Cognitive modeling for navigation of mobile robots using the sensory gradient concept
In order to build models reflecting accurately the structure of the real world Artificial Intelligent-based systems have the difficult problem of reducing the continuous, extremely complex sensory information into a discrete and simple model with the optimal computational burden. The new concept of sensory gradient is introduced in this paper to obtain computational models of the sensory information available for an autonomous mobile robot. The main objective is to guarantee that these models are simple and at the same time powerful enough to allow the performance of complex navigation tasks. We use the sensory gradient's module to identify situations and places that must be recorded (we call them Relevant Sensory Places or RSPs for short) and we build upon these RSPs a graph-based model of the universe that allows to develop navigation plans using plan-as-communication techniques. This novel approach is applied to the spatial reasoning problem as an aid to the autonomous navigation of mobile robots. Using a simulation environment the paper presents some empirical results which have been very encouraging. This novel approach has been successfully tested on a NOMAD-200 mobile robot platform.
Keywordsmobile robots autonomous learning topological maps ultrasonic sensors path planning
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