Abstract
Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. This paper introduced an algorithm for robots that explore the environment so that they can measure physical properties (dust in this paper). While the robot is moving, it measures the amount of dust and registers the value in the corresponding grid cell. At first, the robot moves from local maximum to local minimum, then to another local maximum, and repeats. To reach the local maximum or minimum, the robot simply follows the gradient which is estimated using perturbation/correlation. By introducing the probability of each grid cell, and considering the probability distribution, the robot doesn’t have to visit all the grid cells in the environment while still providing fast and efficient sensing. Robust estimation of the gradient using perturbation/correlation, which is very effective when an analytical solution is not available, is described. To verify the effectiveness of perturbation/correlation based estimation, the simulation results of gradient estimation which were compared to three other numerical methods are presented. The proposed algorithm was performed by the simulation and the comparison of exploration results according to the gradient estimation method is shown.
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Recommended by Editor Jae-Bok Song. This work was supported by the IT R&D program of MKE/IITA [2008-F-045-01, Development of Obstacle Detection and Indoor Localization System for the Blind] and also by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (NO. R01-2007-000-20977-0).
Jungyun Bae received the B.S. and M.S degrees from Hongik University in Seoul, Korea, in 2003 and 2005, respectively. She worked for one year for Division of Applied Robot Technology of Korea Institute of Industrial Technology (KITECH) in Ansan, Korea. Presently, she is pursuing a Ph.D. in the Department of Mechanical Engineering in Texas A&M University. Her interested research areas are Mobile Robotics and Intelligence Robots.
Gon Woo Kim received the B.S. degree in Electrical Engineering from Chung-Ang University, Korea in 2000, and the M.S. and Ph.D. degrees in the School of Electrical Engineering from Seoul National University in 2002 and 2006, respectively. Since 2008, he has been with Wonkwang University, where he is currently an Assistant Professor of Division of Electrical Electronic and Information Engineering. He worked as a Researcher with Korea Institute of Industrial Technology (KITECH) from 2006 to 2008. His research interests include mobile robotics, sensor fusion/integration, map building, and SLAM.
Sooyong Lee received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University, Seoul, Korea in 1989, and 1991, respectively, and the Ph.D. degree from MIT, Cambridge, MA, in 1996. He worked as a Senior Research Scientist at KIST and then as an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. He joined Hongik University, Seoul, Korea in 2003 and is currently an Associate Professor in the Mechanical and System Design Engineering Department. His current research includes mobile robot localization and navigation, and active sensing.
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Bae, J., Kim, GW. & Lee, S. Application of perturbation/correlation based gradient estimation for environment exploration. Int. J. Control Autom. Syst. 7, 233–241 (2009). https://doi.org/10.1007/s12555-009-0209-5
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DOI: https://doi.org/10.1007/s12555-009-0209-5