Multitarget Search of Swarm Robots in Unknown Complex Environments

When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division stra...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-12
Main Authors: Zhang, Xin, Zhang, Hongqiang, Chen, Anhua, Zhou, You, Zhou, Shaowu
Format: Article
Language:English
Subjects:
Behavior
Collaboration
Collision avoidance
Communication
Computer simulation
Control methods
Kinematics
Optimization algorithms
R&D
Research & development
Robot control
Robots
Searching
Velocity
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Summary:When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/8643120