Distributed optimization for the multi-robot system using a neurodynamic approach

被引：18

作者：

Fang, Xiaomeng ^{[1
,2
]}

Pang, Dong ^{[1
,2
]}

Xi, Juntong ^{[1
,2
]}

Le, Xinyi ^{[1
,2
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China

[2] Shanghai Key Lab Adv Mfg Environm, Shanghai 200240, Peoples R China

来源：

NEUROCOMPUTING | 2019年 / 367卷

基金：

中国国家自然科学基金; 上海市自然科学基金;

关键词：

Distributed optimization; Multi-robot system; Trajectory tracking; Obstacle avoidance; Recurrent neural networks; PROJECTION NEURAL-NETWORK; OBSTACLE-AVOIDANCE; KINEMATIC CONTROL; SCHEME;

D O I：

10.1016/j.neucom.2019.08.032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we use a connected undirected graph to describe the multiple redundant manipulator system. An optimization model is formulated as a convex problem with coupled constraints. These constraints include equality constraints derived for path tracking, inequality constraints derived for obstacle avoidance, and convex sets built for joint physical limits. A novel distributed neurodynamics-based algorithm is developed for solving the complex problem in real time, so that there is no need for having a center coordinator in the multi-robot system. To verify the established model and the proposed algorithm, a dual-robot system is simulated to carry a rigid object following desired trajectories with obstacles considered. A more complex tri-robot system is simulated to perform as a supplementary evidence. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：103 / 113

页数：11

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