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Position and orientation data are critical for guiding and managing cooperative robot tasks in the greenhouse. Errors, however, associated with current positioning and orientation systems, such as indoor GPS compasses or the sensitivity of magnetic direction sensors to magnetic materials, limit their application in greenhouses. In this research, we develop and evaluate a positioning and orientation system using a sound signal and two different receiver architectures in a greenhouse. The results obtained: a positioning accuracy of 23.0 ± 5.5 mm and average orientation accuracy of 3.43 ± 1.67° and 1.58 ± 0.86° for these two methods, demonstrate this spread spectrum sound based system has the potential to be deployed on small distributed greenhouse robots.

Quadcopters can play an important role in precision agriculture; mostly having been researched and commercialized for the open field conditions. While greenhouse applications have been limited by the positioning system. Conventional GPS positioning systems are difficult to use in greenhouses. Spread Spectrum Sound-based Positioning System (SSSPS) on the other hand, has the potential to be used on a quadcopter in a greenhouse. In this research, we evaluated whether acoustic noise from a quadcopter over wide frequency range would interfere with the SSSPS operation. We set a microphone on the quadcopter, and evaluated the SSSPS system for quadcopter noise tolerance with several signals. The results demonstrate the navigation system can tolerate quadcopter noise over distances up to 15 m between the tweeter and the microphone on the quadcopter with the RMSE of 5.9 mm.

Indoor navigation plays an important role in agricultural robots working in greenhouses. The SS-sound system, where Time of Arrival (TOA) of spread spectrum modulated sound is used to localization, is one of the effective methods for indoor navigation, but there is a called near-far problem where the sound waves emitted from distant anchors are disturbed by the one emitted from nearby anchors. Transmitting the SS-sound from multiple anchors using Time Division Multiple Access (TDMA) is effective to solve the near-far problem. However, the TOA from multiple anchors cannot be obtained simultaneously, so that the localization cannot be performed. To solve the problem, we propose a method for combining the TDMA-based SS-sound system and inertial navigation system. A simple numerical example simulating the movement of a ground-moving robot was presented to clarify the effectiveness of the proposed method.

Electrically powered unmanned aerial vehicles (UAV) are useful in performing inspection at various infrastructures or plants. A power supply through a tether cable is effective in extending flight time. During inspection activities, some or all satellites may be occluded. UAVs for inspection must be operated even in GPS-denied areas; therefore, a navigation system for GPS-denied areas is required. Depth information cannot be obtained correctly by a monocular camera. The ARToolkit, which is widely applied in augmented reality (AR), is not sufficient as a UAV navigation system. We have proposed a hybrid navigation method that integrates the ARToolkit and an inertial measurement unit (IMU). An analytic solution for both the worst and best estimation of yaw angle can be obtained by simple computation and helps remove outliers in measurements. From experimental results, it was proven that position estimation using the proposed method corresponded reasonably; however, it was necessary to correct the difference between the camera origin and the body’s center of gravity.

Small distributed robots have the potential to be commercialized in greenhouse operations due to their low cost, high efficiency and light weight compared to conventional greenhouse robots. In order to guide the path of these distributed dynamic robots, a positioning system with a centimetre accuracy is needed. While a GPS navigation system does not perform well in such an environment, the passive Spread Spectrum Sound-based Local Positioning System (SSSLPS) using FDMA (Frequency Division Multiple Access) does have the potential to accurately localize robots in such an environment if certain limitations can be overcome. One such issue is the Doppler effect. In this paper a Doppler shift compensation (DSC) algorithm, which applies a carrier frequency detection method, was used for passive SSSLPS (FDMA), and evaluated in terms of its ability to compensate for the frequency shift of the sound signals when measuring distance and position. The results show the applied DSC algorithm can measure distance using a compensated reference signal at 0.1–0.8 m/s constant velocity and 0.06–1.06 m/s2 acceleration. The positioning error of the passive SSSLPS (FDMA) mounted on the crawler robot was 50.3 ± 22.3 mm accuracy, while the failed detection rate was 2.6%. These results suggest the applied DSC algorithm can effectively compensate for Doppler shift when using passive SSSLPS with FDMA for dynamic robot positioning.

