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The authors want to design an adaptive grasping control strategy without setting the expected contact force in advance to maintain grasping stable, so that the proposed control system can deal with unknown object grasping manipulation tasks.

The adaptive grasping control strategy is proposed based on bang-bang-like control principle and slippage detection module. The bang-bang-like control method is designed to find and set the expected contact force for the whole control system, and the slippage detection function is achieved by dynamic time warping algorithm.

The expected contact force can adaptively adjust in grasping tasks to avoid bad effects on the control system by the differences of prior test results or designers. Slippage detection can be recognized in time with variation of expected contact force manipulation environment in the control system. Based on if the slippage caused by an unexpected disturbance happens, the control system can automatically adjust the expected contact force back to the level of the previous stable state after a given time, and has the ability to identify an unnecessary increasing in the expected contact force.

Only contact force is used as feedback variable in control system, and the proposed strategy can save hardware components and electronic circuit components for sensing, reducing the cost and design difficulty of conducting real control system and making it easy to realize in engineering application field. The expected contact force can adaptively adjust due to unknown disturbance and slippage for various grasping manipulation tasks.

Multi-sensor fusion in robotic dexterous hands is a hot research field. However, there is little research on multi-sensor fusion rules. This study aims to introduce a multi-sensor fusion algorithm using a motor force sensor, film pressure sensor, temperature sensor and angle sensor, which can form a consistent interpretation of grasp stability by sensor fusion without multi-dimensional force/torque sensors.

This algorithm is based on the three-finger force balance theorem, which provides a judgment method for the unknown force direction. Moreover, the Monte Carlo method calculates the grasping ability and judges the grasping stability under a certain confidence interval using probability and statistics. Based on three fingers, the situation of four- and five-fingered dexterous hand has been expanded. Moreover, an experimental platform was built using dexterous hands, and a grasping experiment was conducted to confirm the proposed algorithm. The grasping experiment uses three fingers and five fingers to grasp different objects, use the introduced method to judge the grasping stability and calculate the accuracy of the judgment according to the actual grasping situation.

The multi-sensor fusion algorithms are universal and can perform multi-sensor fusion for multi-finger rigid, flexible and rigid-soft coupled dexterous hands. The three-finger balance theorem and Monte Carlo method can better replace the discrimination method using multi-dimensional force/torque sensors.

A new multi-sensor fusion algorithm is proposed and verified. According to the experiments, the accuracy of grasping judgment is more than 85%, which proves that the method is feasible.

The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations.

To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function.

Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand.

This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

The purpose of this paper is to design a multi-sensory anthropomorphic prosthetic hand and a grasping controller that can detect the slip and automatically adjust the grasping force to prevent the slip.

To improve the dexterity, sensing, controllability and practicability of a prosthetic hand, a modular and multi-sensory prosthetic hand was presented. In addition, a slip prevention control based on the tactile feedback was proposed to improve the grasp stability. The proposed controller identifies slippages through detecting the high-frequency vibration signal at the sliding surface in real time and the discrete wavelet transform (DWT) was used to extract the eigenvalues to identify slippages. Once the slip is detected, a direct-feedback method of adjusting the grasp force related with the sliding times was used to prevent it. Furthermore, the stiffness of different objects was estimated and used to improve the grasp force control. The performances of the stiffness estimation, slip detection and slip control are experimentally evaluated.

It was found from the experiment of stiffness estimation that the accuracy rate of identification of the hard metal bottle could reach to 90%, while the accuracy rate of identification of the plastic bottles could reach to 80%. There was a small misjudgment rate in the identification of hard and soft plastic bottles. The stiffness of soft plastic bottles, hard plastic bottles and metal bottles were 0.64 N/mm, 1.36 N/mm and 32.55 N/mm, respectively. The results of slip detection and control show that the proposed prosthetic hand with a slip prevention controller can fast and effectively detect and prevent the slip for different disturbances, which has a certain application prospect.

Due to the small size, low weight, high integration and modularity, the prosthetic hand is easily applied to upper-limb amputees. Meanwhile, the method of the slip prevention control can be used for upper-limb amputees to complete more tasks stably in daily lives.

A multi-sensory anthropomorphic prosthetic hand is designed, and a method of stable grasps control based on slip detection by a tactile sensor on the fingertip is proposed. The method combines the stiffness estimation of the object and the real-time slip detection based on DWT with the design of the proportion differentiation robust controller based on a disturbance observer and the force controller to achieve slip prevention and stable grasps. It is verified effectively by the experiments and is easy to be applied to commercial prostheses.

Underactuated fingers are adapted to generate several grasping modes for different tasks, and coupled fingers and self-adaptive fingers are two important types of them. Aiming to expand the application and increase adaptability of robotic hand, this paper aims to propose a novel grasping model, called coupled and indirectly self-adaptive (CISA) grasping model, which is the combination of coupled finger and indirectly self-adaptive finger.

CISA grasping process includes two stages: first, coupled and then indirectly self-adaptive grasping; thus, it is not only integrated with the good pinching ability of coupled finger but also characterized with the high flexibility of indirectly self-adaptive finger. Furthermore, a CISA hand with linkage-slider, called CISA-LS hand, is designed based on the CISA grasping model, consisting of 1 palm, 5 CISA-LS fingers and 14 degrees of freedom.

