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Operation of the Collaborative Composite Manufacturing CCM System
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在移动机器人路径规划中提高稳定性和性能,使用PMR-Dueling DQN算法.
Demelash Abiye Deguale1, Lingli Yu1, Melikamu Liyih Sinishaw2
1School of Automation, Central South University, Changsha 410083, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种改进的深度强化学习算法,用于移动机器人导航. 这种新的方法提高了复杂环境中的路径规划效率和稳定性,优于传统方法.
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:
- 在复杂的环境中,移动机器人路径规划仍然是一个重大挑战.
- 现有的方法经常与动态障碍作斗争,并有效地实现最佳路径.
研究的目的:
- 引入和评估改进的机器人导航深度强化学习策略.
- 在复杂的环境中增强路径最佳性,避免碰撞和学习速度.
主要方法:
- 开发了对决深度Q网络与修改奖励和优先经验重复 (PMR-对决DQN) 算法.
- 集成的决斗架构,优先级的体验重复,以及为导航提供成形奖励.
- 将PMR-Dueling DQN与Q-learning,DQN和DDQN在网格世界和露台模拟中进行比较.
主要成果:
- PMR-Dueling DQN 显示了显著增加的趋同速度和稳定性.
- 该算法在所有测试环境中实现了卓越的性能和更高的累积奖励.
- 在路径优化和避免碰撞方面表现优于传统方法.
结论:
- 结合先进的深度强化学习技术,为机器人路径规划提供了强大的解决方案.
- PMR-Dueling DQN有效地应对复杂和动态环境带来的挑战.
- 这种方法代表了自主机器人导航的重大进步.
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