Discrete Collective Estimation in Swarm Robotics with Ranked Voting Systems

The best-of-n problem has been a popular research topic for understanding collective decision-making in recent years. Researchers aim to enable a swarm of agents to collectively converge to a single opinion out of a series of potential options, using only local interactions. In this paper, we investigate the viability of decision-making via majority rule using ranked voting systems in multi-option scenarios where n>2. We focus on two ranked voting systems, single transferable vote (STV) and Borda count (BC). The proposed algorithms are tested in a discrete collective estimation scenario, and compared against two benchmark algorithms, direct comparison (DC) and majority rule using first-past-the-post voting (FPTP). We have analyzed the experimental results, focusing on the trade-off between accuracy and speed in decision-making. We have concluded that ranked voting systems can significantly improve the performances of collective decision-making strategies in multi-option scenarios. Our experiments show that BC is the best performing algorithm in the studied scenario.