Optimization of Energy Consumption in Swarms of Robots

This paper presents an agent based computational model in which swarms of autonomous mobile robots have the task to clean the environment. The robots collect debris from the environment and dump them in a designated arena. The energy of each robot decreases as it works and when the energy becomes lower than a fixed threshold, it switches to the charging mode. Under this mode, a robot goes to a destined area to charge itself up. Besides just gaining energy from the designated charging area, they can also share energy with each other when required. The way the robots share energy is inspired by the trophallactic behavior displayed by ant colonies. The model proposed in this paper is inspired by the behavior and group interaction of social insects, particularly by that of the ants. Genetic Algorithm (GA) has been applied on the model to search the model
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s parameter space to obtain the particular set of parameter values for which the model consumes the least energy. Experimental results show that incorporating GA to tune the parameter values improve the performance of the swarm in terms of the energy consumption over earlier strategies.