合规运动规划 整合人类技能用于机器人手臂 收集基于改进的DDPG的番茄束
Yifan Zhang1,2, Yajun Li2, Qingchun Feng2,3
1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Plants (Basel, Switzerland)
|March 17, 2025
概括
这项研究引入了一种新的机器人收获方法,使用人类技能转移和增强的深度决定性政策梯度 (DDPG) 来进行微妙的番茄采集. 这种方法显著提高了机器人在农业任务中的性能和准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 农业工程 农业工程
- 机器学习 机器学习
背景情况:
- 巧妙的操作和逐步放置对于收获期间的果实完整性至关重要.
- 传统的路径规划与农业机器人的学习手册合规技能相斗争.
研究的目的:
- 开发一种使用人机技能转移和深度决定性政策梯度 (DDPG) 的新的番茄束收集方法.
- 通过人类技能重复,专家奖励回归和动态步骤长度回报来提高DDPG的性能.
主要方法:
- 人类示范路径通过手动引导机器人手臂来记录.
- 开发了一种增强的DDPG-Z模型,其中包含了人类技能重复进行预训练.
- 该代理被训练以最大限度地减少路径偏差,接近人类操作.
主要成果:
- 在模拟中实现了25%的收速度改善和10.3%的收后奖励增加.
- 与没有演示的方法相比,目标准确度提高了51.3%.
- 经典模型 (DDPG,SAC,TD3) 在设定插曲中未能趋同.
结论:
- 拟议的方法有效地将人类技能转移到用于农业任务的机器人系统.
- 这种方法在符合机器人操作的趋同,奖励和准确性方面提供了显著的改进.
- 为提高农业机器人的合规运行性能提供了宝贵的见解.
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