ArticleFrontiers in neurorobotics2019
Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators.
Article in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators.Entropy (Basel, Switzerland) · 2022Article
- Harmonic Noise-Tolerant ZNN for Dynamic Matrix Pseudoinversion and Its Application to Robot Manipulator.Frontiers in neurorobotics · 2022Article
- Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study.Frontiers in human neuroscience · 2021Article
- Motion and Trajectory Constraints Control Modeling for Flexible Surgical Robotic Systems.Micromachines · 2020Article
- Bi-criteria Acceleration Level Obstacle Avoidance of Redundant Manipulator.Frontiers in neurorobotics · 2020Article
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Authors and funding
3 authors.
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Abstract
Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By abstracting robots and obstacles into critical point sets respectively, the distance between the robot and obstacles can be described in a simpler way, then the obstacle avoidance strategy is established in form of inequality constraints by general class-K functions. Using minimal-velocity-norm (MVN) scheme, the control problem is formulated as a quadratic-programming case under multiple constraints. Then a deep recurrent neural network considering system models is established to solve the QP problem online. Theoretical conduction and numerical simulations show that the controller is capable of avoiding static or dynamic obstacles, while tracking the predefined trajectories under physical constraints.
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