Telexistence technology enables the manipulation of a remote robot using sensory feedback and leader-follower control, giving the operator the impression of controlling their own body. However, the robot’s operability declines significantly due to increasing communication time delays. In order to address this issue, the "Predictive Display" technique has been proposed and developed. This technique displays both the real robot’s movements with time delay and the computer-generated (CG) robot’s movements without time delay. In the Telexistence robot operation system, a predictive display system can be constructed using a virtual reality (VR) system. In this study, we have developed a VR system for Telexistence operation with THK’s SEED-Noid humanoid robot. Unity is utilized, which enables the predictive display function for the stereo camera mounted on the SEED-Noid. We developed and evaluated a display mode without neck joint angle restrictions to reduce VR sickness and workload using the developed VR system. Additionally, we developed a display mode with a limited neck joint angle for comparison purposes.

Self-body awareness refers to the recognition of one's body as one's own and consists of two senses: “sense of body ownership” and “sense of agency.” In telexistence/telepresence robot operation, time delays in the robot's motion degrade self-body awareness of the robot body. We investigated how self-body recognition can be affected in a telexistence robot operation in a VR space when the robot is presented with a real robot arm that simulates a real robot with a delay and a virtual robot arm, or several virtual robot arms, with a delay less than that of the real robot. These experimental conditions include a ‘Predictive Display,’ which is well known as a time delay countermeasure. The results suggest that virtual robot arms presented consecutively with less delay than a real robot arm do not induce a sense of body ownership to the real robot arm, but they enhance the sense of agency to the real robot arm, and that sense of agency is stronger when the task requires precision.

Software engineering and mission command are two separate but similar fields, as both are instances of complex problem solving in environments with ever changing requirements. Our research hypothesis is that modifications to agile software development based on inspirations from mission command can improve the software engineering process in terms of planning, prioritizing, and communication of software requirements and progress, as well as improving the overall software product. Targeted Scrum is a modification of Traditional Scrum based on three inspirations from Mission Command: End State, Line of Effort, and Targeting. These inspirations have led to the introduction of the Product Design Meeting and modifications of some current Scrum meetings and artifacts. We tested our research hypothesis using a semester-long undergraduate level software engineering class. Students developed two software projects, one using Traditional Scrum and the other using Targeted Scrum. We then assessed how well both methodologies assisted the software development teams in planning and developing the software architecture, prioritizing requirements, and communicating progress. We also evaluated the software product produced by both methodologies. We found that Targeted Scrum did better in assisting the software development teams in the planning and prioritization of the requirements. However, Targeted Scrum had a negligible effect on improving the software development teams external and internal communications. Finally, Targeted Scrum did not have an impact on the product quality by the top performing and worst performing teams. Targeted Scrum did assist the product quality of the teams in the middle of the performance spectrum.

The field of organizational design in multi-agent systems is still in need of powerful tools and methods for enabling effective design, control, and transformation of organizations of different kinds. In this work, we address the problem of reorganization in a distributed problem solving model as a typical multi-agent system application. We look into adaptation of concepts and theories from social organization theory. In particular, we get insights from Schwaninger's model of Intelligent Human Organizations. To this goal, we implement and utilize a multi-level organizational control model which uses a strategic management and an operative management layer to exert organizational control and also to make interactions between these two levels of control possible. Experimental evaluations are performed using a modified model of pursuit game. The results indicate that the proposed model allows the system to stay ahead of the organizational change, resulting in performance improvements, despite the additional costs associated to reorganization.

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

Performing general human behavior by experts' navigation is expected to be realized as wearable and ubiquitous technologies and computing develop. For simple, ordinary behavior, a person does not need the assistance of an expert. However, if one is standing next to an injured/ill person, one needs the instruction on performing first aid treatment from an expert. The wearer of the wearable behavior navigation system will be able to conduct first aid treatment as an expert would. We have developed the wearable behavior navigation systems using Augmented Reality technology, mainly for the navigation of the first aid treatment and for escape from dangerous areas, such as a building on fire. The effectiveness of the wearable navigation systems has been evaluated by a number of experiments. In this paper, the basic mechanism to realize general human behavior navigation is presented, along with the concrete configuration of the prototype of the navigation systems, and the experimental evaluation.

This work focuses on automating the task of estimating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheet's surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time.

The capability to perform specific human tasks with the assistance of expert navigation is expected to be realized through the development wearable and ubiquitous computing technology. For instance, when an injured or ill person requires first-aid treatment, but only non-experts are nearby, instruction from an expert at a remote site is necessary. A behavior navigation system will allow the user to provide first-aid treatment in the same manner as an expert. Focusing on first-aid treatment, we have proposed and developed a prototype wearable behavior navigation system (WBNS) that uses augmented reality (AR) technology. This prototype WBNS has been evaluated in experiments, in which participants wore the prototype and successfully administered various first-aid treatments. Although the effectiveness of the WBNS has been confirmed, many challenges must be addressed to commercialize the system. The head-mounted displays (HMDs) used in the WBNS have a number of drawbacks, for example, high cost (which is not expected to decrease in the near future) and the time required for an ordinary user to become accustomed to the display. Furthermore, some individuals may experience motion sickness wearing the HMD. We expect that these drawbacks to the current technology will be resolved in the future; meanwhile, a near-future remote behavior navigation system (RBNS) is necessary. Accordingly, we have developed RBNSs for first-aid treatment using off-the-shelf components, in addition to the WBNS. In this paper, the basic mechanisms of the RBNS, experiments investigating the demonstration of expert behavior, and a comparative study of the WBNS and the RBNSs are presented.

