT and TEB[J]. Journal of Anhui University of Technology(Natural Science). DOI: 10.12415/j.issn.1671-7872.23166

融合改进RRT和TEB算法的移动焊接机器人路径规划研究

Research on Path Planning of Mobile Welding Robot Based on Fusion Algorithms of Improved RRT and TEB

  • 摘要: 针对移动焊接机器人路径规划中快速遍历随机树(RRT)算法搜索时间较长、易生成曲折且避障性能差的路径,时间弹性带(TEB)算法存在速度输出跳变等不足,提出1种改进RRT和TEB的移动焊接机器人路径规划融合算法。引入目的性扩展和关键点提取策略改进RRT算法,提高算法的搜索效率和规划路径的平滑性;引入最小安全距离对速度的约束改进TEB算法,使移动焊接机器人能够有效避开障碍物,同时增加目标点对速度的约束,减少对移动焊接机器人的冲击;融合改进的RRT和TEB算法,使移动焊接机器人能够动态规划路径,避免动态障碍物的干扰,且对其进行仿真和实物实验验证。结果表明:改进RRT算法的路径搜索时间减少了58.84%,路径拐点数减少了73.33%;采用融合改进的RRT与TEB算法用于移动焊接机器人的路径规划,机器人与障碍物之间的安全距离增加了50%,在到达目标点前可提供足够的速度缓冲时间,极大提高了机器人运行的稳定性和避障能力。

     

    Abstract: For the path planning of mobile welding robots, the fast traversing random tree (RRT) algorithm has a long search time, is prone to generating winding paths, and has poor obstacle avoidance performance, and the time elastic band (TEB) algorithm has shortcomings such as speed output jumps, a fusion algorithm for mobile welding robot path planning with improved RRT and TEB was proposed. The purposeful extension and key point extraction strategies were introduced to improve the RRT algorithm, and enhance its search efficiency and smoothness in path planning. Furthermore, a minimum safety distance was introduced to improve the TEB algorithm,which enabled the mobile welding robot to effectively avoid obstacles, while increasing the constraint of target points on speed and reducing the impact on the mobile welding robot. The fusion of improved RRT and TEB algorithms enabled mobile welding robots to dynamically plan paths, and to avoid interference from dynamic obstacles, and simulation and physical experiments was conducted to verify it. The results demonstrate that the improved RRT algorithm reduces path search time by 58. 84%, and decreases the number of path inflection points by 73. 33%. The fusion of improved RRT and TEB algorithms can increase the safe distance between the mobile welding robot and obstacles by 50%, providing sufficient speed buffering time before reaching the target point, greatly improving the stability and obstacle avoidance ability of the mobile welding robot.

     

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