This paper presents a novel impedance control strategy to improve the performance of a robot manipulator. Impedance control and admittance control have complementary effects on the stability and performance of a control system. Impedance control works well in stiff environments, whereas admittance control works well in soft environments. In this paper, we propose a hybrid impedance and admittance control strategy that switches the controller based on the switching condition. If proper switching between impedance control and admittance control is achieved, the controller will have the advantages of both the control strategies. The proposed schemes were evaluated through simulations using a 2-DOF manipulator. Experiments were conducted using an actual robot. The results of the simulation and experiments performed confirmed that the proposed control strategy improves the performance of the robot manipulator.

This paper presents an approach to optimize the control torque of heavy-duty redundant manipulators used for dismantling nuclear power plants. Such manipulators must endure intensive and repetitive tasks over long periods. In this regard, the torque minimization is essential for decreasing power consumption and the fatigue load acting on the joint bearings. This in turn can increase the lifespan of the manipulators and lead to saving on maintenance costs. Because of the design specifications of the manipulators, gravity entirely dominates the Coriolis and centrifugal torques. Hence, it is challenging to reduce the driving torque through the application of a conventional optimization method, known as the minimum kinetic energy method, where the configuration of the manipulator changes extremely slowly from the beginning to the end of the trajectory. In this study, we propose a new torque minimization method based on the advantage of the redundancy of the manipulator. In particular, the norm of the static torque caused by the manipulator gravity itself tends to decrease owing to the application of the gradient projection method for the redundancy resolution at the acceleration level. Simultaneously, the dynamic torque is minimized to lessen the local acceleration triggered by the change mentioned above. The generalized effectiveness of this proposed method is evaluated through simulations and experiments with two different trajectories and speeds. The results show that the proposed method is more effective in reducing the overall driving torque and dissipated energy compared with the conventional technique, especially in the case of the 7-DOF heavy-duty redundant manipulator, and would be applicable for the revolute robot type.

This article presents a sensor, called a skin-type dual proximity sensor (STPS) that can provide distance information in the form of impedance between a sensor and an approaching object before the collision. Meanwhile, a new method regarding the selection of the resonant frequency range for optimizing the measured distance is validated. The sensor adopts the combined sensing principle of inductive-capacitive proximity sensing. STPS can be fabricated with various dimensions and shapes, and easily attached to robot surfaces. Represented by the series of sensors studied with the dimensions of 100 × 100 × 2.75 mm, the proposed sensor can detect an approaching human body up to 300 mm away. In this article, sensor system modeling and simulation are conducted, followed by fabrication and signal processing methods. Finally, the performances of the sensor are experimentally validated, thoroughly analyzed concerning various parameters, and demonstrated with the collaboration between human and robots.

Force/Torque(F/T) sensing technology enables a dexterous robot control such as direct teaching, master-slave system, and pick-and-place task. In general, 6-axis F/T sensor is attached to the end-effector of the robot manipulator to assist in utilizing advanced robot systems. However, in actual applications, various tools such as robotic grippers, robotic hand, grinders are attached to the sensor and it causes F/T offsets with respect to the gravity. In this letter, Autonomous Weight Compensating(AWC) technique for 6-axis F/T sensor is presented. The proposed AWC technique can reduce the F/T offsets by estimating the F/T offsets through installed Inertial Measurement Unit(IMU) sensor. In this study, the 6-axis F/T are measured based on capacitance sensing scheme and to estimate the orientation of the sensor, a 9-axis IMU sensor is installed inside of the sensor. Then, the F/T offsets are calibrated via Artificial Neural Network(ANN) model. Finally, the performance of the proposed method is demonstrated through comparing the F/T data with both trained data and untrained data.

