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 velocities and distances between robots.
翻译:当大量机器人试图到达一个共同区域时,就会出现拥堵,造成严重延误。为了尽量减少机器人群群系统中的拥堵,必须以分散化的方式使用交通控制算法。根据旨在尽量扩大共同目标区域输送量的战略,我们为机器人开发了两种新的算法,使用人为潜在领域来避免和导航障碍。一种算法的灵感来自为进入目标区域而创建的队列(单一队列前身 -- -- SQF),而另一种则使机器人通过矢量字段(托奇和运行矢量字段 -- -- TRVF)接触圆形区域的边界。我们进行了模拟实验,以显示拟议的算法受其启发的理论战略输送量的约束,并将两种新算法与同一问题的最新算法(PCC、EE和PCC-EE)进行比较。 SQF 算法大大超出许多机器人或圆形目标区域半径小时所有其他算法的所有其他算法。TRPERF,在另一边上,我们进行了模拟试验,显示拟议算法的界限是它们受启发的精度,然而,只通过机器人变形的机器人变形法战略,而只考虑从多的机器人变换到机器人,只有机器人变形的机器人变形的策略,才有它。