Multi-agent Autonomous Patrolling System Using ANN and FSM Control

Abstract

The multi-agent patrolling problem has recently received growing attention from the community due to the wide range of potential applications. This work presents an autonomous patrolling system composed by 4 intelligent robots that can freely move through an indoor environment and detect intruders. The robots use a localization/navigation system composed of an artificial neural network (ANN) trained to detect key features of the environment. These features are used to identify context changes, being used as input of a finite state machine (FSM), allowing a topological map localization and navigation of the robot in the environment. When an intruder is detected, a broadcast message with its position is sent, making all other robots execute a multi-agent version of a coordinated A* algorithm in order to determine the best path to reach that position and to surround the target. Then, the robots autonomously navigate through this defined path until reach the goal. Experiments were performed in the player/stage environment in order to evaluate the multi-agent system. The localization/navigation system with intruder detection was evaluated in the real world with a Pioneer P3-AT mobile robot.