Abstract
We introduce a hybrid algorithm for the autonomous navigation of an Unmanned Ground Vehicle (UGV) using visual topological maps. The main contribution of this paper is the combination of the classical bug algorithm with the entropy of digital images captured for the robot. As the entropy of an image is directly related to the presence of a unique object or the presence of different objects inside the image (the lower the entropy of an image, the higher its probability of containing a single object inside it; and conversely, the higher the entropy, the higher its probability of containing several different objects inside it), we propose to implement landmark search and detection using topological maps based on the bug algorithm, where each landmark is considered as the leave point for guide to the robot to reach the target point (robot homing). The robot has the capacity of avoid obstacles in the enviroment using the entropy of images too. After the presentation of the theoretical foundations of the entropy-based search combined with the bug algorithm, the paper ends with the experimental work performed for its validation.
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References
Lumelsky, V.J., Stepanov, A.A.: Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica 2(1-4), 403–430 (1987)
Lumelsky, V.J.: Sensing, intelligence, motion: how robots and humans move in an unstructured world. John Wiley & Sons (2005)
Lumelsky, V.J., Stepanov, A.A.: Dynamic Path Planning for a Mobile Automaton with Limited Information on the Environment. UEEE Transactions on Automatic Control AC-31(11) (November 1986)
Brea, J.P.F., Maravall, D., de Lope, J.: Entropy-Based Search Combined with a Dual Feedforward-Feedback Controller for Landmark Search and Detection for the Navigation of a UAV Using Visual Topological Maps. ROBOT (2), 65–76 (2013)
Maravall, D., de Lope, J., Fuentes, J.P.: Brea: Vision-based anticipatory controller for the autonomous navigation of an UAV using artificial neural networks. Neurocomputing 151, 101–107 (2015)
Maravall, D., de Lope Asiaín, J., Fuentes, J.P.: Brea: A Vision-Based Dual Anticipatory/Reactive Control Architecture for Indoor Navigation of an Unmanned Aerial Vehicle Using Visual Topological Maps. IWINAC (2), 66–72 (2013)
Maravall, D., de Lope, J., Fuentes, J.P.: Brea: Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images. Pattern Recognition Letters 34(14), 1719–1724 (2013)
Kawato, M.: Feedback-Error-Learning Neural Network for Supervised Motor Learning, Advanced Neural Computers (1990)
Kawato, M.: Internal models for motor control and trajectory planning. Neurobiology 9, 718–727 (1999)
Shannon, C.E.: A mathematical theory of communication. The Bell System Tech. J. 27, 379-423, 623–656 (1948)
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Maravall, D., de Lope, J., Fuentes, J.P. (2015). Visual Bug Algorithm for Simultaneous Robot Homing and Obstacle Avoidance Using Visual Topological Maps in an Unmanned Ground Vehicle. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_32
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DOI: https://doi.org/10.1007/978-3-319-18833-1_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
Online ISBN: 978-3-319-18833-1
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