This paper presents a new mechanical design of an anthropomorphic robot hand. The hand is designed such that it is adaptive, backdrivable, modularized to provide both dexterity and robustness. The hand has 18 joints, 14 degrees of freedom, and a new joint mechanism called an active DIP–PIP joint for the robot finger. The mechanism includes a pair of movable pulleys and springs for generating both linked and adjustable motions. Although the set of DIP (distal interphalangeal) and PIP (proximal interphalangeal) joints exhibits a coupled movement in free space, it moves adaptively when it contacts an object. To ensure a 1:1 ratio movement when a finger is moving freely and to produce additional joint torque when gripping an object, torsion springs are attached to each joint. The backdrivability of each joint is realized using an actuation module with a miniature BLDC motor and a ball screw. In addition, the relatively unknown intermetacarpal joints, which provide additional dexterity to the hand for grasping small objects, are used in the robot hand model. A modularized design simplifies the assembly of the hand and increases the economic feasibility. Experimental results are included in this paper for validating the design of the robot hand.

A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.

This paper presents a variable admittance control method to achieve intuitive human-robot interactions that consider human intentions. Human intention is classified into two categories-direct and indirect. With respect to direct intention, the concept of standard force is introduced to adjust the interacting force. The proposed variable admittance control method improves intuitiveness when velocity is used as an estimate of direct intention. In the estimation of indirect intention, a force guidance method is suggested to make a robot follow and guide a human. The proposed control methodology is adapted to a six-DOF manipulator based on a one-dimensional analysis. The experiments are conducted with a manipulator (Universal Robots, UR10) and a force/torque sensor (Robotus, RFT60-HA) to evaluate the performance. The experiments validate that variable admittance control enhances the execution time, accuracy, and comfort of the operator.

The human hand provides excellent grasp capabilities by using the interaction of the thumb and other fingers, and thus, how to dextrously interact with each other can represent the performance of the robot hand. This paper presents the kinematic design optimization for an anthropomorphic robot hand based on the interactivity of fingers. We propose a new performance index, called ‘interactivity of fingers (IF),’ which is useful for quantifying the multifingered precision grasping capability of the robot hand. Using the IF, the kinematic model of an anthropomorphic robot hand is optimized. The optimization is performed by using the genetic algorithm, and position and orientation of the thumb’s saddle joint are systematically determined. In order to verify the usefulness of the proposed performance index, IF of existing hands and that of the optimized model are compared. As a result, the IF of the optimized robot hand is almost five times larger than those of the other hands and the effectiveness of the proposed method is discussed.

The multi-axial force/torque sensor for advanced robotic applications utilizes the internal strain of deformable structure for its measurement. Thus, there exist coupling and errors due to nonlinearity caused from the variation of the electric signal with respect to the strain. This paper presents a method of calibrating six-axis force/torque sensors with high accuracy based on deep learning. The method can solve the aforementioned problems easily by using deep-neural network (DNN). After performing the structural analysis of the sensor, generalized equations of the electric signal is derived, which leads it to the basic DNN structure, and optimization is performed. Training and test data are prepared by using a dummy and a reference sensor. Then, the proposed method is validated by comparing the three results obtained from the linear transformation method based on least-square method, two-step neural-network, and the DNN-based method for each untrained test data set.

The force/torque sensor is a important tool that gives a robot an ability to interact with environments. Calibration is essential for these force/torque sensors to convert the raw sensor values to accurate forces and torques. However, in practice, the multi-axis force/torque sensor requires complex multi-step data processing, because of the coupling effects and nonlinearity of sensors. Moreover, accuracy is not guaranteed. To solve this problem, we propose an accurate force/torque sensor calibration method that can calibrate the sensor in single step by using deep-learning algorithm, and introduce the method for modeling the DNN(deep neural network) used in this calibration process. In addition, we verify the calibration results through several experiments.

This paper presents a new performance index, called “Interactivity of Fingers (IF)”, for anthropomorphic robot hand and kinematic design optimization process using this performance index and genetic algorithm. This performance index can quantify robot hands' kinematic structures and used for optimizing the kinematic model of an robot hand collaborating with Genetic algorithm. With this procedure position and orientation of the thumb's saddle joint are systematically determined. To evaluate proposed index, optimized hand model's and existing hands' IFs are calculated, compared and discussed.