In this paper, we present a discussion on smart parking systems in urban traffic networks. Reduced vehicle speed when drivers search for parking lots contributes to increased traffic jams in recent urban traffic networks. We aim to shorten the searching time for parking lots, which is one of the causes of traffic jams, by allocating available parking lots to drivers. Furthermore, we design a dynamic parking fee system and redistribute parking lots to equalize the profits earned by managers of multiple parking lots in traffic-congested areas. We then finally confirm the effectiveness of the proposed algorithm through numerical simulations.

The present paper investigates train drivers’ eye-gaze data to find important features to explain behavioral differences between experienced and inexperienced drivers. The obtained eye-gaze data contain too complex transition structure to find any meaningful patterns that might be common across or differentiate drivers. The Markov Cluster Algorithm, which is an unsupervised algorithm for graph clustering, is therefore utilized to divide such a structure into clusters that represent constituent eye-gaze patterns of frequent occurrence. As a result, a common eye-gaze pattern was identified to represent a perception tactic that drivers would repetitively move their gaze ahead soon after looking at other specific areas. Comparing cluster structures extracted with different clustering parameter settings clarified that all of the drivers implement this tactic more or less, but that they are different in that the experienced drivers can consistently follow it while the inexperienced drivers can not.

Train driver's attentive behavior can be considered to reflect differences in his/her level of expertise. In this paper, the authors investigate train drivers' eye-gaze movements to identif...

This paper presents a novel human-system collaborative robot-programming platform, where case-based reasoning (CBR) and explanation-based learning (EBL) are integrated together. CBR takes advantage of its unique CBR cycle to achieve the knowledge acquisition and reuse in the form of case, realizing efficient robot programming with the support of experienced human experts. EBL optimizes the rule structure in the knowledge base through learning in retrieving speedup rules in order to accelerate the case adaptation process. Feasibility of this proposal is verified via a number of experiments that allow the system to output both schemata for generalized robot programming tasks whose calculation rules are adaptive enough so that it can be applied to novel task inputs. Moreover, it is shown that our system is adaptive to the increase of the cases processed and be able to tackle with the learning utility problem.

In recent automated production lines, industrial robots are required to recover from unexpected errors autonomously. In this paper, a hierarchical planning system that realizes autonomous error recovery of robots is proposed with some virtually demonstrated results. In order to realize a variety of flexible error recovery functions, a partial order planning scheme as well as a “deliberate” re-planning capability based on semantic information on the production equipment and/or workpieces are proposed. Its effectiveness in cooperative work simulations of multiple robotic manipulators is verified.

In the present study, dominant eye-gaze patterns in professional train drivers' visual behavior are investigated using the Markov Cluster (MCL) algorithm. Applying the MCL algorithm results in a common gaze pattern showing a sort of perception tactic the drivers usually follow. The drivers repetitively move their gaze ahead soon after looking at somewhere else, independently of their years of experience. They are, however, found different in that experienced drivers can consistently follow the tactic while inexperienced drivers cannot. Time variation in the number of attentive pattern deviations demonstrates that, as well as the inexperienced drivers made higher frequency and larger fluctuations of pattern deviation, there were several particular segments in the route in which intensive pattern deviations arose in common. Inexperienced drivers would make intensive pattern deviations in such route segments that may have higher requirements of their cognitive resources.

In the present paper, additive syntheses of two tones are examined in order to look into what data-to-sound mapping design is better to acoustically communicate varying quantities of a multivariate system. The research focus is placed on the effectiveness of mappings for listeners to discriminate one variable component of sonified data from another. Dyads of pitches are tested through a couple of experiments where a special attention is paid to the “dissonance” of two tones. The first experiment investigates its effects on the perception of two static tones presented simultaneously. The second experiment investigates the effectiveness of choosing dissonant pitch combinations to sonify two quantities of a system being controlled manually. Experimental results demonstrate that the dissonance of tones can improve the perception of changes in individual variates out of a synthesized sound but that the frequency interval between two tones has stronger effects in dynamic situations.

Adaptation to environmental changes, such as wind, plays a very important role in improving the reliability of autonomous unmanned helicopters. Adaptive yaw (heading) control for an autonomous helicopter is discussed in this paper. The control structure is based on a hierarchal scheme that utilizes an inner yaw feedback control loop plus an outer feedback loop. The outer loop estimates the direction of the airspeed using roll angle and roll angular rate. Stable coupling in yaw and roll motion is induced by the proposed controller to improve the stability of the helicopter’s flight. Turning utilizing the proposed adaptive control system is discussed in particular. Results of flight experiments show that bifurcation of the helicopter’smaneuvering in turning occurs depending on airspeed. The results indicate that the autonomous unmanned helicopter can select a turning maneuver that is suitable for the environmental conditions, thus stabilizing its flight.

