通过估计环境物理特性，实现机器人在不同场景下的自适应位置与力控制。
An Environment-Adaptive Position/Force Control Based on Physical Property Estimation
- 基于两组预录动作数据，实时匹配环境阻抗生成控制指令。
- 在极端阻抗条件下验证，相比现有系统提升动作复现精度。
- 仅需少量数据且不依赖训练，适合工业部署和稳定性要求高场景。
当前在显著不同环境下生成机器人动作的方法存在局限性。本文提出一种新方法，通过将两组预录动作数据的阻抗与当前环境阻抗匹配，生成高度自适应的动作。该方法根据实时环境阻抗重新计算位置与力的控制指令，提升了在不同环境中的动作复现性。在极端动作阻抗（如位置与力控制）条件下进行实验，验证了所提方法优于现有运动复现系统。该方法仅需两组动作数据，显著降低数据采集负担，且因采用已有稳定控制系统，避免了学习类方法的稳定性风险。本研究提升了机器人在复杂环境中的适应能力，同时简化了动作生成流程。
原文摘要 · Abstract (English)
The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position and force control, confirmed the superiority of the proposed method over existing motion reproduction system. The advantages of this method include the use of only two sets of motion data, significantly reducing the burden of data acquisition compared with machine-learning based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving the environmental adaptability of robots while simplifying the action generation method.
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