论文标题
基于任务的机器人轨迹计划的自适应跟踪控制
Adaptive tracking control for task-based robot trajectory planning
论文作者
论文摘要
本文介绍了 - 从演示中学习 - 执行机器人运动轨迹的方法，这些轨迹可以在您进行时定义。这样可以执行非结构化任务，而无需准确知道所有任务并事先开始和结束位置。长期的目标是使残疾儿童能够控制机器人在游戏环境中操纵玩具，并使帮助人在游戏任务发生变化时展示所需的轨迹。 Novint制造的相对便宜的3-DOF触觉设备用于执行最终效应器轨迹的任务。在最终效应器承载不同负载的情况下，常规控制系统具有无法补偿负载变化效应的潜在问题。自适应跟踪控制可以处理上述问题。使用Lyapunov稳定性理论，得出了一组更新定律，以通过适当的跟踪性能给出闭环稳定性。
This paper presents a -- Learning from Demonstration -- method to perform robot movement trajectories that can be defined as you go. This way unstructured tasks can be performed, without the need to know exactly all the tasks and start and end positions beforehand. The long-term goal is for children with disabilities to be able to control a robot to manipulate toys in a play environment, and for a helper to demonstrate the desired trajectories as the play tasks change. A relatively inexpensive 3-DOF haptic device made by Novint is used to perform tasks where trajectories of the end-effector are demonstrated and reproduced. Under the condition where the end-effector carries different loads, conventional control systems possess the potential issue that they cannot compensate for the load variation effect. Adaptive tracking control can handle the above issue. Using the Lyapunov stability theory, a set of update laws are derived to give closed-loop stability with proper tracking performance.
