Obstacle Avoidance for Kinematically Redundant Manipulators Using a Dual Neural Network

Yunong Zhang, Jun Wang

Research output: Contribution to journalArticlepeer-review

170 Citations (Scopus)


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 are also incorporated directly into the formulation. Based on the improved problem formulation, a dual neural network is developed for the online solution to collision-free inverse kinematics problem. The neural network is simulated for motion control of the PA10 robot arm in the presence of point and window-shaped obstacle.

Original languageEnglish
Pages (from-to)752-759
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number1
Publication statusPublished - Feb 2004
Externally publishedYes


  • Dual neural network
  • Obstacle avoidance
  • Quadratic programming
  • Redundant manipulators


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