Probabilistic consensus decision making algorithm for artificial swarm of primitive robots Academic Article uri icon


  • 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.

published proceedings

  • SN Applied Sciences

altmetric score

  • 0.5

author list (cited authors)

  • Liu, Y., & Lee, K.

citation count

  • 3

complete list of authors

  • Liu, Y||Lee, K

publication date

  • February 2020