论文标题
通过动态系统的学习障碍功能，用于受限的运动计划
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
论文作者
论文摘要
稳定的动态系统是实时计划机器人运动的灵活工具。在机器人文献中，通常会计划动态系统运动，而无需考虑机器人工作空间中的可能局限性。这项工作提出了一种新的方法，可以从人类的示范中学习工作空间约束，并为受约束工作空间的机器人产生运动轨迹。训练数据将逐步聚集到不同的线性子空间中，并用于拟合每个子空间的低维表示。通过将学习的约束子空间视为零屏障功能，我们可以设计一个控制输入，以使系统轨迹保持在学习的界限内。该控件输入有效地与原始系统动力学结合，从而保留了无约束系统的最终渐近特性。对真正机器人的模拟和实验显示了所提出的方法的有效性。
Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robot's workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.
