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<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/04.jpg"" width=""300"" /> Navigation method for mobile robots</div> We describe a navigation method for autonomous mobile robots and detail knowledge obtained through Tsukuba Challenge 2014 trial runs. The challenge requires robots to navigate autonomously 1.4 km in an urban area and to search for five persons in three areas. Accurate maps are important tools in localization on complex courses in autonomous outdoor navigation. We constructed an occupancy grid map using laser scanners, gyro-assisted odometry and a differential global positioning system (DGPS). In this study, we use maps as a graphical interface. Namely, by using maps, we give environmental information, untravelable low objects such as curb stones, and areas in nonsearches for “target” persons. For the purpose of increasing the map reusability, we developed a waypoint editor, which can modify waypoints on maps to fit a course to a situation. We also developed a velocity control method that the robot uses to follow pedestrians and other robot by keeping safety distance on the course. As a result, our robot took part five of seven official trial runs to get to the goal. This indicates that the autonomous navigation method was stable in the Tsukuba Challenge 2014 urban environment. </span>

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This paper describes the extraction method of low up and down steps and objects in urban area. For autonomous mobile robots, one of the most important technologies is a travelable area extraction. It is similar to extract the low obstacles. However, the position of the laser scanner is unstable since the most of road surface is rough. Therefore, low objects are often confused with the ground and it is difficult to distinguish them by measurement of height. On the other hand, the horizontal distance measured by the 3D-laser scanner has a difference. The measurement distance to the low object is shorter than ground. The distance table as reference is adopted. The scan data from a 3D-laser scanner is compared with the table to extract the low object. On the experiments in outdoor environment, the curb stones of the load under 10 cm were extracted correctly in several scene. This method contributes navigation scene that a robot avoids contact with most of the objects in urban area.

This paper describes a map-making method on position estimation based on scan-matching by using a laser scanner and a GPS device. In outdoor environment such as city areas, high-accuracy positioning on a map is required for achievement of autonomous navigation. However, mis-matching on the map sometimes occurs, and consequently a robot loses its own position. Although a GPS device, one of an absolute positioning device, is valid to estimate the position and attitude in certain accuracy, it often obtains error positions on multi-path problem which is occurred near by high-building. In order to autonomously generate an accurate map, the authors have developed a method of estimating the own accurate positions on a GPS device, referring to the own relative positions on scan-matching by using a laser scanner. Namely, the accurate map on grid is generated by precise structure information of the surrounding based on some accurate global positions. In this paper, performance of the map-making method is shown by experimental results which are evaluated on accuracy of the map on an actual environment.