ARS:人工智能驱动的恢复控制器用于使用单一网络模型的四肢机器人
Han Sol Kang1, Hyun Yong Lee1,2, Ji Man Park1,2
1Department of Mechanical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea.
Biomimetics (Basel, Switzerland)
|December 27, 2024
概括
四肢机器人现在可以使用一种新的单个神经网络模型从跌倒中恢复. 这种由人工智能驱动的系统使机器人能够从各种翻转的位置快速恢复稳定,提高其运行弹性.
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:
- 腿类机器人,特别是四足机器人,擅长在具有挑战性的地形上进行导航.
- 跌倒对于有腿的机器人来说是一个常见的现象,需要强大的恢复机制.
- 现有的机器人摔倒恢复方法往往复杂且范围有限.
研究的目的:
- 开发一种方法,使四足机器人从堕落状态完全恢复.
- 为了利用单一的神经网络模型进行自主倒下恢复.
- 在物理机器人平台上证明拟议方法的有效性.
主要方法:
- 一个单一的神经网络模型被设计用于倒下恢复.
- 该模型在模拟中使用强化学习进行了两阶段的训练.
- 经过训练的模型直接部署在AiDIN-VIII四足机器人上 (12个自由度).
主要成果:
- 机器人在5秒内成功从各种落姿势中恢复.
- 即使机器人被完全推翻时,也实现了完全恢复.
- 该方法在从外部诱导的干扰中恢复时表现出有效性.
结论:
- 一个单一的神经网络模型可以使四足机器人从跌倒中完全恢复.
- 拟议的强化学习方法对于培养强有力的摔倒恢复政策是有效的.
- 该方法在非结构化环境中提高了腿式机器人的操作可靠性和自主性.
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