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快速路径规划用于机器人操纵器运动的动力平滑
Hui Liu1, Yunfan Li1, Zhaofeng Yang1
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种新的算法,KSBB-P-RRT*,用于机器人路径规划,该算法集成了运动控制,以防止冲击和振动. 增强的算法提高了效率,降低了路径成本,使其适合精密机器人.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 快速探索随机树星 (RRT*) 算法是机器人操纵器路径规划的常见选择.
- 然而,RRT*本质上没有解决运动控制,导致潜在的问题,如冲击和振动,降低准确性和稳定性.
研究的目的:
- 提出一种新的算法,即动态平滑,动态偏差双向潜在引导RRT* (KSBB-P-RRT*),它统一了路径规划和运动控制.
- 通过解决现有的RRT*算法的局限性,提高机器人路径规划的效率和稳定性.
主要方法:
- 开发了KSBB-P-RRT*算法,其中包含三个关键创新:使用Bi-RRT*进行快速路径搜索策略,具有自适应性采样和转向,基于三角形不等式的优化以减少路点,以及使用Jerk-Continuous S-Curve的动态约束光滑策略.
- 集成路径规划与运动控制,以确保流和可执行的轨迹.
主要成果:
- 与Bi-RRT*相比,模拟表明KSBB-P-RRT*至少减少了30%的规划时间和至少3%的路径成本.
- 该算法需要更少的代,这表明效率和有效性得到了提高.
结论:
- 该KSBB-P-RRT*算法成功地集成了路径规划和运动控制,提供更平稳,更稳定的轨迹.
- 其已证明的有效性和适合复杂,精确要求的应用,如农业机器人,突出其实际价值.
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