To research the grasping behavior of CISA-LS hand, kinematic analysis, dynamic analysis and force analysis of 2-joint CISA-LS finger are performed. Results of grasping experiments for different objects demonstrate the high reliability and stability of CISA-LS hand.

CISA fingers integrate two grasping modes, coupled grasping and indirectly self-adaptive grasping, into one finger. And a double-linkage-slider mechanism is designed as the switch device.

Hand gesture-based interaction can provide far more intuitive, natural and immersive feelings for users to manipulate 3D objects for virtual assembly (VA). A mechanical assembly consists of mostly general-purpose machine elements or mechanical parts that can be defined into four types based on their geometric features and functionalities. For different types of machine elements, engineers formulate corresponding grasping gestures based on their domain knowledge or customs for ease of assembly. Therefore, this paper aims to support a virtual hand to assemble mechanical parts.

It proposes a novel glove-based virtual hand grasping approach for virtual mechanical assembly. The kinematic model of virtual hand is set up first by analyzing the hand structure and possible movements, and then four types of grasping gestures are defined with joint angles of fingers for connectors and three types of parts, respectively. The recognition of virtual hand grasping is developed based on collision detection and gesture matching. Moreover, stable grasping conditions are discussed.

A prototype system is designed and developed to implement the proposed approach. The case study on VA of a two-stage gear reducer demonstrates the functionality of the system. From the users’ feedback, it is found that more natural and stable hand grasping interaction for VA of mechanical parts can be achieved.

It proposes a novel glove-based virtual hand grasping approach for virtual mechanical assembly.

The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments.

The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized.

The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches.

The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions.

The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

This paper presents a method to elaborate the selections of these parameters to achieve stable grasps. The performance of a prosthetic hand is mainly determined by its mechanical design. However, the effects of the geometric parameters of the hand configuration and the object sizes on the grasp stability are unknown.

First, the thumb functions of human hands are analyzed based on the anatomical model, and the configuration characteristics of the thumbs for typical prosthetic hands are summarized. Then a method of optimizing the thumb configuration is proposed by measuring the kinematic transmission performance of robotics. On the basis of the thumb configuration analysis, a design method of the prosthetic hand configuration is proposed based on form closure theory. The discriminant function of form closure is used to analyze and determine the hand configuration parameters.

An application of this method – the newly developed HIT V prosthetic hand – elaborates the optimization of the thumb configuration and the hand configuration, where the relation between the key hand configuration parameters and the discriminant function on condition of satisfying form closure, sustained by analytical equations and graphs, is revealed and visualized. An experimental verification shows that it is an effective method to design the prosthetic hand configuration available for grasping typical objects in our daily life.

The paper shows how to easily determine the geometric dimensions of the palm, phalanges and hand configuration, so that the desired range of object sizes can be obtained.

This paper aims to present the design and experiment of a modular multisensory prosthetic hand for applications. Design and experiment of a modular multisensory hand for prosthetic applications.

This paper reveals more details focusing on the appearance, mechanism design, electrical design and control of the prosthetic hand considering anthropomorphism, dexterity, sensing and controllability. The finger is internally integrated with the actuator, the transmission mechanism, the sensors and the controller as a modular unit. Integrated with multiple sensors, the prosthetic hand can not only perceive the position, the contact force and the temperature of the environment like a human hand but also provide the foundation for the practical control.

The experiments show that the prosthetic hand can accurately control the contact force to achieve stable grasps based on the sensors feedback and a simple and effective force-tracking impedance control algorithm. In addition, the experiments based on the cosmesis validate not only the cosmesis functionality but also the control performance for a prosthesis–cosmesis system.

Because of the small size, low weight, high integration, modularity and controllability, the prosthetic hand is easily applied to upper-limb amputees. Meanwhile, the finger as a modular unit is easy to be fixed, maintained and applied to a partial upper-limb amputee.

Each modular finger of the prosthetic hand integrated with the actuator, the transmission mechanism, the sensors and the controller as a whole can independently control the position and the force. The cosmetic glove design can provide pretty appearance without compromising the control performance.

The purpose of this paper is to present a novel multimodal approach to the problem of planning and performing a reliable grasping action on unmodeled objects.

The robotic system is composed of three main components. The first is a conceptual manipulation framework based on grasping primitives. The second component is a visual processing module that uses stereo images and biologically inspired algorithms to accurately estimate pose, size, and shape of an unmodeled target object. A grasp action is planned and executed by the third component of the system, a reactive controller that uses tactile feedback to compensate possible inaccuracies and thus complete the grasp even in difficult or unexpected conditions.

Theoretical analysis and experimental results have shown that the proposed approach to grasping based on the concurrent use of complementary sensory modalities, is very promising and suitable even for changing, dynamic environments.

Additional setups with more complicate shapes are being investigated, and each module is being improved both in hardware and software.

This paper introduces a novel, robust, and flexible grasping system based on multimodal integration.