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Wireless Sensor Network and RFID Fusion Approach for Mobile Robot Navigation

DOI: 10.1155/2013/157409

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Abstract:

There are numerous applications for mobile robots that require relatively high levels of speed and precision. For many systems, these two properties are a tradeoff, as oftentimes increasing the movement speed can mean failing to detect some sensors. This research attempts to create a useful and practical system by combining a wireless sensor network with a passive radio frequency identification system. The sensor network provides fast general navigation in open areas and the radio frequency identification system provides precision navigation near static obstacles. By fusing the data from both systems, we are able to provide fast and accurate navigation for a mobile robot. Additionally, with WSN nodes and passive RFID tag mats, the system infrastructure can be easily installed in existing environments. 1. Introduction In ubiquitous robotics, there are several applications for mobile robots that require relatively high levels of precision. In this research, we developed a system that balances the trade-off of speed and precision for mobile robot navigation around static obstacles. The system uses higher speeds when the area is free of obstacles, via a wireless-sensor-network-based (WSN) navigation approach. Around obstacles, or when a specific path or pose is required, the system incorporates a Radio Frequency IDentification (RFID) system, for precise navigation. By fusing the data from both systems using a simple, vector-based approach, we are able to provide fast and accurate navigation for a mobile robot. RFID tag mats were also developed which, along with easily installed wireless sensor nodes, make for quickly deployed system infrastructure. This system focuses on using this fusion approach for indoor mobile robot navigation with static obstacle avoidance. While the avoidance of dynamic obstacles is also key for ubiquitous robotics, there is still room for improvement in static obstacle avoidance. Moreover, the vast majority of applications for indoor robot navigation have a large static obstacle component to them. It is for this reason that we chose to focus first on this aspect. A simple taxonomy for robot sensors (pertaining to navigation) could be listed as such: vision (cameras, etc.), range-finding (laser, sonar, etc.), inertial (encoders, etc.), active beacons (active RFID, WSN, etc.), and passive beacons (passive RFID, magnetic strips, etc.). All of these systems have their advantages and disadvantages. For instance, range-finding can suffer from inaccuracy when there are few reflective surfaces and vision systems often have a high

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