Self-localization Of Autonomous Mobile Robots Research Articles

全方位カメラと発光型ランドマークによる自律型移動ロボットの自己位置検出

全方位カメラと発光型ランドマークによる自律型移動ロボットの自己位置検出

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

Self-localization of Autonomous Mobile Robots using Information Buried in Environment.

Generally, in the self-localization method based on positions of landmarks, it is necessary for robots to manage the maps of the landmarks. In multiple robot systems, it is, however, difficult to keep the consistency among all the maps carried by the robots when the maps are updated frequently and dynamically. In this paper, we introduce Intelligent Data Carriers (IDCs) to realize map-free self-localization in multiple robot systems. The IDC is a portable data management device developed at RIKEN. By putting an IDC in the work area and storing its own position data in itself, robots can acquire the position of the IDC using local communication without refering to any maps. Detalied procedures to acquire and integrate various information are presented. The efficiency of the proposed method are discussed based on the results of the experiments. It is confirmed that this method is available for the robot to localize its own position in multiple robot environment.