在线学习补偿不确定动力学，让欠驱动机器人精准完成摆动动作。
On-Line Learning for Planning and Control of Underactuated Robots with Uncertain Dynamics
- 通过在线学习估计模型不确定性对主动与被动自由度的扰动。
- 仅需少量迭代即可生成可行轨迹并实现高精度跟踪控制。
- 适合需要应对大模型不确定性的欠驱动系统控制场景。
我们提出一种用于欠驱动机器人在动力学不确定情况下的运动规划与控制的迭代方法。核心是学习过程，用于估计模型不确定性对主动和被动自由度所引起的扰动。算法通用迭代中，利用学习到的数据，在基于优化的规划阶段与通过在线更新模型进行部分反馈线性化的控制阶段协同作用。通过在Pendubot上执行多种摆起操作的对比仿真与实验，验证了该方法的有效性。通常只需极少迭代次数，即可生成动态可行轨迹，并保证其准确执行，即使存在较大的模型不确定性。
原文摘要 · Abstract (English)
We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.
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