This paper focuses on automating the task of estimating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheet's surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time. Two primary methods have been studied in this paper, namely, edge-based and active contour. Results are evaluated and presented using the metrics of time requirements and accuracy. Automated ice thickness estimation results from 2006 and 2007 Greenland field campaigns illustrate that the edge-based approach offers faster processing (seconds compared to minutes), but suffers from a lack of continuity and smoothness aspects that active contours provide. The active contour approach is more accurate when compared to ground truth selections provided by human experts, and has proven to be more robust to image artifacts. It is shown that both techniques offer advantages which could be integrated to yield a more effective system.

This paper is focused on the effects of sharing knowledge and collaboration of multiple heterogeneous, intelligent agents (hardware or software) which work together to learn a task. As each agent employs a different machine learning technique, the system consists of multiple knowledge sources and their respective heterogeneous knowledge representations. Collaboration between agents involves sharing knowledge to both speed up team learning, as well as refine the team's overall performance and group behavior. Experiments have been performed that vary the team composition in terms of machine learning algorithms, learning strategies employed by the agents, and sharing frequency for a predator-prey cooperative pursuit task. For lifelong learning, heterogeneous learning teams were more successful than homogeneous learning counterparts. Interestingly, sharing increased the learning rate, but sharing with higher frequency showed diminishing results. Lastly, knowledge conflicts are reduced over time the more sharing takes place. These results support further investigation of the merits of heterogeneous learning. © 2008 Wiley Periodicals, Inc.

As 3-D modeling applications transition from engineering environments into the hands of artists, designers, and the consumer market, there is an increasing demand for more intuitive interfaces. In response, 3-D modeling and interface design communities have begun to develop systems based on traditional artistic techniques, particularly sketching. Collectively this growing field of research has come to be known as sketch-based modeling, however the name belies a diversity of promising techniques and unique approaches. This paper presents a survey of current research in sketch-based modeling, including a basic introduction to the topic, the challenges of sketch-based input, and an examination of a number of popular approaches, including representative examples and a general analysis of the benefits and challenges inherent to each.

Abstract This paper presents a genetic‐algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA‐based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single‐carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade‐offs between the convergence time of the GA and the size of the GA search space. Copyright © 2007 John Wiley & Sons, Ltd.

Artificial neural networks have been traditionally employed to learn and compute the inverse kinematics of a robotic arm. However, the inverse kinematics model of a typical robotic arm with joint limits is a multi-valued and discontinuous function. Because it is difficult for a multilayer neural network to approximate this type of function, an accurate inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the difficulties of inverse kinematics learning, we propose a novel modular neural network system that consists of a number of expert modules, where each expert approximates a continuous part of the inverse kinematics function. The proposed system selects one appropriate expert whose output minimizes the expected position/orientation error of the end-effector of the arm. The system can learn a precise inverse kinematics model of a robotic arm with equal or more degrees of freedom than that of its end-effector. However, there are robotic arms with fewer degrees of freedom, where the system cannot learn their precise inverse kinematics model. We have adopted a modified Gauss-Newton method for finding the least-squares solution to address this issue. Through the modifications presented in this paper, the improved modular neural network system can obtain a precise inverse kinematics model of a general robotic arm.

This paper describes the design, fabrication, and evaluation of a mobile robot as part of a radar system for measuring ice characteristics in Greenland and Antarctica. The robot can survive the polar environments, navigate the terrains, is capable of carrying the necessary radar equipment while providing power, can tow an antenna, and can provide precise positioning for the bistatic synthetic aperture radar (SAR).

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

The ability to create art is a uniquely human endeavor. Throughout history, humans have used paintings, drawings, songs, stories, and other art forms to communicate important ideas or events and to entertain. The advent of computers gave rise to a new medium for art. Computer generated art work has become popular in the entertainment and education industries. The paper uses genetic algorithms (GAs) to automatically evolve a set of unique digital images according to a predetermined set of criteria. By monitoring and automatically evaluating geometric features in images, the work reported in the paper intends to evolve interesting images with a variety of geometric features without the need for human intervention.

The speed, accuracy, and adaptability of human movement depends on the brain performing an inverse kinematics transformation-that is, a transformation from visual to joint angle coordinates-based on learning from experience. In human motion control, it is important to learn a feedback controller for the hand position error in the human inverse kinematics solver. This paper proposes a novel model that uses disturbance noise and the feedback error signal to learn coordinate transformations of the human visual feedback controller. The proposed model redresses drawbacks in current models because it does not rely on complex signal switching, which does not seem neurophysiologically plausible. Numerical simulations show the effectiveness of the model.

