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The design of robot hand for writing and correction assistant applications work will be partially replaced as to develop the remote-assisted robot hand control needed to manipulate all this kind of work. As it is stress and strain full job for all teachers, which needs to bring a sustainable solution, hence robot hand which resembles the human hand which is teleoperated with the remote control is designed.

This work presents the design and simulation of a robot hand for correction assistant applications. To replace the work partially especially for the teachers who are undergoing the paper correction work like putting tick mark, cross mark, allocation of marks, etc.

In this paper the design of the same and its simulation of writing the horizontal line, vertical line and writing number two is presented, in further research, a prototype model and its analysis will be done.

The design is been done using the modelling software Creo 5.4 where the design will be used to print the prototype model which is physical using 3D printing technology and controlling and testing will be done on the same prototype model using simple Arduino.

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.

The purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control.

This paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots.

This interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency.

A novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.

Presents both description and overview of the emerging field of biomechanical grippers and shows the prototype of biomechanical gripper called the Presov biomechanical robot gripper. Biomechanical robots and biomechanical grippers belong under biorobotics and bioengineering systems. Basic components of biorobotics include biomechanisms, biocontrol, biointelligence and biosensors. The Presov Biomechanical Robot Gripper is an electrically‐driven, multi‐fingered dextrous gripper, which has many features that conventional industrial robot grippers do not have. This gripper has been developed in the Department of Industrial Robotics of the Technical University in Presov, Slovak Republic.

The purpose of this paper is to describe the integration of a gesture control system for industrial collaborative robot. Human and robot collaborative systems can be a viable manufacturing solution, but efficient control and communication are required for operations to be carried out effectively and safely.

The integrated system consists of facial recognition, static pose recognition and dynamic hand motion tracking. Each sub-system has been tested in isolation before integration and demonstration of a sample task.

It is demonstrated that the combination of multiple gesture control methods can increase its potential applications for industrial robots.

The novelty of the system is the combination of a dual gesture controls method which allows operators to command an industrial robot by posing hand gestures as well as control the robot motion by moving one of their hands in front of the sensor. A facial verification system is integrated to improve the robustness, reliability and security of the control system which also allows assignment of permission levels to different users.

Many people suffer from injuries related to their hand. This research aims to focus on the improvement of the previously developed smart glove by using position and force control algorithms. The new smart glove may be used for both physiotherapy and assistance.

The proposed robot uses shape memory alloy (SMA) actuators coupled to an under-actuated tendon-driven mechanism. The proposed device, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high forces to grasp objects in various hand configurations. The device’s performance is studied in physiotherapy and object manipulation tasks. In the physiotherapy mode, hand motion frequency is controlled, whereas the grasping force is controlled in the object manipulation mode. To simulate the proposed system behavior, the kinematic and dynamic equations of the proposed system have been derived.

The achieved results verify that the system is suitable to be used as part of a rehabilitation device in which it can flex and extend fingers with accurate trajectories and grasp objects efficiently. Specifically, it will be shown that using six SMA wires with the diameter of 0.25 mm, the proposed robot can provide 45 N gripping force for the patients.

The proposed robot uses SMA actuators and an under-actuated tendon-driven mechanism. The resulted robotic system, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high force levels to grasp objects in various hand configurations. It is shown that the motion and exerted force of the robot may be controlled effectively in practice.

The aim of this paper is to present a novel methodology which incorporates Camshift, Kalman filter (KFs) and adaptive multi-space transformation (AMT) for a human-robot interface, which perfects human intelligence and teleoperation.

In the proposed method, an inertial measurement unit is used to measure the orientation of the human hand, and a Camshift algorithm is used to track the human hand using a three-dimensional camera. Although the location and the orientation of the human can be obtained from the two sensors, the measurement error increases over time due to the noise of the devices and the tracking errors. KFs are used to estimate the location and the orientation of the human hand. Moreover, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. An AMT method is proposed to assist the operator to improve accuracy and reliability in determining the pose of the robot.

The experimental results show that this method would not hinder most natural human-limb motion and allows the operator to concentrate on his/her own task. Compared with the non-contacting marker-less method (Kofman et al., 2007), this method proves more accurate and stable.

The human-robot interface system was experimentally verified in a laboratory environment, and the results indicate that such a system can complete high-precision manipulation efficiently.

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.

Almost everyone is familiar with the word “robot” in our civilized society. A man‐made man first appeared as Talos, a bronze giant of Creta in the remote past of greek mythology. Since ancient times, the robot has been an object of human imagination. Unexpectedly, the word “robot” was used for the first time relatively recently. It was a drama title “Rossum's Universal Robots (RUR)” which Karel Capek, a Czechoslovakian dramatist produced in 1920. “Robot” was a new word which was coined from “robota” which means to work or a serf in Czechoslovakia. Hence, the robot is a man‐made man who works for and serves a human being. In the meantime, the pedigree of automata which started with Heron in about 100 B.C. uninterruptedly continues until today. An outstanding example of all those in existence is the three automata which still please people in a historical museum by the lake of Neuchatel, Switzerland. The automata, which were made in the middle of the 18th century, play the organ, write sentences and draw pictures as if they were alive. (Photo 1).

The purpose of this paper is to present a fully automatic calibration method for hand-eye serial robot system is presented in this paper. The so-called “fully automatic” is meant to calibrate the robot body, the hand-eye relation, and the used measuring binocular system at the same time.

The calibration is done by controlling the joints to rotate several times one by one in the reverse order (i.e. from the last one to the first one), and simultaneously take pictures of the checkerboard patterns by the stereo camera system attached on the end-effector, then the whole robot system can be calibrated automatically from these captured images. In addition, a nonlinear optimization step is used to further refine the calibration results.

The proposed method is essentially based on an improved screw axis identification method, and it needs only a mirror and some paper checkerboard patterns without resorting to any additional costly measuring instrument.

Simulations and real experiments on MOTOMAN-UP6 robot system demonstrate the feasibility and effectiveness of the proposed method.