We present insight into how contextual awareness can be derived from, and improve, a fusion algorithm combing a WSN and a passive RFID for autonomous mobile robot navigation. Contextual awareness of not where the robot is, but rather the context in which it exists in relation to the environment and human user serves to improve accuracy in navigation, alters the speed of the robot, and modifies its behavior. The WSN system, using a virtual potential field, provides fast general navigation in open areas and the RFID provides precision navigation near static obstacles and in narrow areas. We verified the effectiveness of our approaches through navigational and guidance experiments. 1. Introduction As robots become more common in our everyday lives, the need for an awareness beyond what simple sensors can detect also grows. While navigation of a mobile robot can be performed with only such data, a behavior that allows it to seamlessly integrate with humans is desirable. In this paper, we present contextual awareness concepts as derived from a wireless sensor network (WSN) and radio frequency identification (RFID) fusion approach to indoor, mobile robot navigation. A WSN-based navigation system allows a robot to move at faster speeds than an RFID-only approach, albeit with reduced accuracy. Conversely, RFID is a well known and utilized technology that can provide high levels of precision, but it requires the robot to move at a slower speed in order to ensure that all tags are read. By combining the two, we have developed a system capable of moving at relatively high speeds when precision is not a priority and slower speeds when the robot is moving in critical areas or higher accuracy is needed. Experiments were conducted with our fusion approach, as well as with an omnivision camera included, to show not only the method’s efficacy, but also how the system can be simply further augmented. 1.1. Previous and Related Works The use of radio-based systems for navigation is a well-developed field, and a WSN can be used in a similar fashion. In [1], a virtual potential field was created by combining information from nodes deployed around the navigable area for an indoor mobile robot. The radio signal strength intensities (RSSI) were combined with that field in order to allow the robot to navigate. In that same system, a servo-mounted camera combined with a CamShift algorithm-based method, [2], allowed the robot to estimate the human’s distance and angle relative to the robot. RFID technology is well known in robotic applications involving localization and
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