In this article, we focus on the coordinative structure of human behavior, which contributes to specifying dynamics from time-series kinematic data. We propose a method for the extraction of dynamical interaction from time-series data of human behavior using Singular Spectrum Transformation. Using the proposed method, human behavior can be described as a letter string whose letters indicate where the motion segmentation is detected. We also discuss a method of extracting coordinative structures by constructing multiple alignments from the timing structure of extracted motion change points. To confirm the effectivity of the proposed method, the results of motion analysis are shown.

To localize a rover on small planetary body, a method using round-trip propagation delay of radio waves is most promising. In order to improve the estimation accuracy, it is necessary to estimate rotational motion of the small planetary body. A method of localization has been expanded to estimate also the rotational parameters of the small planetary body. The expanded problem includes complex nonlinear and dynamical issues that it cannot be solved analytically. So, the estimation problem has been formulated as an optimization problem to minimize the loss function defined based on the estimation errors derived in comparison with actual measurement data. A solution for the optimization problem has been proposed, which uses Powell's conjugate direction method for local searches. Although this solution does not require calculation of derivatives, it requires a large amount of computation since several forward calculations of the state are required for each minimum search. In this paper, a method to select measurement data is described, which provides as accurate estimation as the original results with reducing the computational amount. The main idea of selection is to conserve the sensitivity of the measurement data. The proposed method to select data is compared with the results using the decimated data of equal interval. Simulation results and experimental results are shown to evaluate computational reduction and estimation accuracy.

This study analyzes the work domain of position teaching in terms of means-end relations for the purpose of developing a practical support tool for human workers engaged in industrial robot teaching. A mechanical explanation model is introduced into the analysis to capture the force-displacement relationship inherent in and informative on the work system. The resulting models are used for the analysis of the robot operators’ decisions in search of accurate operation positions, and this in turn helps to clarify a rational operation strategy for making use of an invariant frame of reference in the position search space. After these findings, a prototype GUI is proposed that can provide effective information supports for different granularities of the activity in position teaching. Based on the principles of Ecological Interface Design, the proposed GUI represents the activity-related information in a way that encourages the robot operators’ intuitive and strategic operations.

Investigation of small planetary bodies is now attracted and some missions on asteroids and comets have been realized. Such bodies are full of geographical features that next demand would be direct surface investigation by a rover. In order to navigate the rover to the specific points, the localization of the rover is required. However, conventional localization method is not adequate on small bodies whose size is less than 1[km]. One of the conventional methods, localization by camera images obtained on the rover needs a map made by a mother spacecraft. The resolution of the map is not enough for the rover's local view. Another method, localization by observing the movements of stars and the sun has little accuracy. Global positioning system (GPS) is not practical for small planetary bodies because it needs several orbiters. In this paper, we propose a reasonable method which localizes the rover on a small body with reasonable accuracy, with reasonable time and with reasonable resource. This method measures two way range between a rover and a mother spacecraft repeatedly. Even this method uses only one orbiter, the localization accuracy is estimated about 1[m] assuming ITOKAWA-size asteroidwhoseradiusisatmost600[m].

Measurement of the heading free from magnetic declination using several GPS antennas is widely used, but it is not suitable for small unmanned aerial vehicles because of limited payload and body length. In this paper, we propose two methods to estimate magnetic declination. One is for UAV for 3D terrain mapping with a laser scanner. Measurements which are collected from different directions are matched to estimate magnet declination. The other uses an IMU and one GPS antenna. It consists of 1) estimation of acceleration of motion from GPS measurements and 2) estimation of magnetic declination using the estimated acceleration and IMU measurement. We show that the estimation of magnetic declination results in reduced QUEST, an eigenvalue problem of a 2 × 2 real matrix using a quaternion which expresses rotation. Experimental results of flight control of an unmanned helicopter demonstrate the effectiveness of proposed methods.

