Ultrasound imaging is widely used for early breast tumor screening in clinical practice due to its operational simplicity and absence of radiation exposure. In this paper, a prone position autonomous breast ultrasound scanning system is developed to improve the quality and repeatability of image acquisition. A practical scanning path generation algorithm is proposed for autonomous breast scanning, which is applicable to linear, radial and anti-radial scanning protocol. Furthermore, a novel hybrid admittance control method is proposed, incorporating force-deformation characteristics of breast and a transition module to address force fluctuations and overshoot. Experimental results demonstrate that the proposed method effectively improves the smoothness and accuracy of scanning paths. During the contact collision stage, probe-breast interaction force exhibits a slow initial buildup, rapid mid-phase acceleration, and final asymptotic stabilization, which is more physiologically compatible with human interaction. Force tracking performance further validates the effectiveness of the proposed method, with maximum errors of 0.1265 N (T=0.008 s) and 0.1810 N (T=0.016 s), and minimum errors of -0.1120 N (T=0.008 s) and -0.1210 N (T=0.016 s) respectively. During the anti-radial scanning experiment, the mean confidence of the acquired images remains close to 0.5, while the confidence weighted barycentre remains around 0, indicating that the quality of ultrasound image is both good and stable.

Humanoid robots are engineered to navigate terrains akin to those encountered by humans, which necessitates human-like locomotion and perceptual abilities. Currently, the most reliable controllers for humanoid motion rely exclusively on proprioception, a reliance that becomes both dangerous and unreliable when coping with rugged terrain. Although the integration of height maps into perception can enable proactive gait planning, robust utilization of this information remains a significant challenge, especially when exteroceptive perception is noisy. To surmount these challenges, we propose a solution based on a teacher-student distillation framework. In this paradigm, an oracle policy accesses noise-free data to establish an optimal reference policy, while the student policy not only imitates the teacher’s actions but also simultaneously trains a world model with a variational information bottleneck for sensor denoising and state estimation. Extensive evaluations demonstrate that our approach markedly enhances performance in scenarios characterized by unreliable terrain estimations. Moreover, we conducted rigorous testing in both challenging urban settings and off-road environments, the model successfully traverse 2 km of varied terrain without external intervention.

Piano-playing tasks, which effectively demonstrate bimanual coordination capabilities in humanoid robots, are increasingly becoming a research focus. However, prior research has predominantly focused on Cartesian space trajectory planning without adequately addressing real-world obstacle avoidance constraints and manipulator acceleration limits. This paper proposes a hierarchical trajectory planning framework that systematically incorporates both obstacle avoidance and acceleration constraints. Firstly, discrete Cartesian path points are generated using a dynamic programming approach; secondly, joint space path points are derived considering obstacle avoidance and joint limit constraints through dynamic programming; thirdly, the joint space trajectory is interpolated using a Jacobian inverse-based method; finally, the trajectory is refined using Model Predictive Control (MPC). Experimental results demonstrate that the proposed method produces trajectories satisfying both obstacle avoidance and acceleration constraints, enabling fluent piano piece execution in real-world environments.

Expanding the input bandwidth of the human-machine interface to capture more control intentions from the human is key to achieving dexterous control of multi-degree-of-freedom prosthetic hands/wrists. This paper presents a wearable intelligent prosthesis system based on multimodal fusion, which integrates voice interaction, myoelectric control, limb movement decoding, and computer vision-based environmental perception. The system supports intention estimation throughout the entire process from grasping to operation, enabling synchronized control of 4 grasp gestures and 2 wrist DOFs. Moreover, all these decisions are made automatically during the user’s natural and continuous body movements. Six able-bodied subjects and two amputee subjects participated in a comparative experiment involving multi-object grasping and operation in a cluttered environment. Compared to myoelectric pattern recognition, our method demonstrated significant advantages in improving grasp and operation efficiency (reducing total time, grasp time, and operation time by 67.62%, 67.89%, and 67.40% for able-bodied subjects, and by 73.93%, 69.82%, and 76.67% for amputees). It also reduced control burden, with gesture and wrist myocontrol times decreasing by 71.77% and 100% in able-bodied subjects, and by 80.59% and 100% in amputees. These advantages are not limited to the grasp object, grasp part, operation type, or the subject involved. Compared to other semi-autonomous control methods, our method achieved a higher reduction in time metrics, with reduction rates ranging from 22.61% to 84.82% higher, resulting in a more significant performance improvement. Furthermore, the questionnaire showed that the system was well-recognized by the subjects in terms of operational robustness, wearing comfort, and user experience.

