学习强大的自主导航和轮腿机器人的机动运动
Joonho Lee1, Marko Bjelonic1, Alexander Reske1
1Robotic Systems Lab, ETH Zurich, Zurich, Switzerland.
Science robotics
|April 24, 2024
概括
自主轮腿机器人使用层次增强学习 (RL) 来实现强大的城市导航. 这种综合系统可以实现高效的机动和避开障碍,为先进的最后一英里配送解决方案铺平了道路.
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
- 城市系统 城市系统
背景情况:
- 城市自主导航在不同的地形和动态障碍物中面临着挑战.
- 有轮腿的机器人为增强后勤和适应性提供了潜力.
- 现有的系统往往缺乏集成的机动和导航控制.
研究的目的:
- 开发和验证一个完全集成的系统,用于在城市环境中自主驾驶的轮腿机器人导航.
- 在复杂的现实环境中提高机器人的适应性和效率.
- 为了证明对机器人控制的等级强化学习的有效性.
主要方法:
- 使用无模型强化学习 (RL) 和特权学习,用于多功能机动控制器.
- 实施了一个分层的RL框架,整合了机动和导航控制器.
- 开发了具有移动意识的本地导航和大规模路径规划模块.
- 通过在城市地区的自主,千米级导航任务验证了该系统.
主要成果:
- 通过无的模式转换 (行走/驾驶) 在各种崎的地形上实现了高效和强大的机动.
- 通过具有挑战性的地形和动态障碍物证明了有效的高速导航.
- 在苏黎世和塞维利亚成功完成了自主导航任务,验证了系统的稳定性和适应性.
结论:
- 综合控制系统对于在复杂的城市环境中无自主导航至关重要.
- 有轮腿的机器人和层次的RL是可行的,用于先进的自主导航.
- 开发的系统对最后一英里的交货和未来的物流有重大影响.
相关概念视频
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01:21Rolling Resistance
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