在深度强化学习的基础上优化机器人任务顺序和轨迹规划
Xiaoting Dong1,2,3,4, Guangxi Wan1,2,3, Peng Zeng1,2,3
1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
本研究介绍了一个统一的机器人任务序列和轨迹规划 (TSTP) 模型,通过深度强化学习 (DRL) 来解决它. 与传统方法相比,DRL方法显著减少了能源消耗和计算时间.
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
- 优化优化 优化优化
背景情况:
- 传统的机器人优化将任务顺序和轨迹规划分开,导致次优化解决方案.
- 这种顺序方法忽略了这些关键机器人问题之间的协同效应.
研究的目的:
- 开发一个协同优化模型,整合机器人的任务顺序和轨迹规划,称为TSTP问题.
- 为解决集成TSTP问题提出深度强化学习 (DRL) 方法.
主要方法:
- 制定了机器人任务顺序和轨迹规划问题作为一个统一的TSTP问题.
- 模拟了TSTP优化作为一个马尔科夫决策过程.
- 开发并应用了深度强化学习 (DRL) 算法来解决TSTP问题.
主要成果:
- 与传统进化算法相比,DRL方法实现了30.54%的能源节约.
- 与顺序优化相比,TSTP模型显示了18.22%的能源减少.
- 与进化算法相比,DRL方法显著减少了计算时间.
结论:
- 集成的TSTP模型有效地解决了机器人技术中顺序优化的局限性.
- 拟议的DRL方法为机器人任务排序和轨迹规划提供了计算效率高和节能解决方案.
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