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills.

The construction of large-scale space facilities requires the use of on-orbit construction technology. However, several of its key components, such as standard interface design, compliant control methods, and path planning for multi-branch robots, still need improvement before practical application. This paper presents a comprehensive solution for on-orbit construction tasks, encompassing a novel standard interface, docking control method, and path planning method for space multi-branch robots. Firstly, a novel standard interface is introduced, which features multiple mating modes and a lightweight design. Additionally, a compliant docking method is provided to generate lower contact forces along the Z-direction. Furthermore, for four-armed space robots, a hierarchical planning method is proposed, which innovates in environment map construction and locomotion planning. Specifically, the closed-form Minkowski sum method is employed to solve the robot’s free space, and a concise locomotion method is elucidated based on transition support points. Finally, simulations and experiments are conducted.

Grasping is usually the initial stage of robotic manipulation tasks. High-precision grasping can reduce the uncertainty of the target and is beneficial for completing downstream manipulation tasks. This article proposes a coarse-to-fine grasping pose estimation scheme for cluttered environments, which can achieve submillimeter grasping accuracy using only consumer-level cameras. In the fine estimation stage, a cascaded end-to-end grasping pose prediction model is designed. We propose a new regularization method based on the semantic segmentation priors to avoid the overfitting problem. Also, an object-level data augmentation method is adopted to adapt the model to cluttered environments. With this method, the model trained with the data collected under a pure background can be generalized to cluttered environments. A large variety of typical experiments are conducted to validate our algorithm, including insertion tasks, screwing tasks, unlocking tasks, and door-opening tasks.

Aiming at the trajectory planning problem considering kinematic and dynamic manipulability optimizations, this article proposes an acceleration level manipulability maximization (ALMM) scheme to solve the problem in acceleration level for the first time. The manipulability index with nonlinear characteristics is reconstructed in acceleration level by a novel multilevel simultaneous processing strategy. The minimum joint velocity index is introduced to ensure the convergence of the system and maintain the velocity at a low level. Subsequently, the ALMM scheme, which includes pose maintenance, manipulability optimization, joint velocity minimization, and joint physical limit avoidance, is constructed and further formulated as a unified quadratic program. Then, a recurrent neural network with rigorously provable convergence is designed for the ALMM method. Simulations and physical experiments illustrate that the ALMM scheme can accomplish the manipulability optimization task excellently. Comparisons further verify the superiority of this scheme.

A kinematics control scheme in acceleration level is proposed to address the trajectory planning problem of redundant space manipulators under unknown loads or external forces. To achieve optimization of the joint torque from multiple sources, which is nonconvex relative to joint angles, quadratic programming (QP) is reconstructed through a novel multilevel simultaneous minimization scheme. This scheme, together with joint velocity norm minimization and physical limits avoidance formulated as the objective function and bound constraint into the QP problem. Subsequently, an extended recurrent neural network and intelligent numerical method, which are proven to converge, are established to solve the problem. Two trajectory tracking path simulations and experiments demonstrate the superb performance of this scheme when the manipulator is subjected to external forces or operating unknown loads. Comparisons with other schemes show that the proposed scheme is safer and more applicable.

