Recent development of motion control technology brings robots to our daily life. In the daily life, physical distance between humans and robots get very closer. This closed distance leads new issues in human robot interaction, such as safety issue and communication issue. The authors developed haptic sensing system called haptic armor. The sensory system can detect any contact on the robot surface and also it can distinguish the haptic command using contact information. The haptic command is the command which is delivered by physical contact to the robot. The present paper proposes the haptic commands for human robot interfaces. Furthermore, the design algorism of the haptic command is established in the paper.

A method for range sensing that projects a single pattern of multiple slits is described. Random dots are used to identify each slit. The random dots are given as randomly distributed cuts on each slit. Thus, each slit is divided into many small line segments. Segment matching between the image and the pattern is performed to obtain 3-D data. Using adjacency relations among slit segments, the false matches are reduced, and segment pairs whose adjacent segments correspond with each other are extracted and considered to be correct matches. Then, from the resultant matches, the correspondence is propagated by exploiting the adjacency relationships to get an entire range image.>

A new multidimensional data structure, multidimensional tree (MD-tree), is proposed. The MD-tree is developed by extending the concept of the B-tree to the multidimensional data, so that the MD-tree is a height balanced tree similar to the B-tree. The theoretical worst-case storage utilization is guaranteed to hold more than 66.7% (2/3) of full capacity. The structure of the MD-tree and the algorithms to perform the insertion, deletion, and spatial searching are described. By the series of simulation tests, the performances of the MD-tree and conventional methods are compared. The results indicate that storage utilization is more than 80% in practice, and that retrieval performance and dynamic characteristics are superior to conventional methods.>

An efficient spatial data management method for non-zero size spatial objects, called R(Region)MD-tree, is proposed. In this method, a graphic object in an N-dimensional space is represented as a point in a 2*N dimensional space by the centroid and the extension in each axial direction. The point data are managed by the MD-tree after applying the coordinate transformation. The point location problem or range searching in the original N-dimensional space are carried out by the hyper-rectangular range searching to the point data in the 2*N dimensional space. In this representation and data management, data objects are sorted and classified according to not only the location but also the size of the data. By the simulation experiments, the performances and efficiencies of the RMD-tree are compared with the conventional MD-tree and the R-tree. As the data objects become larger, the RMD-tree is superior in the searching speed by 2 approximately 4 times to the conventional ones, and the performances are almost comparable even if the objects are points or small graphic data.>

A multilayer (ML) data structure, which is a hierarchical data structure for N-dimensional data classified into layers, is proposed. In the ML structure, a node structure is extended so that data can be stored not only in the leaves (terminal nodes) but also in internal nodes by adding a layer-management mechanism. The creation, management, and searching methods of the ML structure are described, taking two-dimensional point data classified into layers as an object. The management of line data or expansion to n(n>2)-dimensional data can be easily realized. Good retrieval performances are obtained in the case of a spatial search for a specific layer's data and that for plural layers' data.>

A multidimensional data structure, the MD-tree (multidimensional tree), is proposed. The MD-tree is developed by extending the concept of the B-tree to multidimensional data, so that the MD-tree is a height-balanced tree similar to the B-tree and, even its worst-case storage utilization is more than 66.7%. A description is given of the structure of an MD-tree and the results of a series of simulation tests which indicate that the storage utilization is more than 80% in practice and that the retrieval performance and the dynamic characteristics are superior to those of conventional methods.>