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The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments.

The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions; minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered.

The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions.

The motion generated yield good results in both simulation and experimental environments.

The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion.

A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.

Development of autonomous robot manipulator for human-robot assembly tasks is a key component to reach high effectiveness. In such tasks, the robot real-time object recognition is crucial. In addition, the need for simple and safe teaching techniques need to be considered, because: small size robot manipulators’ presence in everyday life environments is increasing requiring non-expert operators to teach the robot; and in small size applications, the operator has to teach several different motions in a short time.

For object recognition, the authors propose a deep belief neural network (DBNN)-based approach. The captured camera image is used as the input of the DBNN. The DBNN extracts the object features in the intermediate layers. In addition, the authors developed three teaching systems which utilize iPhone; haptic; and Kinect devices.

The object recognition by DBNN is robust for real-time applications. The robot picks up the object required by the user and places it in the target location. Three developed teaching systems are easy to use by non-experienced subjects, and they show different performance in terms of time to complete the task and accuracy.

The proposed method can ease the use of robot manipulators helping non-experienced users completing different assembly tasks.

This work applies DBNN for object recognition and three intuitive systems for teaching robot manipulators.

The purpose of this paper is to develop a robotic tooth brushing simulator mimicking realistic tooth brushing motions, thereby facilitating greater understanding of the generation of realistic tooth brushing motion for optimal design of toothbrushes.

Tooth brushing motions were measured via a motion capture system. Different motion patterns of brushing were analysed. A series of elliptical motion segments were generated by interpolating ellipse‐like trajectories. Furthermore, a path generation algorithm for brushing simulation was proposed. A path planning system incorporating robot motion control was developed to simulate realistic tooth brushing. The generality and efficiency of the proposed algorithm was demonstrated through simulation and experimental results.

The interpolation of ellipse‐like trajectories can generate elliptical motion segments. Furthermore, realistic tooth brushing can be achieved by integrating the elliptical motion segments into the path generated from the surfaces of teeth. The brushing simulator demonstrated good reproducibility of clinically standardized tooth brushing.

A robotic toothbrush assessment system is a potential application to the robotic tooth brushing simulator by incorporating control of brushing variables, including brushing pressure, speed and temperature.

This study demonstrates the feasibility of using robotic simulation techniques towards improved realistic human tooth brushing motions simulation for optimal design of tooth brushes.

The purpose of this paper is to deal with a method for gesture encoding and reproduction, particularly aiming at a text‐to‐gesture (TTG) system that enables robotic agents to generate proper gestures automatically and naturally in human‐robot interaction.

Reproducing proper gestures, naturally synchronized with speech, is important under the TTG concept. The authors first introduce a gesture model that is effective to abstract and describe a variety of human gestures. Based on the model, a gesture encoding/decoding scheme is proposed to encode observed gestures symbolically and parametrically and to reproduce robot gestures from the codes. In particular, this paper mainly addresses a gesture scheduling method that deals with the alignment and refinement of gestural motions, in order to reproduce robotic gesticulation in a human‐like, natural fashion.

The proposed method has been evaluated through a series of questionnaire surveys, and it was found that reproduced gestures by a robotic agent could appeal satisfactorily to human beings.

This paper provides a series of algorithms to treat overlapped motions and to refine the timing parameters for the motions, so that robotic agents reproduce human‐like, natural gestures.

Current virtual simulation platforms provide various tools to generate non-immersive simulation processes purposefully in different domains. The generated simulation processes are adopted for analysis, presentation, demonstration and verification. In the virtual maintenance domain, this intuitive and visual method has benefitted product maintainability design and improvement. Generating an ideal and reasonable non-immersive virtual maintenance simulation is always time-consuming because of the complicated human operations and logical relationships involved. This study aims to propose a semiautomatic approach to increase efficiency in non-immersive virtual maintenance simulation implementation.

The methodology analyzes the general catalogs of common maintenance tasks and explores the corresponding secondary development approaches of simulation tools that can achieve motion simulation in virtual environments, by focusing on the diversity, complexity and uncertainty in non-immersive virtual simulation process generation. Afterward, a single virtual human motion can be generated by controlling the parameters and indices of the simulation tools. Subsequently, all of the generated single motions are connected logically to simulate the entire maintenance process.

Instead of selecting various tools, such as that in a traditional method, the proposed methodology analyzes and integrates the necessary basic parameters considering the characteristics of virtual maintenance simulation for a target maintenance activity.

The user can control the predefined parameters to generate the simulation combining several other simple operations in virtual environments. Consequently, the methodology decreases simulation tool selection and logic consideration and increases efficiency to a certain extent in non-immersive virtual maintenance simulation generation.

Aims to realize the high accurate contour control with high‐speed motion of articulated robot manipulator (ARM) with interference.

Proposes a new contour control method by using Gaussian neural network (GNN) to solve the problem of the deterioration of the contour control performance due to the interference between robot links. The construction of the GNN controller and the approximation of the interference are based on the Euler‐Lagrange model of ARM. The actual input/out data about the motion of ARM are used for training the GNN to accurately represent the inverse dynamics of ARM with interference. With the Lyapunov function, the stability and the robustness of the GNN controller are discussed. Through the simulation and experiment, it verified that the precision of the contour control has been improved, and illustrated the good features of the proposed method.

Finds that the actual data about the motion of ARM, which is easily obtained from the working field, can express the real features of ARM, and the GNN controller can improve the precision of the contour control with good features.

The proposed method provides an effective method for realizing high accurate contour control of ARM with interference. It can be extended to the ARMs with more than two links and concerning more factors affecting the precision of the contour control, such as friction or gravity.

