Persistence Region Monitor With a Pheromone-Inspired Robot Swarm Sensor Network

Abstract

In this article, we propose a controller that can coordinate a swarm of robots to cover a region persistently by forming a mobile sensor network. Therefore, the robot swarm can monitor a large area of interest (AOI) over a long time. The performance of the swarm can achieve high flexibility and coverage efficiency with swarm intelligence. This method is inspired by the behavior of large predators, such as lions that use liquid markers containing pheromones to mark their territories and indicate their status. Via interactions based on these clues, the ecosystem can achieve a dynamic balance. The controller inspired by this phenomenon consists of two layers, which are a region divider and a path planner. The region divider evenly splits the area into random shapes according to a pheromone clue and adapts to dynamic changes of the swarm size. Then, the path planner generates a closed patrol path for each robot. Like a random search scheme or an ant pheromone-inspired scheme, the proposed controller can adapt to the dynamic change of swarm size providing high efficiency of region coverage. The effectiveness of this controller is proved by modeling the life cycle of a robot swarm as a finite state machine. It is also verified with simulations and experiments with unmanned ground vehicles (UGVs).