Reducing spatial interference in ant-like trail-following for multi-robot systems

We consider the classical task of transporting resources between spatially separated source and sink by a group of autonomous robots. The robots use ant-like trail-following for navigation. This study consists of two independent parts, both aiming to improve the performance of the system by reducing the spatial interference among robots. First we show that, under certain conditions, a narrow field of view of each robot's trail following sensor can improve system performance. We argue that the benefit is obtained by selectively degrading the individuals trail-following ability so that more work space is exploited in parallel, thus decreasing mutual spatial interference. In the second part, we focus on reducing the interference among robots that are following different trails. We propose a navigation strategy which is effective in separating these trails. The results of simulation experiments indicate that the performance of robots is increased compared to the original algorithm in constrained environments.