Energy Consumption Minimized Task Allocation with Correlated Data for Symbiotic Robotic Swarm

In this paper, computational task allocation schemes with correlated data are investigated to minimize the energy consumption for a symbiotic robot swarm. In such a swarm, tasks need to be computed cooperatively with data from multiple robots. Therefore, data needs to be transmitted to one selected robot. However, the correlated data among robots can increase the energy consumption of transmission and computation due to redundancy. To solve this problem, a model is proposed to investigate the data correlation versus distance among the robots. Based on this model, three task allocation strategies are further proposed. Energy consumption of the robot swarm is reduced through the selection of the robot to transmit data either based on channel gain or data correlation. MATLAB-based simulation results show that the proposed task allocation strategies can significantly reduce the energy consumption of a symbiotic robotic swarm compared to state-of-the-art.