Pang, B.; Zhang, Z.; Song, Y.; Yuan, X.; Xu, Q. Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots. Appl. Sci.2023, 13, 9107.
Pang, B.; Zhang, Z.; Song, Y.; Yuan, X.; Xu, Q. Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots. Appl. Sci. 2023, 13, 9107.
Pang, B.; Zhang, Z.; Song, Y.; Yuan, X.; Xu, Q. Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots. Appl. Sci.2023, 13, 9107.
Pang, B.; Zhang, Z.; Song, Y.; Yuan, X.; Xu, Q. Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots. Appl. Sci. 2023, 13, 9107.
Abstract
In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.
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