Tri-Projection Neural Network for Redundant Manipulators
Resolving redundancy for manipulators subject to various limits is significant in robotics. The problem is often handled by an optimization formulation with joint velocity being the decision variable, for which how to address the joint acceleration constraint is challenging. Motivated by this proble...
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| Published in | IEEE transactions on circuits and systems. II, Express briefs Vol. 69; no. 12; pp. 4879 - 4883 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1549-7747 1558-3791 |
| DOI | 10.1109/TCSII.2022.3189664 |
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| Abstract | Resolving redundancy for manipulators subject to various limits is significant in robotics. The problem is often handled by an optimization formulation with joint velocity being the decision variable, for which how to address the joint acceleration constraint is challenging. Motivated by this problem, a dynamic neural network with triple projections, called tri-projection neural network (TPNN), is developed for quadratic programs with a constraint on the state evolution of the neuron states. The proposed TPNN is applied to resolving redundancy of an ABB IRB 140 industrial manipulator with velocity inputs subject to joint acceleration constraints. Simulation comparisons with an existing method demonstrate the superiority of the developed TPNN in fully employing the acceleration capability of the manipulator. |
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| AbstractList | Resolving redundancy for manipulators subject to various limits is significant in robotics. The problem is often handled by an optimization formulation with joint velocity being the decision variable, for which how to address the joint acceleration constraint is challenging. Motivated by this problem, a dynamic neural network with triple projections, called tri-projection neural network (TPNN), is developed for quadratic programs with a constraint on the state evolution of the neuron states. The proposed TPNN is applied to resolving redundancy of an ABB IRB 140 industrial manipulator with velocity inputs subject to joint acceleration constraints. Simulation comparisons with an existing method demonstrate the superiority of the developed TPNN in fully employing the acceleration capability of the manipulator. |
| Author | Zhang, Yinyan |
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| SubjectTerms | Acceleration Biological neural networks Constraints joint acceleration constraint Kinematics Manipulator dynamics Manipulators Neural networks Neurons Optimization projection neural network Quadratic program Quadratic programming Redundancy redundancy resolution redundant manipulator Robot arms Robotics Termination of employment |
| Title | Tri-Projection Neural Network for Redundant Manipulators |
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