Arvin Agah

KUAR: A Flexible Software-Defined Radio Development Platform

In this paper, we present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. The KUAR hardware implementation and software architecture are discussed in detail. Radio configurations and applications are presented. Future research made possible by this flexible platform is also discussed.

Evolving control for distributed micro air vehicles

We focus on the task of large area surveillance. Given an area to be surveilled and a team of micro air vehicles (MAVs) with appropriate sensors, the task is to dynamically distribute the MAVs appropriately in the surveillance area for maximum coverage based on features present on the ground, and to adjust this distribution over time as changes in the team or on the ground occur. We have developed a system that learn rule sets for controlling the individual MAVs in a distributed surveillance team. Since each rule set governs an individual MAV, control of the overall behavior of the entire team is distributed; there is no single entity controlling the actions of the entire team. Currently, all members of the MAV team utilize the same rule set; specialization of individual MAVs through the evolution of unique rule sets is a logical extension to this work. A genetic algorithm is used to learn the MAV rule sets.

Psychological Effects of Behavior Patterns of a Mobile Personal Robot

It is envisioned that in the near future personal mobile robots will be assisting people in their daily lives. An essential characteristic shaping the design of personal robots is the fact that they must be accepted by human users. This paper explores the interactions between humans and mobile personal robots, by focusing on the psychological effects of robot behavior patterns during task performance. These behaviors include the personal robot approaching a person, avoiding a person while passing, and performing non-interactive tasks in an environment populated with humans. The level of comfort the robot causes human subjects is analyzed according to the effects of robot speed, robot distance, and robot body design, as these parameters are varied in order to present a variety of behaviors to human subjects. The information gained from surveys taken by 40 human subjects can be used to obtain a better understanding of what characteristics make up personal robot behaviors that are most acceptable to the human users.

Human interactions with intelligent systems: research taxonomy

Abstract Human beings are constantly interacting with systems surrounding them. These include personal computers, automobiles, and smart appliances, among many others. It is expected that in the near future there will be interactions with service robots in homes and offices. A great deal of efforts have been made into advancing this interaction using science and technology. The main goal is to make human interactions with systems not only effective and safe, but also enjoyable and entertaining. The objective of this paper is to provide a survey of the current research on such interactions, based on a taxonomy framework. The work presented in this paper embodies human–computer interactions, human–machine interactions, and human–robot interactions. The research undertakings are classified based on five criteria of: application, research approach, system autonomy, interaction distance, and interaction media. Multiple categories are provided in each classification and a large number of research efforts are presented in each category. Additionally, more than 150 cited references are listed in summary tables with respect to each classification category. This provides an easy reference guide for related works with certain characteristics. The advanced interactions discussed in this paper cover a wide range, including: tele-robots, virtual reality, user interfaces, bio-feedback, mobile robots, intelligent user interfaces, sport machines, medical technology, human–machine interfaces, entertainment, intelligent systems, computer input/output devices, human–computer interfaces, and multimedia.

Inverse kinematics learning by modular architecture neural networks with performance prediction networks

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.

Machine tool positioning error compensation using artificial neural networks

This paper is a study of the application of artificial neural networks to the problem of calculating error compensation values for axis positioning on a machine tool. The primary focus is on the development of a neural network-based system that could be implemented and integrated into the open architecture control system of an actual machine. A number of neural network architectures were examined for applicability to the problem and one was selected and implemented on the actual machine. Positioning error compensation capabilities were evaluated using industry standard equipment and procedures, and the results obtained were compared with the capabilities of standard error compensation routines in machine tool controls.

A survey of sketch-based 3-D modeling techniques

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.

Phylogenetic and ontogenetic learning in a colony of interacting robots

The objective of this paper is to describe the development of a specific theory of interactions and learning among multiple robots performing certain tasks. One of the primary objectives of the research was to study the feasibility of a robot colony in achieving global objectives, when each individual robot is provided only with local goals and local information. In order to achieve this objective the paper introduces a novel cognitive architecture for the individual behavior of robots in a colony. Experimental investigation of the properties of the colony demonstrates its ability to achieve global goals, such as the gathering of objects, and to improve its performance as a result of learning, without explicit instructions for cooperation. Since this architecture is based on representation of the “likes” and “dislikes” of the robots, it is called the Tropism System Cognitive Architecture. This paper addresses learning in the framework of the cognitive architecture, specifically, phylogenetic and ontogenetic learning by the robots. The results show that learning is indeed possible with the Tropism Architecture, that the ability of a simulated robot colony to perform a gathering task improves with practice and that it can further improve with evolution over successive generations. Experimental results also show that the variability of the results decreases over successive generations.

Inverse kinematics learning for robotic arms with fewer degrees of freedom by modular neural network systems

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.

Human interaction with a service robot: mobile-manipulator handing over an object to a human

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.