Proposes a new GNN controller for realizing high accurate contour control of ARM with interference, which is significant for industry.

Autonomous mobile manipulation depends on a lot of effort at various levels. In general, the hardware design is as important as algorithm (or software) design. In particular, the absence of certain capabilities of hardware can seriously affect the feasibility and performance of algorithms. The purpose of this paper is to present work on developing hardware capability for mobile manipulation by low‐cost humanoids (LOCH) humanoid robot.

This paper presents research work on developing the hardware support which enables vision‐guided mobile manipulation realized on top of a biped humanoid robot called LOCH. One important goal which guides the development is to achieve the hardware capability with human‐like dexterity, modularity, functionality, and appearance.

This paper discusses the detail of solutions leading to the realization of the intended hardware capability, focusing in particular on the issues related to mechanism, actuation, distributed sensing, and distributed control of humanoid head, humanoid hands and humanoid arms. Finally, the paper shows the result of the actual prototype, which can be controlled by a remote control station through wireless connection.

In designing a machine, it is common to do motor‐sizing and material selection. Since these are standard procedures, these details are omitted because readers with the training in mechanical engineering should be able to work out such details in order to select the appropriate motors and materials. Also, this paper does not delve into the description of the biped system of LOCH humanoid, because such work requires another long paper in order to reveal major details.

This paper presents the major detail of research efforts toward developing hardware capabilities for achieving autonomous mobile manipulation by LOCH humanoid robot, focusing on three important modules, namely: perception head, human‐like hands, and arms. The uniqueness of this work is twofold. First, LOCH humanoid robot's perception head has the most versatile sensing capabilities, which are fully integrated into a compact and human‐like head. Second, each of LOCH humanoid robot's hands has 14 degrees of freedom, which are realized within a mechanism which is of human‐hand size and shape. In addition, the perception head, humanoid hands and humanoid arms are seamlessly integrated together owing to the adoption of a distributed system which supports networked sensing and control through the use of both control area network bus and transmission control protocol/internet protocol internet.

This paper aims to present a new learning from demonstration-based trajectory planner that generalizes and extracts relevant features of the desired motion for an industrial robot.

The proposed trajectory planner is based on the concept of human arm motion imitation by the robot end-effector. The teleoperation-based real-time control architecture is used for direct and effective imitation learning. Using this architecture, a self-sufficient trajectory planner is designed which has inbuilt mapping strategy and direct learning ability. The proposed approach is also compared with the conventional robot programming approach.

The developed planner was implemented on the 5 degrees-of-freedom industrial robot SCORBOT ER-4u for an object manipulation task. The experimental results revealed that despite morphological differences, the robot imitated the demonstrated trajectory with more than 90 per cent geometric similarity and 60 per cent of the demonstrations were successfully learned by the robot with good positioning accuracy. The proposed planner shows an upper hand over the existing approach in robustness and operational ease.

The approach assumes that the human demonstrator has the requisite expertise of the task demonstration and robot teleoperation. Moreover, the kinematic capabilities and the workspace conditions of the robot are known a priori.

The real-time implementation of the proposed methodology is possible and can be successfully used for industrial automation with very little knowledge of robot programming. The proposed approach reduces the complexities involved in robot programming by direct learning of the task from the demonstration given by the teacher.

This paper discusses a new framework blended with teleoperation and kinematic considerations of the Cartesian space, as well joint space of human and industrial robot and optimization for the robot programming by demonstration.

This paper aims to propose an elementary approach towards the measurement of a globoidal cam profile used in an automatic tool changer (ATC) of computer numerical control (CNC) machines.

A simple and unique online method has been designed for the profile metrology of the cam. This simple methodology will replace the traditional methodology of profile metrology of cam by coordinate measuring machine (CMM). A globoidal cam with an indexable turret and roller follower (rotating in an enclosed track) has been evaluated in our analysis. This analysis plays a significant role in the performance determination of the cam as well as the ATC of CNC machines.

A novel model has been designed and implemented to investigate the profile of a globoidal cam. The proposed methodology becomes an enhancement over the old methodology, i.e. measurement through CMM. Theoretical analysis and practical implementation prove the significance of the method.

An enhanced methodology to effectively measure the globoidal cam profile has been proposed. The practical implication of the proposed methodology remains for the CNC machine tool and ATC manufacturers. Finally, analytical explorations have been carried out to prove the validity of the proposal.

One of the development trends of robots is to enable robots to have the ability of anthropomorphic manipulation. Grasping is the first step of manipulation. For mobile manipulator robots, grasping a target during the movement process is extremely challenging, which requires the robots to make rapid motion planning for arms under uncertain dynamic disturbances. However, there are many situations require robots to grasp a target quickly while they move, such as emergency rescue. The purpose of this paper is to propose a method for target dynamic grasping during the movement of a robot.

An off-line learning from demonstrations method is applied to learn a basic reach model for arm and a motion model for fingers. An on-line dynamic adjustment method of arm speed for active and passive grasping mode is designed.

The experimental results of the robot movement on flat, slope and speed bumps ground show that the proposed method can effectively solve the problem of fast planning under uncertain disturbances caused by robot movement. The method performs well in the task of target dynamic grasping during the robot movement.

The main contribution of this paper is to propose a method to solve the problem of rapid motion planning of the robot arm under uncertain disturbances while the robot is grasping a target in the process of robot movement. The proposed method significantly improves the grasping efficiency of the robot in emergency situations. Experimental results show that the proposed method can effectively solve the problem.