This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a flying sensor but also as a tether attachment device. Two robots are connected with a tether, allowing the UAV to anchor the tether to a structure located at the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the poor traversability of the UGV by not only providing a wider range of scanning and mapping from the air, but also by allowing the UGV to climb steep terrains with the winding of the tether. In addition, we present an autonomous framework for the collaborative navigation and tether attachment in an unknown environment. The UAV employs visual inertial navigation with 3D voxel mapping and obstacle avoidance planning. The UGV makes use of the voxel map and generates an elevation map to execute path planning based on a traversability analysis. Furthermore, we compared the pros and cons of possible methods for the tether anchoring from multiple points of view. To increase the probability of successful anchoring, we evaluated the anchoring strategy with an experiment. Finally, the feasibility and capability of our proposed system were demonstrated by an autonomous mission experiment in the field with an obstacle and a cliff.

This paper proposes a method of searching for missing people in mountains by UAVs (Unmanned Aerial Vehicles) based on beacon signals. This method alternately updates the distribution of estimated target position and unknown parameters by particle filtering, and determines the next best observation location based on the idea of the uncertainty sampling. We will show how this method can be integrated with both of the range-based and range-free localization algorithms, and demonstrate the efficiency of it in a simulation study.

Self-localization using visual markers is widely used for precise control of Unmanned aerial vehicles (UAVs). This paper presents a novel method of 6-DoF pose estimation using a monocular camera and non co-planar markers. We conducted numerical simulations comparing pose estimation using co-planar markers and using non co-planar markers, and designed a non co-planar marker feasible for robotics applications. Our method uses a single CPU core and a field programmable gate array (FPGA) and can conduct fast and precise 6-DoF pose estimation at the same frequency of the camera input (100 fps). In addition, to show the performance of our method, we applied our 6-DoF pose estimation algorithm to a palm-sized quadcopter and flew it autonomously using only an on-board system with a monocular camera and three-axis gyro sensor. To the authors' knowledge, this is the first study on fully on-board autonomous flight control of an MAV in 6-DoF space without an accelerometer.

We describe an algorithm and hardware system of a 3D self-localization method for very cost effective hovering robots. The hardware system consists of very compact calculation and sensor units, instead of using a high performance calculation unit like a laptop PC. In the experiment, the comparison with the ground truth sensor is discussed and implementation of the system on a palm-sized quad-copter is studied to realize a very compact, on-board, fully autonomous quad-copter.

A visual analysis method for finding a variety of human networks in chronicles on the basis of different viewpoints is proposed. A vast amount of human experience records is stored all over the world. For understanding and using such chronicles effectively, it is necessary to find several relations embedded in records. In this article, we propose a chronicle analysis tool (CAT), which provides several views of records from different viewpoints. For such purpose, we introduce the notion of chronicle data and a visual analysis framework for embedded relations in chronicles. On the basis of these notions, we describe a chronicle analysis tool and show a variety of historical human networks from different viewpoints as examples of its implementation.

This paper introduces a method for bridging topics designed to facilitate generating stories over documents. First, we present a method for topic extraction based on narrative structure with k-means algorithm. We then model the story generation process and present a method for finding a bridge document between two documents.

This paper describes our vision and approach toward "entertainment blimps for everyone by everyone" through DIY prototyping. Our vision of "entertainment blimps for everyone" is that operators, who control the blimps, and spectators, who observe the blimps, could share and enjoy the activities together. Our vision of "entertainment blimps by everyone" is that both of developers, who are skilled in electronics or computer programming, and users, who are not, could reproduce and modify their own entertainment blimp projects.

In this paper, we describe the concept, practice, and support systems of “design with discourse” that is a method to design from the “ethics level.” First, we place the ethics level in the hierarchical representation of artifacts. Second, we systematize the concept of design with discourse. Third, we describe practice of design with discourse with two examples. Then, we present an overview of support systems that embody the concept of design with discourse. Finally, we conclude that design with discourse contributes to systematic design from the ethics level.

