论文标题
基于目标区域吞吐量的机器人群的拥塞控制算法
Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area
论文作者
论文摘要
当大量机器人试图到达公共区域时，会发生拥堵，导致严重的延误。为了最大程度地减少机器人群体中的交通拥堵，必须以分散的方式使用交通控制算法。基于旨在最大化共同目标区域吞吐量的策略，我们使用人工潜在领域为避免障碍物和导航开发了两种新颖的机器人算法。一种算法是通过创建一个队列进入目标区域的启发的（单队列以前-SQF），而另一个使机器人通过使用向量场（触摸和运行向量字段-TRVF）使机器人触摸圆形区域的边界。我们进行了仿真实验，以表明所提出的算法受其启发的理论策略的吞吐量，并将两种新型算法与同一问题的最先进算法进行比较（PCC，EE和PCC-EE）。 SQF算法显着胜过大量机器人的所有其他算法，或者当圆形目标区域半径较小时。另一方面，对于有限数量的机器人，TRVF仅比SQF更好，并且对于众多机器人而言，PCC仅优于PCC。但是，它使我们能够分析从理论策略转移到混凝土算法时对吞吐量的潜在影响，该算法考虑了改变机器人之间的线性速度和距离。
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.
