Abstract
Efficient parallel algorithms for computing the kinematics, dynamics, and Jacobian of manipulators and their corresponding inverses are discussed and analyzed, and their characteristics are identified based on the type and degree of parallelism, uniformity of the operations, fundamental operations, data dependencies, and communication requirements. Most of the algorithms for robotic computations possess highly regular properties and some common structures, especially the linear recursive structure. They are well-suited to be implemented on a single-instruction-stream-multiple-data-stream (SIMD) computer with reconfigurable interconnection networks. A reconfigurable dual-network SIMD machine with internal direct feedback that best matches these characteristics has been designed. To achieve high efficiency in the computation of robotics algorithms on the proposed parallel machine, a generalized cube interconnection network is proposed. A centralized network switch control scheme is developed to support the pipeline timing of this machine. To maintain high reliability in the overall system, a fault-tolerant generalized cube network is designed to improve the original network. >
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