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This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations.

In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction.

Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.

The purpose of this paper is to propose an agile assistant robot to be used as a mobile partner with two rotational motions and one translational motion. This robot possesses the rolling function and three operating abilities to assistant human beings in the industrial environment.

The main body of the robot is a typical 4-RSR (where R denotes a revolute joint and S denotes a spherical joint) parallel mechanism. The mechanism can reach any position on the ground by two rolling modes (the equivalent Watt linkage rolling mode and the equivalent 6R linkage rolling mode), and the robot can work as a spotlight or a worktable in operating modes at the target location. The mobility, rolling modes, operating modes and kinematics are analyzed.

Based on the results of kinematics of this assistant robot, the upper platform of the 4-RSR rolling mechanism has two rotational motions and one translational motion which can be used in the industry. The proposed concept is verified by experiments on a physical prototype.

This paper also discusses the application to industrial cases where cooperation between workers and robots is required.

The work presented in this paper is a novel exploration to apply parallel mechanisms to the field of assistant rolling robots. It is also a new attempt to use the rolling mechanism in the field of mobile operating robots for industry tasks.

The purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).

Based on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.

The assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.

The work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

The purpose of this paper is to present a navigation system designed for highly dynamic environments which is independent from a metrically exact global map.

A navigation system is developed to cope with highly dynamic environments. Here, this refers especially to changes in the environment itself, like the daily deployment or removal of advertisements or special offers in a supermarket. The navigation system is split into a global part, relying on non‐concealable artificial landmarks and a local part containing a behavior‐based control using a dynamic potential field approach. The required information are the definitively static structures and the actual sensor information only.

The system proved to be useful in environments that change frequently and where the presence of many people complicates the perception of landmarks.

The presented navigation system is robust against changes in the environment and provides reliable collision avoidance capabilities.

It is a useful navigation system for autonomous robots dedicated to frequently changing and populated environments.

Most industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. This is a tedious and time‐consuming task that requires some technical expertise, and hence new approaches to robot programming are required. The purpose of this paper is to present a robotic system that allows users to instruct and program a robot with a high‐level of abstraction from the robot language.

The paper presents in detail a robotic system that allows users, especially non‐expert programmers, to instruct and program a robot just showing it what it should do, in an intuitive way. This is done using the two most natural human interfaces (gestures and speech), a force control system and several code generation techniques. Special attention will be given to the recognition of gestures, where the data extracted from a motion sensor (three‐axis accelerometer) embedded in the Wii remote controller was used to capture human hand behaviours. Gestures (dynamic hand positions) as well as manual postures (static hand positions) are recognized using a statistical approach and artificial neural networks.

It is shown that the robotic system presented is suitable to enable users without programming expertise to rapidly create robot programs. The experimental tests showed that the developed system can be customized for different users and robotic platforms.

The proposed system is tested on two different robotic platforms. Since the options adopted are mainly based on standards, it can be implemented with other robot controllers without significant changes. Future work will focus on improving the recognition rate of gestures and continuous gesture recognition.

The key contribution of this paper is that it offers a practical method to program robots by means of gestures and speech, improving work efficiency and saving time.

This paper presents an alternative to the typical robot teaching process, extending the concept of human‐robot interaction and co‐worker scenario. Since most companies do not have engineering resources to make changes or add new functionalities to their robotic manufacturing systems, this system constitutes a major advantage for small‐ to medium‐sized enterprises.

The purpose of this paper is to report a collection of developments that enable users to program industrial robots using speech, several device interfaces, force control and code generation techniques.

The reported system is explained in detail and a few practical examples are given that demonstrate its usefulness for small to medium‐sized enterprises (SMEs), where robots and humans need to cooperate to achieve a common goal (coworker scenario). The paper also explores the user interface software adapted for use by non‐experts.

The programming‐by‐demonstration (PbD) system presented proved to be very efficient with the task of programming entirely new features to an industrial robotic system. The system uses a speech interface for user command, and a force‐controlled guiding system for teaching the robot the details about the task being programmed. With only a small set of implemented robot instructions it was fairly easy to teach the robot system a new task, generate the robot code and execute it immediately.

Although a particular robot controller was used, the system is in many aspects general, since the options adopted are mainly based on standards. It can obviously be implemented with other robot controllers without significant changes. In fact, most of the features were ported to run with Motoman robots with success.

It is important to stress that the robot program built in this section was obtained without writing a single line of code, but instead just by moving the robot to the desired positions and adding the required robot instructions using speech. Even the upload task of the obtained module to the robot controller is commanded by speech, along with its execution/termination. Consequently, teaching the robotic system a new feature is accessible for any type of user with only minor training.

This type of PbD systems will constitute a major advantage for SMEs, since most of those companies do not have the necessary engineering resources to make changes or add new functionalities to their robotic manufacturing systems. Even at the system integrator level these systems are very useful for avoiding the need for specific knowledge about all the controllers with which they work: complexity is hidden beyond the speech interfaces and portable interface devices, with specific and user‐friendly APIs making the connection between the programmer and the system.

In this study, human activity with finite and specific ranking is modeled with finite state machine, and an application for human–robot interaction was realized. A robot arm was designed that makes specific movements. The purpose of this paper is to create a language associated to a complex task, which was then used to teach individuals by the robot that knows the language.

Although the complex task is known by the robot, it is not known by the human. When the application is started, the robot continuously checks the specific task performed by the human. To carry out the control, the human hand is tracked. For this, the image processing techniques and the particle filter (PF) based on the Bayesian tracking method are used. To determine the complex task performed by the human, the task is divided into a series of sub-tasks. To identify the sequence of the sub-tasks, a push-down automata that uses a context-free grammar language structure is developed. Depending on the correctness of the sequence of the sub-tasks performed by humans, the robot produces different outputs.

This application was carried out for 15 individuals. In total, 11 out of the 15 individuals completed the complex task correctly by following the different outputs.

This type of study is suitable for applications to improve human intelligence and to enable people to learn quickly. Also, the risky tasks of a person working in a production or assembly line can be controlled with such applications by the robots.

For the past ten years, the Intelligent Robotics Laboratory (IRL) at Vanderbilt University has been developing service robots that interact naturally, closely and safely with human beings. Two main issues for research have arisen from this prior work. The first is how to achieve a high level of interaction between the human and robot. The result has been the philosophy of human directed local autonomy (HuDL), a guiding principle for research, design, and implementation of service robots. The human‐robot relationship we seek to achieve is symbiotic in the sense that both the human and the robot work together to achieve goals, for example as aids to the elderly or disabled. The second issue is the general problem of system integration, with a specific focus on integrating humans into the service robotic system. This issue has led to the development of the Intelligent Machine Architecture (IMA), a novel software architecture specifically designed to simplify the integration of the many diverse algorithms, sensors, and actuators necessary for intelligent interactive service robots.