用学习方法避免机器人在模型不准时陷入失控奇点
Singularity-Avoidance Control of Robotic Systems with Model Mismatch and Actuator Constraints
- 用高斯过程学习未知模型误差，保证预测精度上限
- 设计可满足执行器限制的控制屏障函数，确保安全
- 适用于存在模型偏差和执行器约束的机械臂系统
奇异点作为特殊构型状态会恶化机器人性能，甚至导致控制失效。本文针对存在模型失配和执行器约束的机器人系统，提出基于控制屏障函数（CBFs）的奇点规避控制策略。通过高斯过程（GP）回归学习未知模型失配，其预测误差被限定在确定性边界内。同时，给出了参数选择准则，以保证在执行器约束下CBF的可行性。所提方法在2自由度平面机器人上进行了高保真仿真验证。
原文摘要 · Abstract (English)
Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a learning-based control strategy to prevent robots entering singularity regions. More precisely, we leverage Gaussian process (GP) regression to learn the unknown model mismatch, where the prediction error is restricted by a deterministic bound. Moreover, we offer the criteria for parameter selection to ensure the feasibility of CBFs subject to actuator constraints. The proposed approach is validated by high-fidelity simulations on a 2 degrees-of-freedom (DoFs) planar robot.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。