Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area

TL;DR

This paper introduces two novel decentralized congestion control algorithms for robotic swarms aiming to maximize target area throughput, demonstrating superior performance in simulations compared to existing methods.

Contribution

The paper presents two new algorithms, SQF and TRVF, inspired by theoretical strategies, for congestion control in robotic swarms with improved throughput performance.

Findings

SQF outperforms other algorithms for large robot numbers or small target areas.

TRVF is effective for limited robot numbers and surpasses PCC in some scenarios.

Both algorithms are bounded by the throughput limits of their theoretical inspirations.

Abstract

When a large number of robots try to reach a common area, congestions happen, causing severe delays. To minimise congestion in a robotic swarm system, traffic control algorithms must be employed in a decentralised manner. Based on strategies aimed to maximise the throughput of the common target area, we developed two novel algorithms for robots using artificial potential fields for obstacle avoidance and navigation. One algorithm is inspired by creating a queue to get to the target area (Single Queue Former -- SQF), while the other makes the robots touch the boundary of the circular area by using vector fields (Touch and Run Vector Fields -- TRVF). We performed simulation experiments to show that the proposed algorithms are bounded by the throughput of their inspired theoretical strategies and compare the two novel algorithms with state-of-art algorithms for the same problem (PCC, EE and PCC-EE). The SQF algorithm significantly outperforms all other algorithms for a large number of robots or when the circular target region radius is small. TRVF, on the other hand, is better than SQF only for a limited number of robots and outperforms only PCC for numerous robots. However, it allows us to analyse the potential impacts on the throughput when transferring an idea from a theoretical strategy to a concrete algorithm that considers changing linear speeds and distances between robots.