Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy

Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movem...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2018-11, Vol.15 (6), p.1943-1950
Main Authors: Tang, Qirong, Ding, Lu, Yu, Fangchao, Zhang, Yuan, Li, Yinghao, Tu, Haibo
Format: Article
Language:English
Subjects:
Adaptability
Collision avoidance
Environmental information
grouping
multiple targets
Obstacle avoidance
Particle swarm optimization
Robot kinematics
Robot sensing systems
Robots
Search problems
Search process
searching
Stochasticity
Strategy
Swarm robots
Unknown environments
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Summary:Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy, and efficiency of the proposed strategy in multiple targets searching.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2017.2682161