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 to navigate between source and sink. This study consists of two independent parts, both aiming to improve the performance of the system by reducing the spatial interference among robots. In the first part it is shown 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 that is effective in separating these trails. The results of simulation experiments indicate that the performance of robots is usefully increased compared to the original algorithm in constrained environments.