Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator

Authors:
Jinguo Liu;Yuechao Wang;Bin Li;Shugen Ma
Affiliations:
Robotics Laboratory of Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China and Graduate School of Chinese Academy of Sciences, Bejing, China;Robotics Laboratory of Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China;Robotics Laboratory of Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China;Robotics Laboratory of Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China and Center for Promotion of the COE Program, Ritsumeikan University, Shiga-ken, Japan
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 9
Cited 0

On the inverse kinematics of redundant manipulators

International Journal of Robotics Research
On the approximate realization of continuous mappings by neural networks

Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

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
Performance comparison for MLP networks using various back propagation algorithms for breast cancer diagnosis

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.