Obstacle avoidance for kinematically redundant manipulators using a dual neural network

@article{Zhang2004ObstacleAF,
  title={Obstacle avoidance for kinematically redundant manipulators using a dual neural network},
  author={Yunong Zhang and Jun Wang},
  journal={IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
  year={2004},
  volume={34 1},
  pages={
          752-9
        }
}
  • Yunong Zhang, Jun Wang
  • Published 1 February 2004
  • Engineering
  • IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
One important issue in the motion planning and control of kinematically redundant manipulators is the obstacle avoidance. In this paper, a recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability. An improved problem formulation is proposed in the sense that the collision-avoidance requirement is represented by dynamically-updated inequality constraints. In addition, physical constraints such as joint physical limits… 
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References

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Simulation results show that the neural network is capable of computing the redundancy resolution for obstacle avoidance and generates the joint velocity vector which drives the manipulator to avoid obstacles and tracks the desired end-effector trajectory simultaneously.
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TLDR
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