Stochastic Cellular Automata Ant Memory model for swarm robots performing efficiently the garbage collection task Collective intelligence has attracted attention of many researchers seeking to understand different realworld problems. In swarm robotics, the study of this area has revolutionized control algorithms, especially when they are aligned with other techniques that allow the easy programming of these robotic equipment. This work proposes a control algorithm for homogeneous and heterogeneous robots teams that perform garbage collection task based on cellular automata ants and Tabu search. Unlike precursor methods, in this work both searching and homing states are stochastic and the deposition and decline pheromone parameters are dynamic over time. From simulations it was possible to show that the new controller is adaptable to different parameters and at the same time is efficient in the garbage collection task for swarm robotics.

Analysis of Random Walk Models in Swarm Robots for Area Exploration The purpose of area exploration is to cover an area effectively and swarm robots are used widely for this type of area exploration because of their robustness, flexibility, and scalability. Meanwhile, due to the limited individual abilities of swarm robots, the random walk methods have been the generally used areaexploration strategy. Although random walk methods possess better performance in area exploration, there still exist some problems. For one thing, little work has been devoted to the theoretical analysis of the random walk models. For another, no effective measures are used to evaluate the searching efficiency of random walk methods and the searching efficiency is verified mainly by simulation experiments. Therefore, in order to make up for the deficiency, this paper presents the mathematical description of the random walk models by drawing on the experience of related researches in biology. The proposed mathematical theory can not only be used to aid our understanding of random walks but also be easy to analyze and control random motion of the robots. Also, the mean squared displacement (MSD) is introduced as the performance measure to evaluate the effectiveness of the random walk methods. In order to proof the effectiveness of the performance measure of MSD, the areaexploration missions of swarm robots are carried out in the simulation experiments and the the coverage rate is used to evaluate the searching efficiency. The experimental results prove that the MSD is an effective performance measure to evaluate the searching efficiency.