Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots |
| |
Authors: | Kiyohiko Hattori Eri Homma Toshinori Kagawa Masayuki Otani Naoki Tatebe Yasunori Owada Lin Shan Katsuhiro Temma Kiyoshi Hamaguchi |
| |
Affiliation: | 1.Resilient ICT Research Center,National Institute of Information and Communications Technology,Sendai,Japan;2.Hitachi Global Storage Technologies,San Jose,USA;3.Kyoto University,Kyoto,Japan;4.So-net Corporation,Tokyo,Japan |
| |
Abstract: | Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|