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改进了机器人手臂抓取轨迹规划和真实机器人迁移的PPO优化.
Chunlei Li1,2, Zhe Liu2, Liang Li1,2
1Shaanxi Key Laboratory of Advanced Manufacturing and Evaluation of Robot Key Components, Baoji 721016, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种混合增强学习方法,将模拟化 (SA) 和近距离政策优化 (PPO) 结合起来,用于机器人手臂轨迹规划. 这种新的方法可以在复杂的环境中增强对象抓取,从而实现更高的成功率和效率.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 机器学习 机器学习
- 控制系统 控制系统
背景情况:
- 在非结构化环境中的机器人操纵面临着局部最佳和融合问题等挑战.
- 实时交互和适应性对于工业自动化至关重要.
研究的目的:
- 开发一种混合增强学习框架,用于精确,无碰撞的机器人手臂轨迹规划.
- 为了增强在动态障碍物中随机出现的对象的把握.
主要方法:
- 一种混合方法,将模拟化 (SA) 与近接政策优化 (PPO) 结合起来.
- 一个概率增强的模拟环境,具有20%的障碍生成率.
- 一个优化的状态-动作空间,具有12维环境编码和6-DoF联合控制.
主要成果:
- 与基线PPO相比,成功率提高了6.52% (98%与92%相比).
- 每组减少了7.14%的步骤.
- 在AUBO-i5机器人手臂上验证了强大的模拟到现实转移.
结论:
- 在SA-PPO算法有效平衡探索和融合适应性机器人操纵.
- 这项研究为实时机器人响应工业环境中的环境不确定性建立了新的范式.
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