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- W4244607148 abstract "This chapter presents definitions, assumptions, and modeling of the kinematics used to lay a foundation for investigations. It constructs the problem formulation and presents the associated distributed scheme. The chapter presents a noise-tolerant zeroing neural network (NTZNN) model to solve the minimum velocity norm (MVN)-oriented distributed scheme for the motion generation of multiple redundant robot manipulators with guaranteed convergence and robustness. There are two procedures for a distributed network of redundant manipulators cooperatively transporting a payload. In the first procedure each end-effector of all manipulators should reach a desired configuration for holding a different place or a handle on the payload. In the second procedure each end-effector of all manipulators should keep a desired format during the execution to fulfill certain tasks. Simulations and comparisons based on PUMA 560 redundant robot manipulators are illustrated to verify the effectiveness and accuracy of the proposed distributed scheme with the aid of the NTZNN model." @default.
- W4244607148 created "2022-05-12" @default.
- W4244607148 date "2019-02-15" @default.
- W4244607148 modified "2023-09-26" @default.
- W4244607148 title "Zeroing Neural Networks for Robot Arm Motion Generation" @default.
- W4244607148 doi "https://doi.org/10.1002/9781119557005.ch9" @default.
- W4244607148 hasPublicationYear "2019" @default.
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