Integration of intelligent systems and sensor fusion within the CONTROLAB AGV

This paper discusses the integration of intelligent systems and the use of sensor fusion within a Multi-Level Fusion Architecture (MUFA) designed for controlling the navigation of a tele-commanded Autonomous Guided Vehicle (AGV). The AGV can move autonomously within any office environment, following instructions issued by client stations connected to the Internet and reacting accordingly to different situations found in the rear world. The modules which integrate the MUFA architecture are discussed and special emphasis is given to the role played by the intelligent obstacle avoidance procedure. The AGV detailed trajectory is firstly defined by a rule-based PFIELD algorithm from sub-goals established by a global trajectory planner. However, when an unexpected obstacle is detected by the neural network which performs the fusion of information produced by the vision system and sonar sensors, the obstacle avoidance procedure uses a special set of rules to redefine the AGV trajectory. The architecture of the neural network used for performing the sensor fusion function and the adopted set of rules are discussed. In addition, results of some simulation experiments demonstrate the ability of the system to define a new global trajectory when unexpected blocked regions are detected.