RoboCup Project
    Robotic soccer is a new common task for artificial intelligence
        (AI) and robotics research. The robotic soccer provides a good
        testbed for evaluation of various theories, algorithms, and agent

     We're interested in the following research issues :

Our robot:
  We constructed a lightweight mobile robot with visual, tactile sensors, TCP/IP communication device, and portable PC (Toshiba Libretto100) where Linux is running.

Takayuki Nakamura and et. al,
Development of A Cheap On-board Vision Mobile Robot for Robotic Soccer Research,
Proc. of IROS'98, pp.431--436, 1998

Behavior learning algorithm for mobile robot:

  Reinforcement learning has been receiving increased attention as a method with little
or no a priori knowledge and higher capability of  reactive and adaptive behaviors through interactions between the physical agent and its environment. The common reinforcement learning method like a Q-learning,  normally needs well-defined
quantized state and action spacesto converge. This makes it difficult to be applied to real robot tasks because of poor quantization of state and action spaces. Even if it can be applied to real robot tasks, performance of robot behavior is not smooth, but jerky due to quantized action commands such as forward, left turn and so on.
  To deal with this problem, we proposed a continuous valued Q-learning (hereafter, called CVQ-learning) for real robot applications. This method utilized a function approximation method for representing a action value function. In this work, we point out that this type of learning method potentially has a discontinuity problem of optimal actions given a state. To resolve this problem, we also proposed a method for estimating where discontinuity of optimal action takes place and for refining a state space for CVQ-learning. To show the validity of our method, we apply the method to a vision-guided mobile robot of which task is to chase the ball. Although the task is simple, the performance is quite impressive.

M. Takeda, T. Nakamura, M. Imai, T. Ogasawara and M. Asada,
Enhanced Continuous Valued Q-learning for Real Autonomous Robots,
Proc. of Int. Conf. of The Society for Adaptive Behavior 2000,
pp.195--202, 2000.

Adaptive vision system:

  Robust visual tracking is indispensable for building up vision-based robotic system. The hard problem in visual tracking is performing fast and reliable matching of the target every frames.  A variety of tracking techniques and algorithms have been developed.
Among them, color-based image segmentation and tracking algorithm seems to be practical and robust in real world, because color is comparatively insensitive to the presence of changes in scene geometry and occlusion.
  Most of existing mTakayukiethods need bootstrap process to model sample  color distribution for image segmentation.  That is, in order to calibrate their statistical model for image segmentation, these visual tracking systems need to wait for color data to accumulate enough even if these systems work in dynamic environment.  In order to keep visual tracking systems running in real environment,  on-line learning method for acquiring some models for image  segmentation should be developed.
  We developped an on-line visual learning method for color image segmentation and object tracking in dynamic environment.  Such on-line visual learning method is indispensable for realizing a vision-based system which can keep running in real world.
To realize on-line learning, our method utilizes fuzzy ART model which is a kind of neural network for competitive learning.  Although color image we deal with is represented by YUV color space,  YUV space is not suitable for inputs of fuzzy ART model. For this reason, YUV  space is transformed to a certain color space. This transformation enables fuzzy ART model to segment color image in on-line. As a result, even if surroundings such as lighting condition changes, our on-line visual learning method can perform color image segmentation and object tracking correctly.

Takayuki Nakamura and Tsukasa Ogasawara,
On-Line Visual Learning Method for Color Image Segmentation and Object Tracking,
Proc. of IROS'99, pp.222--228, 1999.

Multiple Omni-vision system:

  In order that multiple robots operate successfully in cooperative way,  such robots must be able to localize themselves and to know where other robots are in a dynamic environment. Furthermore, such estimation should be done in real-time. An accurate localization method is a key technology for successful accomplishment of tasks in cooperative way. Since robots in the multiple robot system can share the observations by communicating each other, each robot in such multiple robots can utilize redundant information for localizing itself. Therefore, such robots can solve the localization problem more easily than a single robot does. On the other hand, in multiple robot system, it is difficult to identify other robots by using only visual information.
  We developped a new method for estimating spatial configuration between multiple robots in the environment using omnidirectional vision sensors. Even if there was an obstacle in the environment where the multiple robots were located, our method could estimate absolute configuration between robots in the environment with high accuracy.
Our method is based on identifying potential triangles among any three robots using the simple triangle constraint. In this work, in order to enhance functions of autonomous decentralized system, our method gives a self-localization capability to a single robot among multiple robots, in addition to capability of estimating a relative configuration from the sharing observation.  Due to this self-localization capability, our method can use one more constraint which is called "enumeration constraint." This constraint drastically eliminates impossible triangles and makes our algorithm fast and robust.
After potential triangles are identified, they are sequentially verified using information from neighboring triangles. Finally, our method reconstructs absolute configuration between multiple robots in the environment using the knowledge of landmarks.

T. Nakamura, A. Ebina, T. Ogasawara and H. Ishiguro,
Real-time Estimating Spatial Configuration between Multiple Robots by Triangle and Enumeration Constraints,
Proc. of IROS 2000, pp.2048--2054, 2000 .

T. Nakamura, M. Oohara, T. Ogasawara and H. Ishiguro,
Fast self-localization method for mobile robots using multiple omnidirectional vision sensors.
Machine Vision and Applications, Vol.14, No.2, pp.129--138, 2003.