This paper presents the improved branched tendon mechanism by including additional design parameters to the original branched tendon design, and a kinematic design optimization technique for this mechanism using genetic algorithm. The significance of additional design parameters and the feature of improved branched tendon mechanism are also explained. Unlike traditional joint pulley-tendon mechanism that always has the same length of moment arm, the improved branched tendon mechanism uses special tendon which is divided into two just before it is attached to the remote link. By optimizing these divided two different lengthes of tendons and other design parameters, creating various moment arm on a single joint with respect to the flexion angle of joint and limiting maximum moment arm to ensure all the tendons to be inside of the finger's outer frame are possible at the same time. Total 12 variables, 4 given and 8 independent, are used to represent the mechanism mathematically and the objective function is defined to maximize the moment arm throughout the flexion of the joint while not exceeding joint radius. Optimizing the kinematic model of improved branched tendon mechanism with genetic algorithm, it is possible to minimize the loss of the moment arm to 3.11% throughout the flexion. Meanwhile, overall actuation mechanism becomes much simple than traditional joint pulleytendon mechanism.

The force/torque sensor is an important tool that gives a robot an ability to interact with their usage environments. Calibration is essential for these force/torque sensors to convert the raw sensor values to accurate forces and torques. However, in practice, the multi-axis force/torque sensor requires complex multi-step data processing, because of the coupling effects and nonlinearity of sensors. Moreover, accuracy is not guaranteed. To solve this problem, we propose an accurate force/torque sensor calibration method that can calibrate the sensor in single step by using deep-learning algorithm, and introduce the method for modeling the DNN(deep neural network) used in this calibration process. In addition, we also explain some tricks for learning, and then verify the calibration results through several experiments.

Path planning in complicated environments is a time consuming and computationally expensive task. Especially in high-dimensional configuration spaces with complex obstacles, searching for a proper path while avoiding collisions is still challenging. This paper presents an improved sampling-based algorithm, called the Goal Oriented sampling method (GO sampling) that quickly generates an initial solution overcoming these problems. GO sampling extends the sampling method of the Rapidly-exploring Random Tree (RRT) algorithm. GO sampling is able to identify the initial solution in a shorter time than that of the RRT algorithm and shows significant improvement in computational efficiency. The algorithm is evaluated with simulations in 2D and 3D space.

This paper presents a novel anthropomorphic robot hand with IMC joints. IMC joints are applied to robot hand to grasp various objects regardless of the size and get large overlapped workspace between thumb and other fingers, which allow all the fingertips to be located on a single spot in a wide range. Desinged IMC joints of robot hand are always rotated with 1:1 ratio by mechanical constraint using passive tendon. Actuation module consists of miniature BLDC motor and ballscrew drives two IMC joints with a single motor. To evaluate the advantages of robot hand with IMC joints, grasping experiments are performed.

This paper presents a new joint actuation mechanism, called Active DIP-PIP (ADP) joint, for robotic finger. The mechanism consists of a pair of moveable pulleys and springs to generate both linked and adjustable motion. While the set of DIP (Distal-Interphalangeal) and PIP (Proximal-Interphalangeal) joint shows coupled movement in free space, it moves adaptively when it contacts with an object. The torsion springs attached on each joints ensure 1:1 ratio movement while finger is moving freely and produce additional joint torque while grasping an object. In addition, actuation module composed of miniature BLDC motor and ball screw allows each joint to be back drivable.

This paper proposes a method of exploring the global shape of an unknown object using information on local geometric features. In the first, we introduce a rolling and sliding motion of a fingertip with a force/torque sensor to estimate an unknown local curvature. Also, a recognition algorithm for local geometry using normal curvature equations is presented, which are composed of principal curvatures and principal direction. Finally, to reconstruct the global shape of the object, we propose an interpolation method using principal curvatures at contact points. The proposed method is verified using a hand-arm system consisting of an industrial robot arm and an anthropomorphic robot hand with a 6-axis force/torque sensor. The effectiveness of the proposed method is experimentally validated for different type of objects.

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

The versatility of a human hand is what the researchers eager to mimic. As one of the attempt, the redundant degree of freedom in the human hand is considered. However, in the force domain the redundant joint causes a control issue. To solve this problem, the force control method for a redundant robotic hand which is similar to the human is proposed. First, the redundancy of the human hand is analyzed. Then, to resolve the redundancy in force domain, the artificial minimum energy point is specified and the restoring force is used to control the configuration of the finger other than the force in a null space. Finally, the method is verified experimentally with a commercial robot hand, called Allegro Hand with a force/torque sensor.