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在机器人手臂中进行轨迹规划的新框架,通过将动态运动原体与粒子群集优化集成而开发出来
Guanghui Dai1, Qingqing Zhang2, Bing Xu2
1School of Mechanical Engineering, Chaohu University, Hefei, 238024, China. daiguanghui1987@126.com.
Scientific reports
|August 13, 2025
概括
这项研究引入了机器人手臂轨迹规划的新框架,集成动态运动原始 (DMP) 和粒子群优化 (PSO). DMP-PSO方法提高了机器人在协作任务中的障碍回避和适应能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 人机协作需要先进的模仿学习和机器人手臂的自主适应.
- 当前的轨迹规划方法在动态环境和复杂任务中面临挑战.
研究的目的:
- 提出一个新的框架,DMP-PSO,用于增强机器人手臂的轨迹规划.
- 提高自主适应和避开障碍的能力.
主要方法:
- 整合动态运动原体 (DMP) 进行轨迹学习和概括.
- 使用粒子群集优化 (PSO) 以增强的初始状态和搜索区域来实现快速避障轨迹生成.
- 开发DMP-PSO框架,用于自主生成各种机器人臂轨迹.
主要成果:
- 数字模拟表明DMP-PSO具有良好的适应性,强大的一致性和提高效率.
- 虚拟和实体实验验证实了框架的稳定性和实用性.
- 该框架成功地产生了各种障碍回避轨迹.
结论:
- 拟议的DMP-PSO框架有效地解决了人机协作中的轨迹规划挑战.
- 整合DMP和PSO为机器人手臂控制提供了强大的和实用的解决方案.
- 该框架显示了在复杂场景中改善机器人手臂性能的巨大潜力.
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