Time-varying linear programming via LVI-PDNN with numerical examples

Wu, Sheng; He, Ling; Zou, Maotai; Li, Jian; Zhang, Yunong

doi:10.1109/icsp.2016.7877849

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Drift-free Inverse Kinematics1

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2019

2024

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Cited by 2 publications

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“…Many dynamic solvers based on neural networks were developed. 54,55,59,113,121,133,135,[137][138][139][140][141][142][143] Neural networks of other kinds 83,144 can also be applied to solve the redundancy resolution problem. In the following sections, the main types of RNNs used as QP solvers are briefly reviewed.…”

Section: Drift-free Inverse Kinematicsmentioning

confidence: 99%

Inverse Kinematics of Redundant Manipulators Formulated as Quadratic Programming Optimization Problem Solved Using Recurrent Neural Networks: A Review

2019

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SUMMARYThe Inverse Kinematics (IK) problem of manipulators can be divided into two distinct steps: (1) Problem formulation, where the problem is developed into a form which can then be solved using various methods. (2) Problem solution, where the IK problem is actually solved by producing the values of different joint space variables (joint angles, joint velocities or joint accelerations). The main focus of this paper is concentrated on the discussion of the IK problem of redundant manipulators, formulated as a quadratic programming optimization problem solved by different kinds of recurrent neural networks.

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