Akihisa Ohya

Development of Fundamental Software Modules for Mobile Robots

The development of the fundamental software modules for mobile robots is presented in this report. This is a part of the METI project “Technology development of robot intelligence for next generation”. We’re aiming to develop reusable software and to open software business in future.

Filtering Algorithm for Reliable Localization of Mobile Robot in Multi-Sensor Environment

Localization problem of mobile robot in an environment of interest is one of research fields that are not still completely solved. Many methods to settle this problem are unceasingly being examined. Although a lot of researchers have dedicated themselves to this problem, it demands new but more accurate method by which mobile robot itself recognizes its own position while moving. It tries to find solution by making mention of various localization problems with estimation method in this chapter. Robot researchers had to recognize the current position information for movement control of mobile robot to last destination. It is being adopted from the conventional Kalman filter, which was proposed to reduce stochastic noise error, to complicated algorithms in the field of target tracking. The localization of the mobile robot occurred in various environments such as indoor, outdoor, warehouse, harbors, airports, and offices is based on estimation theories of target tracking field. The estimation method to be proposed in this chapter is mentioned with localization problem in the text. As the most widely used method for providing the current position of mobile robot, odometry is inexpensive method to implement in real time, but has vital demerit of accumulation of position error for long distance navigation. Various external sensors were used to supplement this problem, but the adopted sensors have also intrinsic errors. GPS, used as a global sensor in the outdoors, has a fatal weak point suffering from multi-path effects in the surrounding of buildings or trees. In this chapter, method for decreasing the above error occurred in localization problem are provided and embodied. This error is assumed as bias error. New nonlinear estimation method included in data integration method to reduce that error is derived and evaluated through experiment. The proposed integration method is provided to take full advantage of characteristics of mobile and outdoor environment sensors. 11

1P1-B15 Adaptive Human Direction Control Using Mobile Robot's Behavior

In this paper, we describe a robot system to control human detection. In our proposed system, the robot surveils a security area with equipped SOKUIKI sensor, ofdinarily. When it detect trespasser who is for the area, robot calculates his velocity vector, plans own path to forestall and interrupt him and starts to move along the obtained path. While moving the robot continues these processes for adapting change of situation. After arriving at a trespasser’s opposite position, the robot advises him not to be headed more and change his course. Our experimental results show the robot can estimate human’s future trajectory and calculate appropriate its own path.