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|Title:||Towards piecewise-linear primal neural networks for optimization and redundant robotics||Authors:||Zhang, Y.||Issue Date:||2006||Citation:||Zhang, Y. (2006). Towards piecewise-linear primal neural networks for optimization and redundant robotics. Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06 : 374-379. ScholarBank@NUS Repository.||Abstract:||Motivated by handling joint physical limits, environmental obstacles and various performance indices, researchers have developed a general quadratic-programming (QP) formulation for the redundancy resolution of robot manipulators. Such a general QP formulation is subject to equality constraint, inequality constraint and bound constraint, simultaneously. Each of the constraints has interpretably physical meaning and utility. Motivated by the real-time solution to the robotic problems, dynamic system solvers in the form of recurrent neural networks (RNN) have been developed and employed. This is in light of their parallel-computing nature and hardware implementability. In this paper, we have reviewed five RNN models, which include state-of-the-art dual neural networks (DNN) and LVI-based primal-dual neural networks (LVI-PDNN). Based on the review of the design experience, this paper proposes the concept, requirement and possibility of developing a future recurrent neural network model for solving online QP problems in redundant robotics; i.e., a piecewise-linear primal neural network. © 2006 IEEE.||Source Title:||Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06||URI:||http://scholarbank.nus.edu.sg/handle/10635/72057||ISBN:||1424400651|
|Appears in Collections:||Staff Publications|
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