This paper proposes methods of visualizing the personal promotion records of Japanese government officials in medieval times. Our research is aimed at developing multiple visualization tools that facilitate discovery of exceptional promotions as a way of highlighting interesting or important factors behind historical events, or that enable comparison of various promotion patterns to aid in understanding the political background the period. We have developed the following tools for visualizing personal promotion records:(i) the personal promotion graph (PPG-I) enables visualization of the promotional transition of court nobles in medieval Japan, and (ii) the promotion pattern graph (PPG-II) shows various promotion patterns that help reveal typical patterns of promotion for court nobles in relation to a particular period or family. There are many techniques for visualizing data in different ways. These tools provide variations in viewpoints for analyzing the political background of the era and help trigger consideration of different scenarios.

This paper proposes several methods visualizing topics in documents, (i) Word Colony represents the dependency relationships among term occurrences in a target document. It helps users get an overview of a document. Using Word Colony with pictures gives users a more intuitive impression, (ii) topic sequence is a concatenation of Word Colonies for segmented documents. It shows plots as a story's topic transitions. (Hi) topic matrix represents relations among topics based on latent contexts within a collection of documents. Several visualization techniques enable users to visualize topics in a document in different ways. It gives variations of viewpoint and triggers the creative imagination.

This paper proposes a visualization tool, called Topic Tracer, to show topic transitions of not only a story existing in a document but also stories traversing over several documents. To visualize topic transition, first we have developed an analytical method to divide a document into parts. Then, we introduce a Word Colony to show topic terms in a whole or a part of document. It is a directed graph which shows the dependency relationships among terms occurrences in the target document. The Word Colony helps users to grasp the overview of a document. The concatenation of Word Colony gives plots of an existing story or crossing stories over several documents. This paper explains the concept and implementation of Topic Tracer using the minutes of the CUBE-SAT project, "Student-built Educational Pico-Satellite Design", in University of Tokyo

Autonomous map construction is one of the most fundamental and significant issues in intelligent mobile robot research. While a variety of map construction methods have been proposed, most require some quantitative measurements of the environment and a mechanism of precise self-localization. This paper proposes a novel map construction method using only qualitative information about "how often two objects are observed simultaneously." This method is based on heuristics--"closely located objects are likely to be seen simultaneously more often than distant objects" and a well-known multivariate data analysis technique-multidimensional scaling. A significant feature of this method is that it requires neither quantitative sensor measurements nor information about the robot's own position. Simulation and experimental results demonstrated that this method is sufficiently practical for capturing a qualitative spatial relationship among identifiable landmark objects rapidly.

In the field of design and creativity support, "externalization of mental space" has been recognized as an important challenge. In this paper, we tackle this challenge on musical composition, which is one of the important human creative activities. In our research, we focus on the analysis of cognitive processes in musical composition. That is, we analyze what the cognitive processes in musical composition are like and how the process is affected when a representation of information is changed. For this research, we propose a musical composition supporting system named "MACSS (MAcroscopic Composition Supporting System)" which offers a spatial representation of music to a composer to support his/her composition process. Comparing with ordinary musical editors, which give chronological (1-dimensional, score-metaphored) representation, this system provides a macroscopic view by locating phrases on the 2-dimensional space. We have investigated how the cognitive process changes when the spatial representation is introduced, i.e., the representation of information is changed. Moreover, we have analyzed the observed change microscopically, especially the processes of "mental fixation" and "mental leap". We have found how the spatial representation triggers mental leap escaping from mental fixation in musical composition.

Most current creativity support systems support short-term temporal thinking that is separate from users' daily activities. In this paper, we propose a system that supports long-term idea-generation in daily life. The system consists of two subsystems: a management system for problems and ideas called IdeaManager; and a personal information storage system called iBox. When information is registered in iBox, it searches related problems and ideas in IdeaManager and then presents the results. Then users try to generate or enhance ideas for automatically retrieved problems or ideas using registered information as the hint. Our target users are those who must generate novel ideas and acquire relevant information for a certain problem or theme, such as researchers or planners. To evaluate and enhance our system, we carried out a six-week experiment. During this period, subjects managed their problems and ideas in their daily activities. In the experiment, several methods of searching for related problems and ideas in IdeaManager were compared. Based on the results, we give some proposals for future systems.

This paper proposes a new map construction method based on rough information of "how often two objects are observed simultaneously". This method is founded on the combination of a simple and general heuristics "closely located objects are likely to be seen simultaneously more often than distant objects", and a well-known technique in the multivariate data analysis - multidimensional scaling. A significant feature of this method is that it requires little quantitative models nor precise sensor information, unlike conventional map construction methods. Simulation and experiment results suggest that this method is sufficiently practical for grasping a topological configuration of identifiable landmarks quickly.

