Paper Info
Title
Inequality-based Manipulator-Obstacle Avoidance Using the LVI-based Primal-dual Neural Network
Abstract
An important issue in the motion planning and control of redundant manipulators is the online obstacle-avoidance. This paper presents the algorithmic and computational aspects of inequality-based criteria/formulations for obstacle avoidance of PA10 robot arm. The formulations are unified as a quadratic- programming (QP) problem. In addition to handling environmental obstacles, this unified QP problem formulation could avoid joint physical limits as well as optimize various performance indices. Motivated by the online solution to such robotic optimization problems, four QP online algorithms/solvers are reviewed, especially the LVI-based primal-dual neural network. The inequality-based QP formulation and its solution for obstacle avoidance are substantiated by simulation results. This simulation also shows that joint-acceleration information could be generated online by using dynamic QP solvers for torque control even in the velocity-level redundancy resolution.
Year
DOI
Venue
2006
10.1109/ROBIO.2006.340144
ROBIO
Keywords
Field
DocType
redun-dancy resolution,motion planning,motion control,pa10 robot arm,quadratic programming,primal-dual neural network,quadratic-programming problem,torque control,lvi,manipulator-obstacle avoidance,robot manipulator,redundant manipulators,obstacle avoidance,redundancy resolution,collision avoidance,online solution,performance indicator,robot arm,quadratic program,online algorithm,optimization problem,neural network
Obstacle avoidance,Motion planning,Online algorithm,Mathematical optimization,Robotic arm,Control theory,Control engineering,Redundancy (engineering),Quadratic programming,Engineering,Artificial neural network,Optimization problem
Conference
ISBN
Citations 
PageRank 
1-4244-0571-8
1
0.36
References 
Authors
17
3
Name
Order
Citations
PageRank
Yunong Zhang12344162.43
Zhonghua Li2344.59
Hong-Zhou Tan319633.88