Probabilistic Consensus Decision Making Algorithm for Artificial Swarm of Primitive Robots

This paper presents a consensus algorithm for artificial swarms of primitive agents, such as robots with limited sensing, processing, and communication capabilities. The presented consensus algorithm provides solutions of collective decision making for a connected network of robots. The decisions are considered abstract choices without difference, thus the algorithm can be “programmed” for a broad range of applications with specific decisions. Each robot in the swarm is considered a probabilistic finite state machine, whose preferences towards a set of discrete states are defined as a probabilistic mass function. Then, the individual preferences are updated via local negotiation with directly connected robots, followed by a convergence improvement process. The presented algorithm is evaluated for the effects of network topology and scalability (i.e., the number of decisions and the size of the swarm) on convergence performance.