Wheeled vehicles’ velocity updating by navigating on outdoor terrains

This paper addresses the velocity control of wheeled vehicles regarding the terrain features, beyond detection and avoidance of the obstacles as most current works do. Terrain appearance average is used to enable the wheeled vehicle to adapt velocity such that, as speedy as possible, it safely navig...

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Bibliographic Details
Published in:Neural computing & applications 2011-10, Vol.20 (7), p.1097-1109
Main Authors: Alvarado, Matías, García, Farid
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Global analysis, analysis on manifolds
Image Processing and Computer Vision
Learning and adaptive systems
Mathematics
Multivariate analysis
Original Article
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
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Summary:This paper addresses the velocity control of wheeled vehicles regarding the terrain features, beyond detection and avoidance of the obstacles as most current works do. Terrain appearance average is used to enable the wheeled vehicle to adapt velocity such that, as speedy as possible, it safely navigates. The vehicle velocity adaptation imitates the human beings’ driving behavior regarding the terrain features: humans use a quick and imprecise estimation of the terrain features but enough to drive–navigate without sliding or falling. A fuzzy neural network sets the vehicle velocity according to average estimations of terrain roughness. The terrain textures are modeled by the principal components that are enough to use pattern recognition for navigation purpose. One set of tests is executed using a small, wheeled robot, which adjusts velocity while navigating on surfaces such as ground, ground with grass, and stones paving. The other tests are done using images of roads of ground, concrete, asphalt, and loose stones, which are video filmed from a real car driven at less than 60 km/h of velocity; by applying the present approach, the required time/distance ratio to smoothly velocity change is granted.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-010-0429-x