From the optimization perspective, this article proposes a novel actual shape-based obstacle avoidance synthesized by velocity–acceleration minimization (ASOA-VAM) scheme that performs operational tasks safely in a complex environment utilizing redundant manipulators. Concretely, an actual shape-based obstacle avoidance (ASOA) strategy with a variable magnitude escape acceleration using the Gilbert–Johnson–Keerthi distance algorithm is presented. Trajectory tracking, the end-effector’s errors feedback, and the joint multilevel physical limits (joint angle, -velocity, and -acceleration limits) avoidance are also incorporated into this optimization scheme. Meanwhile, the velocity–acceleration minimization (VAM) measure is developed. Combining the ASOA strategy with the VAM measure, the ASOA-VAM scheme is formed and further reformulated as a quadratic program (QP). Moreover, a recurrent neural network with theoretically provable convergence is designed to solve the QP online. Finally, simulations, comparisons, and experiments of a 7-degree-of-freedom manipulator with engineering applications illustrate the ASOA-VAM scheme’s effectiveness, accuracy, superiority, and physical realizability.

Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks. It is difficult to give a generic threshold that works well on all images as the image contents are totally different. This paper presents an adaptive, robust and effective edge detector for real-time applications. According to the two-dimensional entropy, the images can be clarified into three groups, each attached with a reference percentage value based on the edge proportion statistics. Compared with the attached points along the gradient direction, anchor points were extracted with high probability to be edge pixels. Taking the segment direction into account, these points were then jointed into different edge segments, each of which was a clean, contiguous, 1-pixel wide chain of pixels. Experimental results indicate that the proposed edge detector outperforms the traditional edge following methods in terms of detection accuracy. Besides, the detection results can be used as the input information for post-processing applications in real-time.

This study presents a fast and robust ellipse detector based on edge following method. The detector first extracts segments using an edge predictor based on curvature analysis. Then, line segments are generated based on length condition other than least‐squares approximation. After that, potential ellipses are detected based on edge curvature and convexity. In addition, a re‐find contours detection method is introduced to improve the accuracy by searching edge points in the missing part of the ellipse. The performance of the detector has been tested on different datasets containing both synthetic and real images with three other algorithms based on the edge following method. Experimental results indicate that the proposed method always has the fastest execution time. Besides, it advances the state of the art in accuracy in most cases. Generally speaking, it is a fast, robust and effective ellipse detector for real‐time applications.

Pattern recognition (PR) methods are commonly utilized in the extraction of motion intentions from myoelectric signals, which is realized by relating several electromyogram (EMG) patterns to specific types of motion. Researchers have reported that the hand-engineering features widely used in PR-based methods can be significantly affected by external confounding factors that diminish their accuracy and robustness in clinical settings. Moreover, since only simple mapping is carried out from the feature space to the task space (involving, for the most part, discrete motion intentions), there is no opportunity to exploit fully the underlying mechanism of synergic neuromuscular control. Inspired by deep learning, we have proposed a novel convolutional neural network (CNN) structure based on the characteristics of raw EMG signals that can effectively decode complex wrist movements with three degrees of freedom (DOF) directly from raw EMG signals rather than relying on hand-engineering features. Our method has the potential to incorporate more information than other models by enlarging the training dataset. We demonstrate here that our method performs significantly better (in terms of R2) than the current state-of-art regression method (i.e., support vector regression), especially when confounding factors are involved. We further found that this CNN-based decoding method can be generalized when multiple healthy subjects are taken into account. For a new subject, our method can provide an appropriate control over 3-DOF cursor movements on a screen even without a specific training.

3-D object retrieval has been a hot research topic in recent years. Within such a field, view-based approaches are attracting increasing attention because of the flexibility of data representation as well as the reported state-of-the-art performance. One of the most important issues related to view-based 3-D object retrieval is how to learn embedding features that are discriminative across classes while being compactly distributed within each class. In this paper, we analyze the difference between the two tasks of classification and retrieval, and propose a novel way to learn a view-pooling feature via a triplet network. In addition, we propose a new loss, named cube loss, which is able to sample a number of triplets equal to the cube of the samples in a batch. With the new loss, both hard-negative and hard-positive pairs can be effectively investigated. The experimental results on the ModelNet benchmark demonstrate that the proposed method achieves superior performance compared to state-of-the-art approaches.

