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The Scientific World Journal
Volume 2014 (2014), Article ID 614346, 12 pages
Research Article

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

1College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
2College of Information Science and Engineering, Jishou University, Jishou, Hunan 416000, China

Received 17 August 2013; Accepted 14 November 2013; Published 10 February 2014

Academic Editors: Y. Lu, J. Shu, and F. Yu

Copyright © 2014 Zuo Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.