Consensus-Based Artificial Potential Field Approach for Swarm

We propose a consensus-based artificial potential field (CAPF) approach for swarm control. The CAPF approach enables a swarm to accomplish different complex tasks including task allocation. The artificial potential field (APF) approach provides an efficient control law for different types of swarm control, such as consensus control, formation control, and coverage control. In the APF approach, the control inputs of robots are determined on the basis of a potential field, that is, a gradient of the potential function. In the existing swarm controls, the potential field for each robot depends on only local information such as the robot's own state and states of nearby robots. A swarm cannot accomplish complex tasks with the existing APF approach because of this restriction on the potential function. In our CAPF approach, in contrast, the potential function does not have this restriction and the potential field is calculated on the basis of a consensus filter that requires only local communication. We show that, by using the CAPF approach, a state of a swarm converges to a local minimum of the potential function. Moreover, we apply the CAPF approach to a multi-robot task allocation (MRTA) problem.