We present a novel regularization technique for learning local feature descriptors based on statistical information extracted from batches of training samples. With the proposed regularization term, we learn a descriptor distribution in Euclidean space that aims at minimizing the overlap between the distributions of positive pairs and that of negative pairs. The proposed method is able to improve the performance of pairwise and triplet losses with various deep convolution network architectures. This improvement is demonstrated through two different types of architectures, able to obtain state-of-the-art results on the reference benchmark for local feature matching.

How to design a robotic hand for reproducing the move characteristics of human hand joints is a big challenge in robotics. In this paper, we present an approach to determine the actuation configuration based on the statistical results of hand joint angle in different grasps. A relationship between the basic statistical metrics and actuation configuration strategies is built. In this case, a novel actuation configuration is proposed and the joints of four fingers are arranged into five actuation modules. For the mechanical implementation, the motion of human four finger joints is decomposed to proportion motion, differential motion and chain proportion motion, mechanically implemented by pulley, planetary gear differential module and gear transmission chain. Finally, the implemented mechanism is embedded in palm, and the mechanical implementation to the human hand move characteristics is verified by the measured joint angles of the robotic hand when actuators separately move along PC1 and PC2. Meanwhile, the robotic hand can grasp different objects with a versatile grasp function.

To allow collaborative robots to work efficiently and effectively with their human partners, one of the critical functions they need is to precisely and robustly recognize human intentions, i.e., what action they will perform next. In this paper, we present a Recurrent Convolutional Neural Networks (RCNN)-based system that is capable of recognizing a human intention much earlier than the intended action takes place. The system consists of two main components, a Deep Convolutional Neural Networks (DCNN) component that extracts spatial patterns of human activities and a Long Short-Term Memory (LSTM) component that extracts temporal patterns of human activities. We demonstrate the power of our proposed system to data of humans manipulating objects. The results show that our system has superior performance than many existing algorithms in term of recognition accuracy. Moreover, our system can achieve a quite high intention prediction accuracy (about 80%) provided with only the first 80% of the data sequence.

To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.

In the paper, a new method named Cartesian Space Synchronous Impedance Control (CSSIC) has been developed. The method combines the synchronous control and the impedance control together which can not only be used in the position control but can also realize the purpose of force control of the system with multi-manipulators. Therefore, if there are multi-manipulators grasping the same object, it can ensure the object will not fall and not be destroyed. The mathematical validation process and the stability proof of the method have been given. Besides, an experiment setup which has two 7-DOF robot arms has been established to testify the method. The testing result shows that the dual arm system, under disturbance, can ensure stable grasping of the object with the CSSIC method.

A tactile sensor based varying contact point manipulation strategy that utilizes the mathematical models based on the assumption of fixed contact points is proposed to manipulate unknown objects with rolling contact for dexterous robot hand. In this strategy, fingertip tactile sensor is utilized to detect contact position and to update the size parameters about the finger end-link when rolling occurs. Experimental results show that the strategy can effectively improve the rolling contact manipulation performance when dexterous robot hand manipulates unknown objects.

Based on HIT/DLR (Harbin Institute of Technology/Deutsches Zentrum für Luft- und Raumfahrt) Prosthetic Hand II, an anthropomorphic controller is developed to help the amputees use and perceive the prosthetic hands more like people with normal physiological hands. The core of the anthropomorphic controller is a hierarchical control system. It is composed of a top controller and a low level controller. The top controller has been designed both to interpret the amputee's intensions through electromyography (EMG) signals recognition and to provide the subject-prosthesis interface control with electro-cutaneous sensory feedback (ESF), while the low level controller is responsible for grasp stability. The control strategies include the EMG control strategy, EMG and ESF closed loop control strategy, and voice control strategy. Through EMG signal recognition, 10 types of hand postures are recognized based on support vector machine (SVM). An anthropomorphic closed loop system is constructed to include the customer, sensory feedback system, EMG control system, and the prosthetic hand, so as to help the amputee perform a more successful EMG grasp. Experimental results suggest that the anthropomorphic controller can be used for multi-posture recognition, and that grasp with ESF is a cognitive dual process with visual and sensory feedback. This process while outperforming the visual feedback process provides the concept of grasp force magnitude during manipulation of objects.

