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强化学习增强了伪反向方法,以避免冗余机器人的自我碰撞
Tinghe Hong1, Weibing Li1, Kai Huang1
1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Frontiers in neurorobotics
|April 12, 2024
概括
本研究引入了强化学习 (RL) 增强的伪反向方法,以防止冗余机器人的自我碰撞. 这种方法有效地减轻了碰撞风险,提高了机器人系统的安全性和性能.
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:
- 冗余机器人提供了更大的灵活性,但面临着更大的自我碰撞风险.
- 在冗余机器人中选择逆动力学 (IK) 解决方案以避免碰撞是具有挑战性的,因为解决方案的可能性是无限的.
研究的目的:
- 提出强化学习 (RL) 增强的伪反向方法,以避免冗余机器人的自我碰撞.
- 为有效的IK解决方案选择,将RL代理集成到冗余解决过程中.
主要方法:
- 一种伪反向方法用强化学习 (RL) 代理来增强.
- 机器人执行任务时,RL代理确定IK解决方案,以避免机器人执行任务时自我碰撞.
- 使用改进的重播缓冲器来提高RL算法性能.
主要成果:
- 模拟和实验验证证证实了该方法的有效性.
- 拟议的方法大大降低了冗余机器人自我碰撞的风险.
结论:
- 用RL增强的伪反向方法显示出减轻自我碰撞风险的潜力.
- 这种方法提高了机器人系统的安全性和性能.
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