Uncertainty Modeling And Perceptual Guiding For Safe Operation In Unknown Environments

• Main Authors: M. Joao Rendas, Jose Santos-Victor, Jean-Yves Tigli, Isabel Louetie, Luc Pronzato, Alexandre Bernardino
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: narval
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.2315; http://viriato.isr.ist.utl.pt/labs/vislab/publications/ps/98-ijics.ps.gz

This paper addresses the impact of uncertainty modeling and of the use of perceptual guidance on the navigational autonomy for vehicles operating in unknown environments, concentrating around four main topics: security control, planning, perceptual guidance and mapping. We present some of the results obtained in the European Union project NARVAL, which globally aims at using the perception capabilities of an autonomous vehicle to extend its autonomy range. One of the major issues addressed is related to guarantee vehicle's safety. We propose novel methodologies to (i) minimize the risk of loosing the vehicle and (ii) avoid destructive interference with objects present in its workspace. Planning under uncertainty is also studied, and algorithms to define trajectories for guiding the robot to a target region, exploring a delimited environment, or for optimizing autonomous beacon laying, are presented. The problem of using perceptual data for direct control is discussed in connection with the problem of visual tracking of a moving object. A new internal knowledge representation structure is proposed, representing the past robot's experience and the acquired knowledge relevant for future planning of the robot's displacements. The problem of maintaining the topological consistency of the internal representation is also considered.