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

Abstract

We study how robot swarms can achieve a consensus on the best among a set of n possible options available in the environment. While the robots rely on local communication with one another, follow simple rules, and make estimates of the option's qualities subject to measurement errors, the swarm as a whole is able to make accurate collective decisions. We compare the performance of two prominent decision-making algorithms that are based, respectively, on the directswitching and the cross-inhibition models, both of which are well-suited for simplistic robots. Most studies used these models to let robots achieve consensus by solely relying on social interactions and ignored the aspect of enabling robots to self-source information from the environment. However, in order to select the best option, we deem sampling environmental information crucial for the successful performance of the task. Through robot-swarm simulations, we show that swarms programmed with the direct-switching model are only able to make consensus decisions in asymmetric environments where options have different quality values. Instead, using cross-inhibition, the robot swarm can also break decision deadlocks and reach a consensus in symmetric environments with equal quality options. We investigate the mechanistic causes of such differences and we find that the time the robots spend in a state of indecision is a key parameter to break the symmetry. This research highlights the importance of considering both social and environmental information when studying collective decision-making.

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

“…Different from previous work requiring maintenance of shared collective knowledge through rich inter-robot communication [28,29], here the robots send simple messages with a few bits of information, only indicating their preferred element (i.e., their chosen colour, for n = 2 that is one bit of information). Other works in collective perception that are comparable to ours in their simplicity of individual robot requirements are Valentini et al [33] and Zakir et al [35]. We extend previous analyses by considering a dynamic environment which has only been considered in a few consensus decision making studies for the site selection scenario [16,17,6,30], while here we consider the collective perception scenario.…”

Response Threshold Distributions to Improve Best-of-N Decisions in Minimalistic Robot Swarms

“…In our research, we explore the possibility of using heterogeneous response thresholds to make best-of-n decisions. We envision the possibility of simplifying the existing algorithms used in collective decision making in the context of best-of-n [19,23,28,29,31,32,37,3], by removing the (currently necessary) robots' ability of (i) scaling the estimated option's quality in a normalised quality range, and (ii) memorise the option's quality in order to modulate the communication frequency (i.e. weighted voting).…”

“…Several works investigated swarm robotics solutions for best-of-n problems [30,22]. Existing solutions that we believe have the fewest requirements on the individual robots, in terms of communication, computation, and memory, are based on simple voting algorithms combined with quality-based frequency of communication [32,29,31,19,37,3]. Through these methods, robots search for available options, and once they find one, they make an individual estimate of the option's quality and use this quality to regulate their communication frequency.…”

Application of Polling Scheduling in Mobile Edge Computing

“…D. Miculescu and Li, S. et al [29,30] use a polling mechanism in autonomous driving, reducing delays and improving system stability. Mohamed S. and Zakir, R. et al [31,32] apply a polling mechanism to robots, enabling low-latency robot swarm control to cooperate in completing tasks. The swarm of robots can perform more complex tasks than a single robot.…”