Self-organized task allocation in swarm robotics foraging based on dynamical response threshold approach

Social insects can flexibly respond to variational environment without global information and centralized control, and inspired by the collective behavior in ants and bees, this paper proposes a dynamical response threshold model (DRTM) in which the robots perform task allocation in self-organized manner. The proposed method depends on neither the global information nor the communication between robots. Using this method, the individual robot considers the amount of food in nest as stimulus and can compute the threshold dynamically according to monitoring the number of resting robots as well as counting the times of obstacle avoidance in the last foraging task. Taking advantage of stimulus and threshold, the robots automatically decide whether to forage on the basis of foraging probability. Simulation experiments are carried out with the aim of studying the effectiveness of the proposed model and evaluating the performance of task allocation in foraging scenarios. The experimental results presented in this paper prove that DRTM can achieve an efficient task allocation and possess better self-organized feature compared with the existing fixed response threshold model (FRTM).