The Hidden Benefits of Limited Communication and Slow Sensing in Collective Monitoring of Dynamic Environments

Abstract

Most of our experiences, as well as our intuition, are usually built on a linear understanding of systems and processes. Complex systems in general, and more specifically swarm robotics in this context, leverage non-linear effects to self-organise and to ensure that 'more is different'. In previous work, the non-linear and therefore counter-intuitive effect of 'less is more' was shown for a site-selection swarm scenario. Although it seems intuitive that being able to communicate over longer distances should be beneficial, swarms were found to sometimes profit from communication limitations. Here, we build on this work and show the same effect for the collective perception scenario in a dynamic environment. We also find an additional effect that we call 'slower is faster': in certain situations, swarms benefit from sampling their environment less frequently. Our findings are supported by an intensive empirical approach and a mean-field model. All our experimental work is based on simulations using the ARGoS simulator extended with a simulator of the smart environment for the Kilobot robot called Kilogrid. 4 The current geopolitical situation motivated our choice of tile colour .

Benchmarking Effects of Communication Bandwidths and Number of Options on Collective Decision-making Strategies in Discrete Collective Estimation

“…Both opinion-based approaches also experience an oversaturation of communication at high bandwidth levels, which leads to a faster consensus but higher error in decision making. This reflects the finding in [13,14] where an increase in communication has an adverse effect on the decision-making process in limiting adaptibility of the swarm. Although both aforementioned studies address decision-making scenarios with dynamic environments, the finding translates to the context of multi-option decision making in static environments, where adaptability is crucial in convergence towards the correct option out of multiple suboptimal ones.…”

Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition

“…To provide robots with a virtual environment from which they can self-source information, we simulate the Kilogrid [1,32]. The Kilogrid is an electronic table sized 1 × 2 m 2 , composed of 800 cells that interact with the Kilobots through IR and that can be easily simulated in ARGoS [2]. With the exception of the Kilogrid cells at the borders (depicted in white in Figure 1B), all the cells are set to send constantly IR messages signalling their ID and their colour, either the yellow colour associated with option A or the blue colour associated with option B.…”

“…Periodically, robots choose to ignore social information and self-source information from the environment by independently switching their commitment to the option locally sensed in the environment. The individual selfsourcing of information through independent exploration of the environment can allow the swarm to achieve better adaptability in dynamic environments [36,30,2] where qualities of options may change over time. However, self-sourcing information is a form of asocial behaviour that also increases fluctuations (or noise) in the consensus formation [31,21] that may result in decision deadlocks in certain decision-making algorithms [16,21,12].…”

Impact of the Update Time on the Aggregation of Robotic Swarms Through Informed Robots

“…The Kilobots are also able to modify the environment by sending messages to the current cell that will be processed as actions. Experiments were performed using the ARGoS simulator [21] with the ARGoS-Kilobot plugin to model the Kilobots [20], and the ARGoS-Kilogrid plugin [1] which allow the use of identical code in simulation and on the real Kilobots and Kilogrid. In our previous experimental scenario [26], the arena was a circle with two circular aggregation sites.…”