Normally, people always believe that the more measurement poses used in a robot calibration process, the more accurate result can be obtained. However, the accuracy improvement converges to a threshold after a number of measurement poses. Moreover, robot calibration is a time consuming process, too many poses would seriously complicate the process and consumedly increase the spending time. In this paper, an optimal measurement pose number searching method was proposed to improve the calibration method in time spending aspect. Optimal robot poses were added to an initial pose set one by one to establish a new pose set for the robot calibration. The root mean squares (RMS) of the end-effector pose errors after being calibrated by using these pose sets were calculated. The optimal number of the configuration set which correspond to the least RMS of pose error can then be obtained. This algorithm can get higher end-effector accuracy, meanwhile consume less time. The simulation on a serial robot manipulator with 24 unknown kinematic parameters shows that the end-effector pose accuracy after calibrated by the using of the optimal pose set is much better than the result before calibration, and is better than the using of a random pose set.

This paper is to develop an ontology-based mobile environment for collaborative product design and manufacture, which enables geographically dispersed team members to collaborate over the internet within the total design or manufacturing process, including product design specification, conceptual design, detail design, manufacture and recycling. In this paper, a database which based on a new method named ontology is built up to demonstrate the all related information in order to achieve an efficient and convenient management. According these two achievements, a new data management system is built and tested in the mobile device and compared to the traditional data management system at the end of this paper.

This paper presents experimental results on the five-finger dexterous robot hand DLR-HIT II, with Cartesian impedance control based on joint torque and nonlinearity compensation for elastic dexterous robot joints. To improve the performance of the impedance controller, system parameter estimations with extended kalman filter and gravity compensation have been investigated on the robot hand. Experimental results show that, for the harmonic drive robot hand with joint toruqe feedback, accurate position tracking and stable torque/force response can be achieved with cartesian and joint impedance controller. In addition, a FPGA-based control architecture with flexible communication is proposed to perform the designed impedance controller.

A five-fingered, multi-sensory biomechatronic hand with sEMG interface is presented. The cambered palm is specially designed to enhance the stability while grasping. The location of the thumb is designed by maximizing interaction area between the thumb and other fingers. The opposite thumb could grasp along a cone surface, while maintaining its function. By taken the advantage of coupling linkage mechanism, each finger with three phalanges could fulfill flexion-extension movement independently. Besides, each finger is equipped with torque and position sensors. Thus, the cosmetics and dexterity are improved remarkably compared to conventional prosthesis. The hardware architecture is divided into control system and EMG signal processing system. Moreover, a novel two-stage decision strategy combing the position-based impedance control scheme is implemented to realize the real-time sEMG control of the hand. According to the grasp experiment results, the hand can accomplish several grasp modes stably; the success rate of 10 modes is up to 90%.

In order to eliminate the drawbacks of conventional force feedback gloves, a new type of master hand has been developed. By utilizing three ¿four-bar mechanism joint¿ in series and wire coupling mechanism, the master finger transmission ratio is kept exact 1:1.4:1 in the whole movement range and it can make active motions in both extension and flexion direction. Additionally, to assure faster data transmission and near zero delay in master-slave operation, a digital signal processing/field programmable gate array (DSP/FPGA-FPGA) structure with 200 ¿s cycle time is designed. The operating modes of the master hand can be contact or non-contact, which depends on the motion states of slave hand, free motion or constrained motion. The position control employed in non-contact mode ensures unconstrained motion and the force control adopted in contact mode guarantees natural contact sensation. To evaluate the performances of the master hand, an master-slave control experiment based on force-position control method between the master hand and DLR/HIT hand is conducted. The results demonstrate this new type master hand can augment telepresence.

