@article{bd03c131f7d34693825b8662782f6f18,
title = "Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information",
abstract = "In this paper, we propose a new learning algorithm, named as the Cooperative and Geometric Learning Algorithm (CGLA), to solve problems of maneuverability, collision avoidance and information sharing in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGLA are three folds: (1) CGLA is designed for path planning based on cooperation of multiple UAVs. Technically, CGLA exploits a new defined individual cost matrix, which leads to an efficient path planning algorithm for multiple UAVs. (2) The convergence of the proposed algorithm for calculating the cost matrix is proven theoretically, and the optimal path in terms of path length and risk measure from a starting point to a target point can be calculated in polynomial time. (3) In CGLA, the proposed individual weight matrix can be efficiently calculated and adaptively updated based on the geometric distance and risk information shared among UAVs. Finally, risk evaluation is introduced first time in this paper for UAV navigation and extensive computer simulation results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.",
keywords = "Limited information, Path planning, UAV",
author = "Baochang Zhang and Wanquan Liu and Zhili Mao and Jianzhuang Liu and Linlin Shen",
note = "Funding Information: Baochang Zhang received the B.S., M.S., and Ph.D. degrees in Computer Science from the Harbin Institute of Technology, Harbin, China, in 1999, 2001, and 2006, respectively. From 2006 to 2008, he was a research fellow with the Chinese University of Hong Kong, Hong Kong, and with Griffith University, Brisbane, Australia. Currently, he is an associate professor with the Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He was supported by the Program for New Century Excellent Talents in University of Ministry of Education of China. His current research interests include pattern recognition, machine learning, face recognition, and wavelets. Funding Information: Wanquan Liu received the B.Sc. degree in Applied Mathematics from Qufu Normal University, PR China, in 1985, the M.Sc. degree in Control Theory and Operation Research from Chinese Academy of Science in 1988, and the Ph.D. degree in Electrical Engineering from Shanghai Jiaotong University, in 1993. He once held the ARC Fellowship, U2000 Fellowship and JSPS Fellowship and attracted research funds from different resources over 2 million dollars. He is currently an Associate Professor in the Department of Computing at Curtin University and is in editorial board for seven international journals. His current research interests include large-scale pattern recognition, signal processing, machine learning, and control systems. Funding Information: This work was supported in part by the Natural Science Foundation of China , under Contracts 60903065, 61039003 and 61272052, in part by the Fundamental Research Funds for the Central Universities , and by the Program for New Century Excellent Talents in University of Ministry of Education of China. The material in this paper was partially presented at the ICUAS conference. This paper was recommended for publication in revised form by Editor Ber{\c c} R{\"u}stem. ",
year = "2014",
month = mar,
doi = "10.1016/j.automatica.2013.12.035",
language = "English",
volume = "50",
pages = "809--820",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Ltd.",
number = "3",
}
