在机器人操纵中,生物启发的轨迹调制可用于有效的滑动控制
Kiyanoush Nazari1, Willow Mandil1,2, Marco Santello3
1School of Computer Science and LIAT, University of Lincoln, Lincoln, UK.
Nature machine intelligence
|July 25, 2025
概括
机器人抓地稳定性可以通过轨迹调制而不是传统的抓地力控制来提高. 这种新方法通过预测控制框架进行了优化,提高了机器人在具有挑战性的环境中的机器人适应能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 稳定的机器人抓取对于熟练的操纵至关重要.
- 传统的滑动控制依赖于抓地力调制.
- 对于某些任务,握力控制存在限制.
研究的目的:
- 研究轨迹调制作为机器人操纵的替代滑动控制策略.
- 为了比较轨迹调制与握力控制的有效性.
- 使用预测控制框架开发一个优化的轨迹调节政策.
主要方法:
- 基于轨道调制的滑动控制政策的制定.
- 与传统的基于抓地力控制方法的比较.
- 在模型预测控制 (MPC) 框架中整合数据驱动的动作条件期货模型.
主要成果:
- 在特定的机器人操纵场景中,轨迹调制显著优于抓地力控制.
- 包含前模型的预测控制框架是优化轨迹调节的关键.
- 拟议的方法增强了动态和非结构化的环境中的掌握稳定性.
结论:
- 轨道调制为机器人操纵中防滑提供了一个强大而有效的替代方案.
- 用数据驱动的前模型进行模型预测控制对于优化这一策略至关重要.
- 这种方法提高了机器人系统在具有挑战性的条件下的适应性和性能.
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