On the inverse kinematics of redundant manipulators
International Journal of Robotics Research
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Biologically Inspired Robots: Serpentile Locomotors and Manipulators
Biologically Inspired Robots: Serpentile Locomotors and Manipulators
Analysis of Creeping Locomotion of a Snake-like Robot on a Slope
Autonomous Robots
Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators
Journal of Intelligent and Robotic Systems
RBF neural network based shape control of hyper-redundant manipulator with constrained end-effector
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Neural network approaches to dynamic collision-free trajectorygeneration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In a sinusoid like curve configuration, the snake-like manipulator (also called snake arm) has a wide range of potential applications for its redundancy to overcome conventional industrial robot's limitation when carrying out a complex task. It can perform many kinds of locomotion like the nature snake or the animal's tentacle to avoid obstacles, follow designated trajectories, and grasp objects. Effectively control of the snake-like manipulator is difficult for its redundancy. In this study, we propose an approach based on BP neural network to kinematic control the hyper-redundant snake-like manipulator. This approach, inspired by the Serpenoid curve and the concertina motion principle of the nature snake, is completely capable of solving the control problem of a planar snake-like manipulator with any number of links following any desired direction and trajectory. With shape transformation and base rotation, the manipulator's configuration changes accordingly and moves actively to perform the designated tasks. By using BP neural networks in modeling the inverse kinematics, this approach has such superiorities as few control parameters and high precision. Simulations have demonstrated that this control technique for the snake-like manipulator is available and effective.