The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control

Compared with the space fixed feature of traditional wireless sensor network (WSN), mobile WSN has better robustness and adaptability in unknown environment, so that it is always applied in the research of target tracking. In order to reach the target, the nodes group should find a self-adaptive met...

Full description

Bibliographic Details
Main Authors: Zuo Chen, Lei Ding, Kai Chen, Renfa Li
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/614346
id doaj-f87823a1f97444d2b53098fed7709e5d
record_format Article
spelling doaj-f87823a1f97444d2b53098fed7709e5d2020-11-25T00:54:34ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/614346614346The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking ControlZuo Chen0Lei Ding1Kai Chen2Renfa Li3College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, ChinaCollege of Information Science and Engineering, Jishou University, Jishou, Hunan 416000, ChinaCollege of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, ChinaCollege of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, ChinaCompared with the space fixed feature of traditional wireless sensor network (WSN), mobile WSN has better robustness and adaptability in unknown environment, so that it is always applied in the research of target tracking. In order to reach the target, the nodes group should find a self-adaptive method to avoid the obstacles together in their moving directions. Previous methods, which were based on flocking control model, realized the strategy of obstacle avoidance by means of potential field. However, these may sometimes lead the nodes group to fall into a restricted area like a trap and never get out of it. Based on traditional flocking control model, this paper introduced a new cooperative obstacle avoidance model combined with improved SA obstacle avoidance algorithm. It defined the tangent line of the intersection of node’s velocity line and the edge of obstacle as the steering direction. Furthermore, the cooperative obstacle avoidance model was also improved in avoiding complex obstacles. When nodes group encounters mobile obstacles, nodes will predict movement path based on the spatial location and velocity of obstacle. And when nodes group enters concave obstacles, nodes will temporarily ignore the gravity of the target and search path along the edge of the concave obstacles. Simulation results showed that cooperative obstacle avoidance model has significant improvement on average speed and time efficiency in avoiding obstacle compared with the traditional flocking control model. It is more suitable for obstacle avoidance in complex environment.http://dx.doi.org/10.1155/2014/614346
collection DOAJ
language English
format Article
sources DOAJ
author Zuo Chen
Lei Ding
Kai Chen
Renfa Li
spellingShingle Zuo Chen
Lei Ding
Kai Chen
Renfa Li
The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
The Scientific World Journal
author_facet Zuo Chen
Lei Ding
Kai Chen
Renfa Li
author_sort Zuo Chen
title The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
title_short The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
title_full The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
title_fullStr The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
title_full_unstemmed The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control
title_sort study of cooperative obstacle avoidance method for mwsn based on flocking control
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2014-01-01
description Compared with the space fixed feature of traditional wireless sensor network (WSN), mobile WSN has better robustness and adaptability in unknown environment, so that it is always applied in the research of target tracking. In order to reach the target, the nodes group should find a self-adaptive method to avoid the obstacles together in their moving directions. Previous methods, which were based on flocking control model, realized the strategy of obstacle avoidance by means of potential field. However, these may sometimes lead the nodes group to fall into a restricted area like a trap and never get out of it. Based on traditional flocking control model, this paper introduced a new cooperative obstacle avoidance model combined with improved SA obstacle avoidance algorithm. It defined the tangent line of the intersection of node’s velocity line and the edge of obstacle as the steering direction. Furthermore, the cooperative obstacle avoidance model was also improved in avoiding complex obstacles. When nodes group encounters mobile obstacles, nodes will predict movement path based on the spatial location and velocity of obstacle. And when nodes group enters concave obstacles, nodes will temporarily ignore the gravity of the target and search path along the edge of the concave obstacles. Simulation results showed that cooperative obstacle avoidance model has significant improvement on average speed and time efficiency in avoiding obstacle compared with the traditional flocking control model. It is more suitable for obstacle avoidance in complex environment.
url http://dx.doi.org/10.1155/2014/614346
work_keys_str_mv AT zuochen thestudyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT leiding thestudyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT kaichen thestudyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT renfali thestudyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT zuochen studyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT leiding studyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT kaichen studyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
AT renfali studyofcooperativeobstacleavoidancemethodformwsnbasedonflockingcontrol
_version_ 1725233664212598784