This paper presents a DSP/FPGA based multi-sensory flexible-joint robot including modular mechanics, hardware and software architecture. The robot is composed of four modular flexible joints and a DLR-HIT-Hand. In each joint there is a Field Programmable Gate Array (FPGA) for sensor data processing, brushless DC motor control and communication. The kernel of the hardware system is a PCI-based high speed floating-point Digital Signal Processor (DSP) for the arm/hand Cartesian level control, and FPGA for two high speed (up to 25 Mbps) real-time Serial Buses communication with the arm and hand. All the electronics are integrated into the joint to achieve the high modularity and reliability. Cartesian impedance control and position control with on-line gravity compensation are realized for regulation tasks of the robot with elastic joints. In addition, the experiments of the joint impedance control and Cartesian impedance control demonstrate that the multi-sensors highly integrated robot has a good performance.

The multi-DOF prosthetic hand's myocontrol needs to recognize more hand gestures (or motions) based on myoelectric signals. This paper presents a classification method, which is based on the support vector machine (SVM), to classify 19 different hand gesture modes through electromyographic (EMG) signals acquired from six surface myoelectric electrodes. All hand gestures are based on a 3-DOF configuration, which makes the hand perform like three-fingered. The training performance is very high within each test session, but the cross-session validation is typically low. Acceptable cross-session performance can be achieved by training with more sessions or fewer gesture modes. A fast rhythm muscle contraction is suggested, which can make the training samples more resourceful and improve the prediction accuracy comparing with a relative slow muscle contraction method. For many precise grasp tasks, it is beneficial to the prosthetic hand's myocontrol if we can efficiently extract the grasp force directly from EMG signals. Through grasping a JR3 6 dimension force/torque sensor, the force signal applying to the sensor can be recorded synchronously with myoelectric signals. This paper uses three methods, local weighted projection regression (LWPR), artificial neural network (ANN) and SVM, to find the best regression relationship between these two kinds of signals. It reveals that the SVM method is better than ANN and LWPR, especially in the case of cross-session validation. Also, the performance of grasping force estimation based on specific hand gestures is superior to the performance of grasping with random fingers.

This paper presents a new developed multisensory five-fingered dexterous robot hand: the DLR/HIT Hand II. The hand has an independent palm and five identical modular fingers, each finger has three DOFs and four joints. All the actuators and electronics are integrated in the finger body and the palm. By using powerful super flat brushless DC motors, tiny harmonic drivers and BGA form DSPs and FPGAs, the whole fingerpsilas size is about one third smaller than the former finger in the DLR/HIT Hand I. By using the steel coupling mechanism, the phalanx distalpsilas transmission ratio is exact 1:1 in the whole movement range. At the same time, the multisensory dexterous hand integrates position, force/torque and temperature sensors. The hierarchical hardware structure of the hand consists of the finger DSPs, the finger FPGAs, the palm FPGA and the PCI based DSP/FPGA board. The hand can communicate with external with PPSeCo, CAN and Internet. Instead of extra cover, the packing mechanism of the hand is implemented directly in the finger body and palm to make the hand smaller and more human like. The whole weight of the hand is about 1.5Kg and the fingertip force can reach 10N.

In this paper, an improved algorithm to measure Chinapsilas EMU spacesuit joint damping parameters for the old passive robot system is presented. The measuring principle is based on robot kinematics and dynamics. Firstly, a kinematic model of the passive robot and the EMU spacesuitpsilas arm is built according to the special mechanical structure. Secondly, improved methods of solving the inverse kinematics of the EMU spacesuit arm including flexible joints are proposed. And finally, a new algorithm that eliminates the gravity effect is implemented to calculate the damping torques of the EMU spacesuit joints. Experimental results and verification on SGI workstation proved the correctness and effectiveness of the proposed measuring method.

