Research Article Construction of Fuzzy Map for Autonomous Mobile Robots Based on Fuzzy Confidence Model Jung-Fu Hou, 1 Yau-Zen Chang, 1 Ming-Hsi Hsu, 1 Shih-Tseng Lee, 2 and Chieh-Tsai Wu 2 1 Department of Mechanical Engineering, Chang Gung University, Taoyuan 33302, Taiwan 2 Department of Neurosurgery, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan Correspondence should be addressed to Yau-Zen Chang; zen@mail.cgu.edu.tw Received 15 January 2014; Revised 23 April 2014; Accepted 25 April 2014; Published 16 June 2014 Academic Editor: Leo Chen Copyright © 2014 Jung-Fu Hou et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper presents the use of fuzzy models to explicitly consider sensor uncertainty and fnite resolution in solving the SLAM (simultaneous localization and mapping) problem for autonomous mobile robots. Te approach establishes fuzzy confdence models in describing occupied obstacles and available space. Te problem is transformed into an optimization task of minimizing the alignment error between newly scanned local fuzzy maps and selected parts of a developing global fuzzy map. In aligning local fuzzy maps into a global fuzzy map, we developed a prediction strategy to crop the most potential part from the sensed local fuzzy maps to be overlapped with the global fuzzy map. A mobile vehicle equipped with a laser range fnder, the Hokuyo URG-04LX, is used to demonstrate the procedure of fuzzy map building. Experimental results show that the proposed architecture is efective in generating a comprehensive global fuzzy map, which is suitable for both human comprehension and path design during real-time navigation. 1. Introduction An essential task of an autonomous mobile robot is to determine its location and construct a map of its environ- ment, usually denoted as the work of solving the SLAM (simultaneous localization and mapping) problem [1–3]. Self- localization is about fnding the location of a robot in a map, while mapping is about constructing a referable map when the robot is moving in an unknown or changing environment. Autonomous map construction has been under extensive research for decades [4–9]. For instance, Chong and Kleeman [5] used a sonar sensor and a positioning sensor, and Jaradat and Langari [6] used a sonar sensor in developing the OGM (occupancy grid map) method, where the environment is simplifed into occupied and vacant grids. Guivant et al. [7] used encoders in cooperation with a laser range fnder for positioning. Davison and Kita [8] combined an accelerometer and two dynamic video cameras to construct irregular maps. Tomono [9] used baselines as the basis for a video camera to choose the characteristic points for map reconstruction. Tere are various kinds of sensors developed for these tasks, such as sonar [10, 11], laser range fnders [12], and video cameras [13]. Sonar is efective in detecting range, but only a narrow region can be detected at one time. Laser range fnders can efectively provide 2D environmental information at high refresh rate, up to 10 frames per sec, but may fail to sense black objects and complex 3D obstacles. Moreover, video cameras can emulate the capability of human eyes, but huge computing power is required for real-time implementation. An early work [14] proposed a fuzzy model for the sonar sensing, but the paper lacks detailed procedures for the SLAM problem. Inspired by the research, this paper presents the use of a fuzzy model to explicitly consider sensor uncertainty and fnite resolution of laser range fnders in solving the SLAM problem. Our proposed system is realized by establishing a fuzzy confdence model, which is composed of sensed obstacles and assured space based on sensor readings. Te SLAM problem is transformed into an optimization task of minimizing the alignment error between newly scanned local fuzzy maps and selected parts of a developing global fuzzy map. Te task is then solved by the Cuckoo search optimization algorithm [15, 16]. Being a nature-inspired meta-heuristic algorithm, Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 526781, 8 pages http://dx.doi.org/10.1155/2014/526781