Obstacle Avoidance for Kinematically Redundant Manipulators Using a Dual Neural Network

Yunong Zhang, Jun Wang

Результат исследований: Вклад в журналСтатьярецензирование

174 Цитирования (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.

Язык оригиналаАнглийский
Страницы (с-по)752-759
Число страниц8
ЖурналIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Номер выпуска1
СостояниеОпубликовано - февр. 2004
Опубликовано для внешнего пользованияДа


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