In robotics, there are several applications for mobile robots that require relatively high levels of precision. In our research, we developed a system that balances the trade-off of speed and precision for mobile robot navigation around static obstacles.
Our 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 computational cost and can suffer from reliability problems due to poor lighting.
Ideally, when fusing two sensors, they would have complementary properties. For this system, we chose to focus on combining active and passive radio systems. Active beacons generally broadcast their own radio signals independently. Usable anywhere the signal can be read, the robot can travel at higher speeds.
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.
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