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首页> 外文期刊>International journal of systems science >Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators
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Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

机译：机器人机械手加速级避障方案的动态设计，数值解和有效验证

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摘要

For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important 'bridge' theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and window-shaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.
机译：为了避免机器人操纵器的障碍物和关节物理约束，本文提出并研究了一种新颖的障碍物避免方案（称为加速级障碍物避免方案）。该方案基于首次使用梯度神经网络方法设计的新的避障标准。另外，这种关节方案包括关节物理限制，例如关节角度限制，关节速度限制和关节加速度限制，并进一步重新设计为二次规划（QP）。建立了两个重要的“桥梁”定理，以便可以将此类QP等效转换为线性变分不等式，然后等效转换为分段线性投影方程（PLPE）。因此，开发了基于PLPE的数值算法，并将其应用于所得QP的在线解决方案。在存在点和窗口形障碍物的情况下，基于PA10机器人的四个路径跟踪任务演示并验证了加速度级避障方案的有效性和准确性。此外，非障碍回避和障碍回避结果之间的比较进一步验证了该方案的优越性。

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