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Scene Reconstruction and Robot Navigation Using Dynamic Fields

Scene Reconstruction and Robot Navigation Using Dynamic Fields In this paper, we present an approach to autonomous robot navigation in an unknown environment. We design and integrate algorithms to reconstruct the scene, locate obstacles and do short-term field-based path planning. The scene reconstruction is done using a region matching flow algorithm to recover image deformation and structure from motion to recover depth. Obstacles are located by comparing the surface normal of the known floor with the surface normal of the scene. Our path planning method is based on electric-like fields and uses current densities that can guarantee fields without local minima and maxima which can provide solutions without the need of heuristics that plague the more traditional potential fields approaches. We implemented a modular distributed software platform (FBN) to test this approach and we ran several experiments to verify the performance with very encouraging results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Scene Reconstruction and Robot Navigation Using Dynamic Fields

Autonomous Robots , Volume 8 (1) – Oct 15, 2004

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References (36)

Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1023/A:1008992902895
Publisher site
See Article on Publisher Site

Abstract

In this paper, we present an approach to autonomous robot navigation in an unknown environment. We design and integrate algorithms to reconstruct the scene, locate obstacles and do short-term field-based path planning. The scene reconstruction is done using a region matching flow algorithm to recover image deformation and structure from motion to recover depth. Obstacles are located by comparing the surface normal of the known floor with the surface normal of the scene. Our path planning method is based on electric-like fields and uses current densities that can guarantee fields without local minima and maxima which can provide solutions without the need of heuristics that plague the more traditional potential fields approaches. We implemented a modular distributed software platform (FBN) to test this approach and we ran several experiments to verify the performance with very encouraging results.

Journal

Autonomous RobotsSpringer Journals

Published: Oct 15, 2004

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