This paper presents a new developed multisensory five-fingered dexterous robot hand : the DLR/HIT Hand II. The hand has an independent palm and five identical modular fingers, each finger has three DOFs and four joints. All the actuators and electronics are integrated in the finger body and the palm. By using powerful super flat brushless DC motors, tiny harmonic drives and BGA form DSPs and FPGAs, the whole fingerpsilas size is about one third smaller than the former finger in the DLR/HIT Hand I. By using the steel coupling mechanism, the phalanx distalpsilas transmission ratio is exact 1:1 in the whole movement range. At the same time, the multisensory dexterous hand integrates position, force/torque and temperature sensors. The hierarchical hardware structure of the hand consists of the finger DSPs, the finger FPGAs, the palm FPGA and the PCI based DSP/FPGA board. The hand can communicate with external with PPSeCo , CAN and Internet. Instead of extra cover, the packing mechanism of the hand is implemented directly in the finger body and palm to make the hand smaller and more human like. The whole weight of the hand is about 1.5 Kg and the fingertip force can reach 10N.

Based on under-actuated mechanism and coupling principle, a five-fingered, multi-sensory and biomechatronic prosthetic hand has been designed. The multi-DOF hand comprises 13 joints and is controlled by 3 motors. Actuated by only one motor, the thumb can move along a cone surface which is superior in the appearance. Also driven by one motor and transmitted by springs, the mid finger, the ring finger and the little finger can move simultaneously and envelop objects with complex shape. On the other hand, during the hand designation, the handsome appearance has been considered and its glove prototype has been designed. The hardware system and the sensory system have been developed. Through Bluetooth wireless protocol, the hand can be controlled by voice signal. Furthermore, it can also be controlled by electromyography (EMG) signal like most prosthetic hand in existence. It has been verified by experiments that the hand has strong capability of self-adaptation grasp and can accomplish precise and power grasp

The cost and risks associated with the execution of robotics tasks in space require that all procedures be verified on Earth prior to their execution. The DLR-HIT Joint Robotics Lab is responsible for the verification of all the tasks of space manipulator of the Chinese free-flight robot space satellite system. We are currently developing the free-flight robot task verification facility (FTVF) through a hardware-in-the-loop way. It consists of a series of simulation and analysis tools to be used for verifying the kinematics (clearance, interface reach, degrees of freedom), dynamics (computed torque, flexibility), visual accessibility (ability to see the work site) and computation power (control period). This paper presents both theoretical and experimental study on the control of a free-flying robot manipulator for space application. The goal of the study is to develop a new control method and hardware-in-the-loop simulation system for target capturing with vision sensor in micro-gravity space environment, considering the dynamical interaction between the space robot and its mobile satellite base. Finally experimental results demonstrate the effect of the simulation system

A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the prosthetic hand can achieve more prehensile postures such as power grasp, fingertip grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its high recognition capability

This paper presents a surface Electromyography (EMG) motion pattern classifier which combines Levenberg-Marquardt (LM) based neural network with parametric Autoregressive (AR) model. This motion pattern classifier can successfully identify three types of motion of thumb, index finger and middle finger, by measuring the surface EMG through two electrodes mounted on the flexor digitorum profundus and flexor pollicis longus. Furthermore, via continuously controlling single finger’s motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its fast learning speed, high recognition capability and strong robustness.

In this paper, FPGA (field programmable gate array) based hardware architecture for the HIT/DLR hand has been investigated. With the FPGAs for lower level control and DSP (digital signal processor) for higher level control, the whole hardware is very intelligent. By using the high capacity of FPGAs, the additional hardware such as communication controller and PWM generators, can be implemented in a single chip and the hardware system is more flexible and compact. In each finger there is an FPGA for data collection, brushless DC motors control and communication with palm's FPGA by point-to-point serial communication (PPSeCo). The kernel of the hardware system is a PCI-based high speed floating-point DSP for data processing, and FPGA for high-speed (up to 25Mbps) real-time serial communication with the palm's FPGA. There needs only 4 cables for the data transmission and the sampling cycle for each sensor is only 200 /spl mu/s. This paper presents the basic ideas behind the HIT/DLR hand's hard- and software architecture adapted to new needs in data processing.

