Learning view graphs for robot navigation
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Fast Grid-Based Position TRacking for Mobile Robots
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Navigation mobiler Roboter mit Laserscans
Autonome Mobile Systeme 1997, 13. Fachgespräch
Routenbeschreibung durch Odometrie-Scans
Autonome Mobile Systeme 1998, 14. Fachgespräch
Online-Positionskorrektur für mobile Roboter durch Korrelation lokaler Gitterkarten
Autonome Mobile Systeme 1998, 14. Fachgespräch
Qualitative and quantitative representations of locomotion and their application in robot navigation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Exploiting qualitative spatial neighborhoods in the situation calculus
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
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This paper presents a simple approach for the acquisition and representation of spatial knowledge needed for controlling a semi-autonomous wheelchair. Simplicity is required in the domain of rehabilitation robotics because typical users of assistive technology are persons with severe impairments who are not technical experts. The approach proposed is a combination of carrying out so-called basic behaviors and the analysis of the wheelchair's track of motion when performing these behaviors. As a result, autonomous navigation in the user's apartment or place of work can be learned by the wheelchair by teaching single routes between potential target locations. This paper focuses on the analysis of the motion tracks recorded by the vehicle's dead reckoning system. As a means for unveiling the structure of the environment while the system is moving, an incremental generalization is applied to the motion tracks. In addition, it is discussed how two of such generalized motion tracks are matched to perform a one-dimensional self-localization along the route that is followed.