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

A novel approach for improving the effectiveness and efficiency of human–computer interactions through an intelligent graphical user interface (GUI) is presented in this paper. It is demonstrated that through monitoring the user's actions, the system can determine the intents of the user, transforming the deduced intentions into system actions. Understanding of the user's implicit actions can reduce the burden on the user in terms of required explicit commands. Hence, a human friendlier computer system is built. One method to assess the effectiveness of the user interface enhancements is by reduction in the number of operations performed by the user in achieving a specific goal while using the GUI. The enhanced graphical user interface described in this paper is a conventional GUI enhanced with an intelligent control module. The intelligent component is composed of a group of software agents that cooperate and compete to control the system. A computer mouse is used as the input device, through which the user performs a number of clicks and drag-and-drop tasks, while selecting and moving objects on the computer screen. The intelligent module monitors the movements and states of the mouse and the cursor, and aids the user based on the multiple agents’ determinations of the user's sub-goals. Experimental results are presented, where human subjects achieved specific goals using the GUI, with and without the intelligent control module. Initial experiments have confirmed that the novel technique is a feasible approach for designing an effective and efficient intelligent GUI, as it reduces the amount of work required from the human user who is interacting with the computer.

The goal of the robotic soccer competition is to develop completely autonomous robotic agents that can cooperate to perform the desired tasks in an extremely dynamic environment. The approach is to train the robots, using various combinations of learning algorithms, i.e., the robots are to initially have no understanding of their environment. The robots are rewarded for positive contributions to the team, and in turn learn better performance through multiple sessions. The majority of learning will take place in simulation and then transferred to real physical mobile robots. The concept of tropism-based control architecture is introduced that not only allows for the evolution of cooperative strategies, but also obtains the acquired knowledge in a format that is easily comprehensible by humans. The results of many generations of simulated evolution are presented, accompanied by the game results and fitness characteristics.

This paper presents the design and implementation of a telepresence system, where a mobile manipulator robot is utilized to explore a museum at a remote site, sending live video and audio on demand to a user at the local site. The user's sense of presence is also enhanced through the capability to manipulate objects remotely via the robot gripper. The display system is a television display system that can be worn like a pair of glasses. The head movements of the user are monitored using color tracking, and the displayed images are updated accordingly. This is achieved by interpreting the human head movements and transforming them into control signals for the robot and its mounted color camera. Novel features of the presented system include: utilization of human implicit commands, availability and cost-effectiveness of the system, feeling of presence, and intelligence fusion of the human user with robot's intelligence. The new version of the telepresence system that is currently under development is also presented.

The interaction of a human and a service robot through the hand-over task is studied in this paper. Control issues are addressed for a service mobile-manipulator delivering and handing over objects to a human. The Contention Architecture controller is designed and implemented allowing for such task to be accomplished in a safe manner, as it requires coexistence and close interaction of the robot and the human. Experimental results of a realistic computer simulation are presented where a 75 kilogram human receives an object from a mobile manipulator with three degrees of freedom. The robot is capable of handing over the object to human, and compensating for unexpected movements of the human through its arm motion and base movement.

Automatic development and learning of robot soccer strategies are presented in this paper. It is shown that using a novel control system, it is possible to allow teams of robots to acquire strategies for playing a better game of soccer through successive generations, utilizing simulated evolution. A number of soccer techniques, as developed through robot games, are discussed. The mechanism presented in the paper is suitable for other tasks requiring multiple robots to interact and cooperate in teams.

The tropism system cognitive architecture provides an intuitive formalism for colonies of agents, either hardware or software. We present a fine-grained implementation of the architecture on a colony of software agents for the interpretation of human tactile gestures for robotic trajectory specification and modification. The fine-grained nature of the architecture and the use of the port-based object framework for agent instantiation allows the manual construction of a capable agent set that is reconfigurable and reusable across different gesture-based interaction tasks.

A novel architecture for agents in colonies has been developed in order to investigate certain forms of group interaction. Specifically, we are interested in the extent to which overall goals for a colony can be achieved when each agent is only aware of limited local goals, whether the architecture allows for emergence of unexpected behaviour, and whether explicit communication among agents facilitates or hinders task performance. The architecture supports several forms of learning. Large colonies of agents (as many as 100) have been studied in simulation experiments, where they carried out fetch-and-carry tasks in the presence of predators and with limited energy reserves. In addition, a physical colony of four agents has been fabricated with the same architecture, to ensure that the behaviours observed in simulation were also present in the hardware implementations.

This paper presents a novel cognitive architecture for simulated autonomous robots in an artificial world. The design principle of the architecture enables the robotic system to both react to the world and perform premeditated actions. The cognitive architecture is a modular structure composed of a number of multilevel interconnected components, each with distributed, module-specific memory. The modular structure allows for simple portability of the architecture across a variety of robotic platforms. The behavior of the robot is based on the automatic creation of composite behavior from a set of predefined atomic behaviors. Facts about the world are discovered as the robot performs its tasks. An autonomous robot embedded within an artificial world (named Machina Cogitans) is equipped with this architecture, and a number of experiments are presented.>