Dexterity measures for the design and control of kinematically redundant manipulators
International Journal of Robotics Research
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning Dynamic Obstacle Avoidance for a Robot Arm Using Neuroevolution
Neural Processing Letters
Optimum robot manipulator path generation using differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Resolve redundancy with constraints for obstacle and singularity avoidance subgoals
International Journal of Robotics and Automation
Robotics and Autonomous Systems
Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
Robotics and Computer-Integrated Manufacturing
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
A geometric approach for inverse kinematics of a 4-link redundant In-Vivo robot for biopsy
Robotics and Autonomous Systems
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This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.