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首页> 外文期刊>Robotics, IEEE Transactions on >Kinematic Control of Redundant Manipulators: Generalizing the Task-Priority Framework to Inequality Task
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Kinematic Control of Redundant Manipulators: Generalizing the Task-Priority Framework to Inequality Task

机译：冗余机械手的运动学控制：将任务优先级框架推广到不平等任务

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摘要

Redundant mechanical systems like humanoid robots are designed to fulfill multiple tasks at a time. A task, in velocity-resolved inverse kinematics, is a desired value for a function of the robot configuration that can be regulated with an ordinary differential equation (ODE). When facing simultaneous tasks, the corresponding equations can be grouped in a single system or, better, sorted in priority and solved each in the solutions set of higher priority tasks. This elegant framework for hierarchical task regulation has been implemented as a sequence of least-squares problems. Its limitation lies in the handling of inequality constraints, which are usually transformed into more restrictive equality constraints through potential fields. In this paper, we propose a new prioritized task-regulation framework based on a sequence of quadratic programs (QP) that removes the limitation. At the basis of the proposed algorithm, there is a study of the optimal sets resulting from the sequence of QPs. The algorithm is implemented and illustrated in simulation on the humanoid robot HRP-2.
机译：诸如人形机器人之类的冗余机械系统旨在一次完成多项任务。速度分解逆运动学中的一项任务是所需的机器人功能函数值，可以用一个常微分方程（ODE）进行调节。面对同时执行的任务时，可以将相应的方程式分组到单个系统中，或者更好地按优先级排序并在更高优先级任务的解决方案集中求解每个方程。这种用于分层任务调节的优雅框架已实现为一系列最小二乘问题。它的局限性在于对不平等约束的处理，这些不平等约束通常通过潜在字段转换为更具约束性的平等约束。在本文中，我们提出了一个新的优先任务调整框架，该框架基于一系列二次程序（QP）消除了限制。在提出的算法的基础上，对QP序列产生的最优集合进行了研究。该算法在类人机器人HRP-2上的仿真中实现和说明。

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