When an autonomous robot is to learn its behavior, whether an appropriate state space is available or not is a critical issue for the flexibility and efficiency of the learning process. What is problematic is that it is usually very difficult to prepare such an ideal state space manually beforehand. We propose a new state space "reconstruction" method. With this, behavior-based robots can autonomously "rebuild" their state spaces after they accumulate behavior experience using initial state spaces. This reconstruction approach is more advantageous than the conventional state space construction methods or incremental state partitioning methods in that it achieves both the efficiency in the learning process and the optimality of the resultant behavior performance.

The paper describes a novel method for computer aided strategic concept formation in the early stage of product development. This method combines interactive evolutionary computing (IEC) and machine learning techniques. It enables the user to generate a creative and well-grounded product concept based on marketing strategy, while also stimulating the user to clarify the product concept idea he/she had in mind. The system called BICSS was developed based on this method. The proposed method has been qualitatively validated by a case study.

The authors have built and evaluated several systems to assist creative concept formation by professional engineers and scientists. Through experiments, it has become clear that a system that can reveal different viewpoints automatically is strongly needed by many users to support their creative activities. We are all surrounded by an almost infinite amount of information. If we can elicit different viewpoints from large information sources, we can arrive at new understandings that could not have been possible through discussions with other persons alone. This paper presents a system that automatically elicits and visualizes different viewpoints of authors concerning certain topics from a text database of journal and conference papers. Users can review different viewpoints of a topic of interest by looking at a space configured by the system. Through interaction with the system, users are expected to build their own concepts creatively. The effect of promoting creative concept formation and other benefits, such as enhanced information retrieval and knowledge sharing, have been validated by several experiments.

This paper describes a system with which a cognitive agent learns the way of abstraction and the policy of behavior selection simultaneously. We call the system situation transition network system (STNS). The system extracts situations and maintains them dynamically in the continuous state space on the basis of rewards from the environment. In this way, the system learns the way of abstraction in a dynamic environment. At the same time, the system stores results of transitions between situations and constructs a network of situations. This network is used for partial planning. At a point of time in the learning process, the system selects a behavior according to the partial plan. Because the planning is performed on a network of the abstracted situations, the agent with STNS does not have to deliberate details in planning. Furthermore, the agent can make a plan even on the early stage of learning because the planning is partial. Owing to the simultaneous learning with task executions the agent can adapt to the current task. The results of computer simulations are given.

A document retrieval system for researchers in the field of science and technology is described. The main feature of the system is the visualization of the semantic relations between documents and interactive operation to it. The system has a test database composed of a large number of journal and conference papers. It elicits keywords from each paper by an automatic indexing algorithm and then visualizes the relations between the papers and their keywords in a metric space by a statistical method. We carried out several experiments. Through them we have confirmed that our system is effective because it can assist the users in their query formation and modification while they use it. Our system can also assist the users in their creative research because they can find the relations between the papers and their keywords which they haven't noticed so far. In the experiment on effectiveness of retrieval we have confirmed that our system can realize high recall and precision ratio.

This paper describes a system named AA1 (Articulation Aid 1) which aids human users in the formation of new concepts in the domain of engineering and science. From the viewpoint of concept formation, one main process of creation is divergent thinking in which broad alternatives are searched, and another process is convergent thinking in which a unique solution is sought. From the viewpoint of human activities, creation also includes the aspect of collaboration among people and the aspect of individual reflection, although they are interrelated. AA1, the system presented in this paper, supports divergent thinking during individual reflection. Engineers and scientists usually scrawl many notes on paper while exploring new possible concepts in the divergent thinking process. A system is needed to reflect the fragments of concepts that are not articulated yet and thereby stimulate the formation of new concepts. AA1 builds a two-dimensional space from the words the user provides. Looking at this space and other precedent spaces, the user can form new concepts little by little. The main feature of AA1 different, from existing hypermedia systems and CSCW systems is the strategy for building the space presented to the user. The system is as nonprescriptive as possible, but it gives stimulation for the user to form concepts that he could not by using only pencil and paper. Experimentation has shown that the space which AA1 displays can effectively help the user to build new concepts. The most prominent effect is that empty regions in the space automatically configured by the system often lead to new concepts.>