Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Statistical Learning for Humanoid Robots
Autonomous Robots
Learning to Control in Operational Space
International Journal of Robotics Research
Linear Hopfield networks and constrained optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Obstacle avoidance for kinematically redundant manipulators using a dual neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Canonical parameterization of excess motor degrees of freedom with self-organizing maps
IEEE Transactions on Neural Networks
A Lagrangian network for kinematic control of redundant robot manipulators
IEEE Transactions on Neural Networks
Three-dimensional neural net for learning visuomotor coordination of a robot arm
IEEE Transactions on Neural Networks
Implementation of self-organizing neural networks for visuo-motor control of an industrial robot
IEEE Transactions on Neural Networks
Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
Robotics and Computer-Integrated Manufacturing
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This paper proposes an online inverse-forward adaptive scheme with a KSOM based hint generator for solving the inverse kinematic problem of a redundant manipulator. In this approach, a feed-forward network such as a radial basis function (RBF) network is used to learn the forward kinematic map of the redundant manipulator. This network is inverted using an inverse-forward adaptive scheme until the network inversion solution guides the manipulator end-effector to reach a given target position with a specified accuracy. The positioning accuracy, attainable by a conventional network inversion scheme, depends on the approximation error present in the forward model. But, an accurate forward map would require a very large size of training data as well as network architecture. The proposed inverse-forward adaptive scheme effectively approximates the forward map around the joint angle vector provided by a hint generator. Thus the inverse kinematic solution obtained using the network inversion approach can take the end-effector to the target position within any arbitrary accuracy. In order to satisfy the joint angle constraints, it is necessary to provide the network inversion algorithm with an initial hint for the joint angle vector. Since a redundant manipulator can reach a given target end-effector position through several joint angle vectors, it is desirable that the hint generator is capable of providing multiple hints. This problem has been addressed by using a Kohonen self organizing map based sub-clustering (KSOM-SC) network architecture. The redundancy resolution process involves selecting a suitable joint angle configuration based on different task related criteria. The simulations and experiments are carried out on a 7 DOF PowerCube(TM) manipulator. It is shown that one can obtain a positioning accuracy of 1 mm without violating joint angle constraints even when the forward approximation error is as large as 4 cm. An obstacle avoidance problem has also been solved to demonstrate the redundancy resolution process with the proposed scheme.