A control system for an autonomous robot, which consists of several cooperative modules whose combination and structures change dynamically through interaction with environment, is discussed in this paper. We propose a method to design a control system by modular learning based on Lyapunov design method. In our method, modules that have different property and the dynamic relations between modules to achieve the task are learned. Numerical simulations and flight experiment of an autonomous aero-robot demonstrate the effectiveness of the proposed method.

In this study, we applied the methodology of Interpretive Structural Modeling (ISM) in order to extract eye-gaze movement patterns characteristic of experienced train drivers in their visual attentive behavior. Application of ISM to eye-gaze data generates multilevel structural models that represent dominant transition patterns hidden in complex eye-gaze movements. The extracted structural models demonstrate that, as a basic perception strategy, the drivers repetitively moved their gaze ahead soon after looking at some other specific area. What differentiates the experienced and younger drivers is that the former group of drivers can consistently follow this basic perception strategy during driving while the latter can not. The experienced drivers can balance their limited attentional resources between vigilance to the external environment and other operational requirements, distributing their attention effectively and efficiently among various areas of interest.

This paper describes methods to develop autonomous flight control systems for an aero-robot using an unmanned helicopter RMAX developed by YAMAHA Co., LTD. Such systems are difficult to develop because helicopter dynamics are nonlinear. We propose an efficient method to design controllers by training neural networks. The proposed method is easy to use with online training or adaptive controllers to compensate for undesirable effects not modeled or sudden changes of the controlled object and environment, making the control system highly reliable. Results of flight experiments demonstrate the effectiveness of our approach.

Autonomous unmanned helicopters are useful in various disaster prevention activities. 3D terrain mapping system is one of the most promising applications of autonomous unmanned helicopters. To improve the accuracy of the terrain mapping, it is necessary to improve the estimation accuracy of the attitude. In this paper, we focused on minimal representations of the attitude, which do not require unprofitable procedures in filtering, such as normalization. We defined generalized Rodrigues parameters, which are generalizations of Rodrigues and modified Rodrigues parameters. Generalized Rodrigues parameters were compared with Euler angles, which are the most well-known minimal representation of the attitude. Through numerical simulation results, properties of attitude estimation systems using those attitude representations are discussed in this paper. Finally, we concluded that generalized Rodrigues parameters are suitable for 3D terrain mapping.

Human motion is affected by some dynamic parameters, which represent the interaction between the body and the environment. Human can estimate the scale of the dynamic parameter from observation of motions. In this paper, a method to extract adaptive components in the motion is proposed. This method provides precise reconstruction of behavior using the adaptive components. However, it is not proven that these reconstructed behaviors affect people to perceive the dynamic parameters properly. A method to estimate the cognitive scale of the dynamic parameter and a method to extract the characteristic components necessary for perception are also proposed. The proposed methods have been applied to loading motion. Those results have suggested that the extracted characteristic components, that is the cognitive index, are not identical with the adaptive components. It was found that motions reconstructed on the basis of the cognitive indices lead to appropriate perceptions of the dynamic parameters.

This study applies information scent model to the domain of menu-based interfaces and demonstrates its ability to predict user behavior within a menu hierarchy. The information scent model addresses how people make use of perceptual cues like snippets of text in order to make their navigation decisions in choosing a particular option and gives a formula to measure the relevance of each option to a particular goal concept. The model was used to analyze the data collected in a set of experiments to study characteristics of users' menu selection behaviors. Users have to base their navigation decisions on the lexical similarity found on menu labels with their intended use when they have not learned the structure of the menu system yet. Our analysis confirmed that the information scent distribution can give a useful indicator to predict which item in a menu such users may select.

Autonomous unmanned helicopters are useful in disaster response activities, but their reliability needs improvement to broaden its application area. GPS-INS hybrid navigation systems using Kalman Filter have been used to improve the reliability of autonomous unmanned helicopters, but they are not robust against the absence of GPS measurements. The vertical accuracy of GPS measurement is less than horizontal ones. The vertical channel of INS is unstable. Adding the altitude sensor to GPS-INS hybrid navigation system is expected to increase the accuracy and improve reliability. Barometric altimeter is not so accurate, but it is widely used because of its convenience. For unmanned helicopters, though, it is known that the barometer altitude deviate downward from the actual altitude, where the helicopter is close to the ground. This phenomena is due to the ground effect. This paper describes the method to build GPS-INS-BARO hybrid navigation system, which takes into account the ground effect for the autonomous unmanned helicopter. We adopted a centralized approach to integrate different sensors with asynchronous measurement updates. The parameter sets for successful estimation were shown using numerical simulation results.

