When less is more: Robot swarms adapt better to changes with constrained communication

Abstract

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

“…Classically, the global agreement is an emergent behavior resulting from the sum of local coordination efforts. However, if the neighborhood is small, the agents are more willing to adapt their actions to the local environment, a result also demonstrated by Strömbom (2011), Talamali et al (2021 in a movement consensus task and in a dynamic optimum area aggregation task respectively. However, this over-willingness to adapt leaves an agent vulnerable to random fluctuations of its neighbors' output-i.e., the swarm becomes undecided, with a low degree of coherence (Czirók and Vicsek, 1999;Buhl et al, 2006).…”

Social learning in swarm robotics

“…robots physically align with one another, which can be used for collective motion [ 8 ]). local communication: robot-to-robot communication is limited in radius, whether because of technical limitations (short-range communication apparatus) or by design (constrained communication can be beneficial in dynamic environments [ 12 ]). Moreover, there may be limitations in terms of the quantity of information that can be transferred.…”

“…Very recently, swarm robotics has become increasingly popular, as illustrated by the recent publication of several works on this topic in major journals [ 8 – 11 ]. In particular, the work of [ 12 ] shows that the material limitations in terms of communication distance between robots can be advantageous when the environment is dynamic and requires a fast detection of environmental changes.…”

Fixation Maximization in the Positional Moran Process

“…A related propagation model is that of the Moran process, introduced as a model of genetic evolution (Moran 1958), and the variants thereof, such as the discrete Voter model (Clifford and Sudbury 1973;Liggett 1985;Antal, Redner, and Sood 2006;Talamali et al 2021). A single mutant (e.g., a new opinion, strain, social trait, etc.)…”