To realize the ubiquitous robot and software avatar society in the near future, the concept of the MotionMedia contents sharing platform is proposed. MotionMedia is defined as a physical communication medium, that includes the actual motion of robots or physical end-effectors, as the fifth medium following audio, text, image, and video. MotionMedia should be valuable to realize better understanding and communication between humans via broadband networks through the support of advanced communication media. To share MotionMedia contents, especially motion data, a motion data modeling and formulation system for human-type robots based on eXtensible Markup Language (MotionXML) is developed. MotionXML leads to specifications to convey and reproduce robot motion via networks. It allows the same set of motion data to be shared by robots that have different structures and control interfaces. An application program interface (MotionAPI) as a common access interface for MotionXML is also developed. It allows application developers to handle MotionXML easily and to develop applications more rapidly.

We propose a template-matching method in which a parametric template space is constructed from a given set of template images then quickly matched to a reference image. In this method geometrical changes in an image object due to translation, rotation or scaling, and non-geometrical changes such as illumination variations or individual variation between objects are represented by template images in the parametric template space. The method also provides a subpixel matching scheme that yields a high-precision estimation of the object position. Experiments using real images have confirmed the effectiveness of the method.

This paper describes a robust and broadly applicable path-finding method that uses the range data of object surfaces adjacent to the path. The proposed method locates the path by using the shape defined by multiple sequences of less noisy parts of the range data. It is based on an originally devised algorithm for matching the shape of the object to that of the prototype model. This shape-matching algorithm can define the degree of similarity for curved surfaces three-dimensionally without assuming that the surface consists of parametric surfaces, such as planes, or conic surfaces. Segmentation of the surface is therefore not required. Moreover, this similarity can be defined even if the processing extent is not determined in advance. Consequently, the position of the paths used for robotic movement can be determined accurately from matching information by using less noisy parts of the range data, without putting restrictions on the shape of the object.>

An efficient encoding scheme for arbitrary curves, based on the chain code representation, has been proposed. The encoding scheme takes advantage of the property that a curve with gentle curvature is divided into somewhat long curve sections (segments), each of which is represented by a sequence of two adjacent-direction chain codes. The coding efficiency of the scheme becomes higher as the segments become longer, while a variable-length coding technique makes it possible to encode short segments without an extreme increase in code amount. An experiment with complicated curves obtained from geographic maps has shown a high data compression rate of the proposed scheme, whose code amount is about 50-60 percent of that required for the conventional chain encoding scheme.