Mobile Robot Indoor Autonomous Navigation Based on Multi-sensor Integration

This paper presents methodologies and techniques for indoor autonomous navigation based on multi-sensor integration. For orientation estimation, an extended Kalman filter has been developed that fuses three different orientation information sources (i.e., MEMS gyroscopes, combination of MEMS accelerometers and magnetometers as well as wheel encoders). The developed heading system is specifically designed to overcome many challenges in an indoor environment. A sonic-vision module, which employs ultrasonic range finders, has been designed to improve RFID positioning solutions and furthermore to realize collision avoidance and turning tasks. An intelligent logic controller has been developed based on fuzzy logic technique. Experiments were conducted to demonstrate the effectiveness and merits of the proposed indoor navigation system.