论文标题
预测撞击引起的关节速度在运动学控制器上的跳跃
Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator
论文作者
论文摘要
为了实现实用的机器人影响任务，预测联合速度跳跃对于执行控制器的可行性和硬件完整性至关重要。我们观察到与熊猫机械手的250个基准实验相比，机器人技术中常用方法的相当大的预测误差。我们将平均预测误差减少81.98％，如下所示：首先，我们专注于任务空间方程，而不反转未解决的关节空间惯性矩阵。其次，在影响事件发生之前，我们考虑了高增长（刚性）运动学控制的操纵器的表现，就像复合刚性的身体一样。
In order to enable on-purpose robotic impact tasks, predicting joint-velocity jumps is essential to enforce controller feasibility and hardware integrity. We observe a considerable prediction error of a commonly-used approach in robotics compared against 250 benchmark experiments with the Panda manipulator. We reduce the average prediction error by 81.98% as follows: First, we focus on task-space equations without inverting the ill-conditioned joint-space inertia matrix. Second, before the impact event, we compute the equivalent inertial properties of the end-effector tip considering that a high-gains (stiff) kinematic-controlled manipulator behaves like a composite-rigid body.
