  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.
