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Prioritized motion-force control of constrained fully-actuated robots: 'Task Space Inverse Dynamics'

机译:受约束的全驱动机器人的优先运动力控制:“任务空间逆动力学”

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We present a new framework for prioritized multi-task motion/force control of fully-actuated robots. This work is established on a careful review and comparison of the state of the art. Some control frameworks are not optimal, that is they do not find the optimal solution for the secondary tasks. Other frameworks are optimal, but they tackle the control problem at kinematic level, hence they neglect the robot dynamics and they do not allow for force control. Still other frameworks are optimal and consider force control, but they are computationally less efficient than ours. Our final claim is that, for fully-actuated robots, computing the operational-space inverse dynamics is equivalent to computing the inverse kinematics (at acceleration level) and then the joint-space inverse dynamics. Thanks to this fact, our control framework can efficiently compute the optimal solution by decoupling kinematics and dynamics of the robot. We take into account: motion and force control, soft and rigid contacts, free and constrained robots. Tests in simulation validate Our control framework, comparing it with other state-of-the-art equivalent frameworks and showing remarkable improvements in optimality and efficiency. (C) 2014 Elsevier B.V. All rights reserved.
机译:我们提出了一个新的框架,用于优先控制全驱动机器人的多任务运动/力控制。这项工作建立在对现有技术的仔细审查和比较之后。一些控制框架不是最佳的,也就是说,它们找不到次要任务的最佳解决方案。其他框架是最佳的,但是它们在运动学水平上解决了控制问题,因此它们忽略了机器人动力学,并且不允许进行力控制。还有其他框架是最佳的,并且考虑了力控制,但是它们的计算效率比我们的低。我们的最终主张是,对于全驱动机器人,计算操作空间逆动力学等效于计算逆运动学(在加速级别),然后计算关节空间逆动力学。由于这个事实,我们的控制框架可以通过将机器人的运动学和动力学解耦来有效地计算最佳解决方案。我们考虑到以下因素:运动和力控制,软和刚性接触,自由和受约束的机器人。仿真测试验证了我们的控制框架,并将其与其他最新的等效框架进行了比较,并显示出优化性和效率的显着提高。 (C)2014 Elsevier B.V.保留所有权利。

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