To avoid breakdowns in human-system interaction on the way to the target item, menu systems must be designed as compatible with his/her way of thinking. Concerning this issue, this study focuses on conceptual dependencies between elements involved in device functions. Dependency structures are examined to design interaction processes in which the user can narrow down options reasonably and effectively. A small set of common structures to classify device functions were extracted from the analysis of a wide variety of appliances' functions. The proposed method makes use of those structures to generate a menu hierarchy, facilitating the users applying the mental model they have already developed from their past experience to another new search context.

To improve reliability of autonomous unmanned helicopter, GPS-INS hybrid navigation systems using Kalman Filter are utilized. Barometer can be integrated into the hybrid navigation system, but the problem is that there is no sufficient model which can describe the relation between the altitude and the air pressure in ground effect. The purpose of this paper is to develop a measurement model of a barometer on an unmanned helicopter which can be used in the hybrid navigation system for autonomous unmanned helicopters. In particular, we propose that the model equation of atmospheric pressure under ground effect can be applied to estimate the terrain clearance of the helicopter. Results of flight experiments show that the proposed method is effective when the helicopter is close to the surface.

In this chapter, we summarize the findings and knowhow in the individual developments of rescue robots and attempt to establish design guidelines for the human interface for rescue robots. The guidelines for image display were established on the basis of some case studies in the DDT project and RoboCupRescue. Since the established guidelines are general and independent of the purpose of a robot, we decided to build a standardized image display prototype. After introducing some implementation examples based on our guidelines, the standardized image display prototype is presented. The established guidelines would be useful for the future development of rescue robots. Further, the prototype image display is a good example of a standardized interface testbed.

This research focuses on supporting an analyst's activity of interpreting the contents of existing incident reports. During this activity, analysts are always predicting expected scenarios of the incidents at hand in comparing that with the actual development of the incidents reported therein. In order to learn lessons from a particular prior experience, analysts should be aware of the latent similarities among the incidents and should experience a breakdown called "expectation-failure" to let that incident be surely printed in their memory. To let the human analysts experience this breakdown, our system introduces a theory of Memory Organization Packets (MOPs) as a framework for explaining the dynamic memory structure of the human. By utilizing this idea as a basis for scenario-based expectation of human analysts and by integrating this idea with a text-mining method, a system for supporting an incident analysis is developed for a domain of medical incidents. Results of the experiments using our proposing system are presented, where the subjects are nurses working for a hospital. Based on those results, effectiveness of the system is discussed from various viewpoints by investigating into the protocols gathered from the subjects of the experiments.

This chapter introduces R&D results for aerial robot systems for urban search and rescue (USAR). Different types of aerial robot system have been developed and effectively combined so as to offer a quick and continuous service for disaster information gathering. First, autonomous helicopters collect disaster situation data from the sky for first decision making in USAR planning. Then, a blimptype robot system and a cable-driven robot system survey victims under collapsed houses by detecting faint signs of life. As a continuous information service, a captive balloon system with a monitoring camera presents bird’s-eye-views of the disaster area, and relays wireless communication among working teams on the ground. These robot systems and other developed technologies are integrated to provide a total solution for quick information gathering from the sky for USAR activity support. The availability of aerial robot systems was demonstrated in field tests conducted at Yamakoshi village.

The DDT Project on rescue robots and related technologies was carried out in Japan’s fiscal years 2002–2006 by nationwide researchers, and was organized by International Rescue System Institute. The objective of this project was to develop practical technologies related to robotics as a countermeasure against earthquake disasters, and include robots, intelligent sensors, information equipment, and human interfaces that support emergency responses such as urban search and rescue, particularly victim search, information gathering, and communication. Typical technologies are teleoperated robots for victim search in hazardous disaster areas, and robotic systems with distributed sensors for gathering disaster information to support human decision making. This chapter introduces the objective of this project, and a brief overview of the research results.

