基于自我优化重播机制的动态环境中的机器人手臂轨迹规划
Pengyao Xu1, Chong Di1, Jiandong Lv2
1Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种基于神经网络的新型专家指导的三重体验重复机制 (NETM),以改善机器人臂在动态环境中的轨迹规划的深度强化学习,提高准确性和安全性.
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
- 机器学习 机器学习
背景情况:
- 在动态环境中的机器人手臂面临着诸如实时变化和不确定性等挑战.
- 轨迹规划的深度强化学习 (DRL) 与专家战略获取,低经验利用和奖励函数设计的斗争.
研究的目的:
- 为了解决DRL在机器人手臂轨迹规划中的局限性.
- 在复杂的动态环境中改进融合速度和性能.
主要方法:
- 设计了一个基于神经网络的专家指导的三重体验重复机制 (NETM).
- 开发了一个改进的奖励功能,为动态环境量身定制.
- 集成模仿学习与DLR,以优化体验重复.
主要成果:
- NETM将有限的专家演示和算法成功扩展到优化的专家体验中.
- 实验结果表明,在动态情景中,趋同加速.
- 与基线算法相比,NETM的准确性提高了30%以上,安全率提高了2.28%.
结论:
- 拟议的NETM有效地增强了DRL用于机器人手臂轨迹规划.
- 该方法在动态环境中显著提高了性能和趋同.
- 对于面临不确定性的现实世界机器人应用,NETM提供了一个可行的解决方案.
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