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Content Provider | IEEE Xplore Digital Library |
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Author | Antonelli, G. Chiaverini, S. Fusco, G. |
Copyright Year | 1993 |
Abstract | One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method |
Sponsorship | IEEE Computational Intelligence Society |
Starting Page | 211 |
Ending Page | 221 |
Page Count | 11 |
File Size | 1685946 |
File Format | |
ISSN | 10636706 |
Volume Number | 15 |
Issue Number | 2 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-04-01 |
Publisher Place | U.S.A. |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mobile robots Vehicle driving Vehicle safety Navigation Kinematics Remotely operated vehicles Angular velocity Acceleration Path planning Motion planning path tracking Fuzzy control mobile robots motion planning navigation |
Content Type | Text |
Resource Type | Article |
Subject | Control and Systems Engineering Computational Theory and Mathematics Applied Mathematics Artificial Intelligence |
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