Brian Yamauchi, Alan Schultz, William Adams, Kevin Graves, John Grefenstette, Dennis Perzanowski
In order for a robot to add its perceptions to a map, it needs to know its location, but in order for a robot to determine its location, it often needs a map. This is a central dilemma in robot exploration. Robots often use dead reckoning to estimate their position without a map, but wheels slip and internal linkages may be imprecise. These errors accumulate over time, and the robot’s position estimate becomes increasingly inaccurate. We have addressed this problem in ARIEL. ARIEL uses frontier-based exploration (Yamauchi 1997) to navigate to unexplored space and to map the territory that it perceives, and continuous localization (Schultz, Adams, and Grefenstette 1996) to maintain an accurate estimate of its position at all times. ARIEL has been implemented on a Nomad 200 mobile robot equipped with sonar, infrared, and laser range sensors. ARIEL runs on a SPARCstation 20 and communicates with the robot’s onboard Pentium processor via radio ethernet. This system has been used to explore real-world office environments. We will demonstrate ARIEL at the AAAI-97 Robot Exhibition. We are also interested in using genetic algorithms to automatically learn behaviors for controlling mobile robots, and we will be demonstrating some of those learned behaviors at the Exhibition.