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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 onstrategies aimed to maximise the throughput of the common target area, wedeveloped two novel algorithms for robots using artificial potential fields forobstacle avoidance and navigation. One algorithm is inspired by creating aqueue to get to the target area (Single Queue Former -- SQF), while the othermakes the robots touch the boundary of the circular area by using vector fields(Touch and Run Vector Fields -- TRVF). We performed simulation experiments toshow that the proposed algorithms are bounded by the throughput of theirinspired theoretical strategies and compare the two novel algorithms withstate-of-art algorithms for the same problem (PCC, EE and PCC-EE). The SQFalgorithm significantly outperforms all other algorithms for a large number ofrobots or when the circular target region radius is small. TRVF, on the otherhand, is better than SQF only for a limited number of robots and outperformsonly PCC for numerous robots. However, it allows us to analyse the potentialimpacts on the throughput when transferring an idea from a theoretical strategyto a concrete algorithm that considers changing linear speeds and distancesbetween robots.

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