High communicability of the menu-based system is on the basis of consistent vision and clear policy in designing the system of menus, and then they should be perceivable to the users. In this light, failures in menu-based interactions can be explained that they might emerge from lack of information in the users' available cues to identify the design vision. This study focuses on communicative breakdowns in menu-based human-computer interactions from this perspective, and investigates their causes in ill-organized structures of menu hierarchy in terms of the user's interpretation of the menu items. Pirolli's information scentmodel is extended and utilized as an analytical tool for describing the meaning system of menus from the users' point of view, and their decision making in search of particular menu items is analyzed by use of information scent.

This paper discusses usability issues in the design of hierarchical menu systems in terms of the userpsilas interpretation of the interface signs, or the menus. Focusing on the exploration of a particular functional item in the menu hierarchy, novice userspsila interactions with a computerized system are examined from the perspective of communicative breakdowns at. first., and then the menu structure is evaluated through the information scent analysis. The former analysis investigates differences between the designer and the users in the basic assumptions for signifying or interpreting the interface signs, (i.e., menu texts and their organization for listing), especially on the dasiaperipheralpsila functions of the product. The latter analysis investigates the distribution of information scent in a menu list in order to obtain new quantitative clues for the assessment of whether the menu design is compatible with the userspsila naturalistic decision strategy or not.

Direct investigations are required for future missions to small planetary bodies, in addition to obtain terrain maps and collecting samples from the surface. Rovers, mobile robots on surface of a planetary body, are effective in investigating into several specific points. We proposed a method of localization for navigating rovers to specific features. The proposed method measures two-way range between the rover and the mother spacecraft orbiting around the investigating planetary body using radio waves. The proposed method can be applied with reasonable accuracy on large and small planetary bodies and it can particularly be used with sub-hundred-meter-sized bodies, which conventional methods cannot be applied to. Sensitivity analysis of the proposed method is summarized in this paper, according with estimation errors in rotation parameters of the planetary body. A method of estimation is described here, which takes into account errors of rotational parameters.

In designing controllers, robustness against uncertainties is very important property, therefore we proposed design methods of nonlinear robust control system by use of neural networks. Competition between a neural network, which acts as a robust controller and uncertainties of the plant, plays a very important role to be robust, and simple order formation, such as power law scaling, can be found in a set of trained robust controllers. Such simple order is widely found in nature and complex systems, but learning methods without considering uncertainties has never formed such relations. Therefore competitive learning will have important relationship to order formation in various systems.

Network computing is an emerging paradigm that promises to extend computing and information access capabilities transparently beyond the domain of a specific organization and enable unprecedented collaboration and business transactions in a global open network environment. It also challenges the present client-server paradigm, which has proven to be rather expensive, owing to the high cost of maintaining and managing individual workstations and PCs. Network computing is closely coupled with the Internet and World Wide Web explosion. The HTTP-based browser is becoming a standard open universal interface not just for WWW browsing, but for access to corporate databases, for remote system/network management, communication and collaboration, workflow management, and for other services and applications. The Network Computer (NC) is viewed by many as the successor of the familiar PC, that will get most of the needed software and services on demand from the network. To realize the vision of network computing, powerful, robust, and efficient “backend” infrastructure is required to provide such applications and services within an organization, across domains, and on the global scale. What kind of role does the autonomous distributed system (ADS) paradigm play in network computing? Are these two different worlds? Are they complementary? Or, is ADS an inseparable part of or a base for the fledging network computing? The individual panelists address these questions from the point of view of different environments and applications. They discuss technologies that support these paradigms and form a bridge between them.

Stochastic uncertainty are the most typical in flight control system, because wind direction and wind speed, which have significant effect on the flight, vary stochastically. We propose methods to design robust control systems by training a neural network against stochastic uncertainties. Numerical simulations of flight control of an autonomous unmanned helicopter demonstrate the effectiveness of proposed methods.

This paper describes methods to develop autonomous flight control systems for UAVs. The unmanned helicopter "RMAX" produced by Yamaha Motor Co., Ltd. is used in this study. It was difficult to develop flight control systems, because the dynamics of the helicopter is nonlinear. An efficient method to design controllers by training neural networks is proposed in this paper. It is easy to use trained neural network together with online training neural networks or adaptive controllers to compensate undesirable effects which are not modeled or sudden changes of the target and environment, therefore the control system can be highly reliable. Results of flight experiments are shown to demonstrate the effectiveness of our approach.