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Efficient parallel algorithms for robot forward dynamics computation

机译:用于机器人前向动力学计算的高效并行算法

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Two efficient parallel algorithms for computing the forward dynamics for real-time simulation were developed for implementation on a single-instruction multiple-data-stream (SIMD) computer with n processors, where n is the number of degrees of freedom of the manipulator. The first parallel algorithm, based on the composite rigid-body method, generates the inertia matrix using the parallel Newton-Euler algorithm, the parallel linear recurrence algorithm, and the modified row-sweep algorithm, and then inverts the inertia matrix to obtain the joint acceleration vector at time t. The time complexity of this parallel algorithm is of the order O(n/sup 2/) with O(n) processors. The second parallel algorithm, based on the conjugate gradient method, computes the joint acceleration with a time complexity of O(n) for multiplication operation and O(n log/sub 2) for addition operation. The interprocessor communication problem for the implementation of the proposed parallel algorithms on SIMD machines is also discussed and analyzed.
机译:开发了两种有效的并行算法来计算正向动力学以进行实时仿真,以在具有n个处理器的单指令多数据流(SIMD)计算机上实现,其中n是操纵器的自由度数。第一种基于复合刚体方法的并行算法,使用并行牛顿-欧拉算法,并行线性递归算法和改进的行扫描算法生成惯性矩阵,然后将惯性矩阵求逆以获得联合在时间t的加速度矢量。对于O(n)个处理器,此并行算法的时间复杂度约为O(n / sup 2 /)。第二种基于共轭梯度法的并行算法,以乘法运算的时间复杂度为O(n),加法运算的时间复杂度为O(n log / sub 2 / n)计算联合加速度。还讨论和分析了在SIMD机器上实现所提出的并行算法的处理器间通信问题。

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