The paper presents hardware and software architectures of the HIT/DLR Hand. The hand has four identical fingers and an extra degree of freedom (d.o.f) for the palm. In each finger, there is a re-configurable Field Programmable Gate Array (FPGA) for data acquisition, Brushless DC (BLDC) motor control and communication with the palm's FPGA by Point-to-Point Serial Communication (PPSeCo). The kernel of the hardware system is a PCI-based high speed floating-point Digital Signal Processor (DSP) for data processing, and an FPGA for high speed (up to 25 Mbps) real-time serial communication with the palm's FPGA. In order to achieve high modularity and reliability of the hand, a fully mechatronic integration and analog signals in-situ digitalization philosophy are implemented to minimize the dimension, number of the cables (5 cables including power supply) and protect data communication from outside disturbances. Furthermore, according to the hardware architecture of the hand, a hierarchical software architecture has been established to perform all data processing and control of the hand. The software structure provides basic Application Programming Interface (API) functions and skills to access all hardware resources for data acquisition, computation and teleoperation.

Using human action to guide robot execution can greatly reduce the planning complexity. We calibrate a human hand model and map its motion to a four-finger dexterous robot hand. The parameters of human hand model are determined by open-loop kinematic calibration method based on a vision system. We analyze the kinematic difference between the human hand and dexterous robot hand, and present a modified fingertip mapping to solve the partial overlap of the fingertip workspaces. 3D graphic simulation and manipulation experiments show that the accuracy of the human hand model and the mapping method are sufficiently precise for teleoperation tasks.

At our institute, two generations of antropomorphic hands have been designed. In quite a few experiments and demonstrations we could show the abilities of our hands and gain a lot of experience in what artificial hands can do, what abilities they need and where their limitations lie. In this paper, we would like to give an overview over the experiments performed with the DLR hands, our hands abilities and the things that need to be done in the near future.

This paper presents the current work progress of HIT/DLR Dexterous Hand. Based on the technology of DLR Hand II, HIT and DLR are jointly developing a smaller and easier manufactured robot hand. The prototype of one finger has been successfully built. The finger has three DOF and four joints, the last two joints are mechanically coupled by a rigid linkage. All the actuators are commercial brushless DC motors with integrated analog Hall sensors. DSP based control system is implemented in PCI bus architecture and the serial communication between the hand and DSP needs only 6 lines(4 lines power supply and 2 lines communication interface). The fingertip force can reach 10N.

We prove a new necessary and sufficient condition for 2D three-finger equilibrium grasps and implement a geometrical algorithm for computing force-closure grasps of polygonal objects in this article. The algorithm is quite simple and only needs some algebraic calculations. An easily computable measure of how far a grasp is from losing force-closure is provided as well. Finally, we implement the algorithm and demonstrate its usefulness by an example.

This paper presents a novel passive robot system with 6 DOF force/torque sensor for measuring spacesuit joint damping parameters. Based on its special mechanical structure, a 3 DOF model of flexible IVA (intra vehicular activity) spacesuit joint has been built. Experimental results prove the effectiveness of the measuring principle. Potential application of the measuring system is discussed.

In this work, a cartesian impedance controller purposely designed for dexterous manipulation is described. Based on the main features of the DLR Hand II, concerning kinematic structure and sensory equipment of fingers, this control strategy allows to overcome the main problems encountered in fine manipulation, namely: effects of the friction (and unmodeled dynamics) on robot performances and occurrence of singularity conditions. The achieved control scheme bas been experimentally validated by testing it on a finger of the DLR Hand.

In this paper, we present a novel algorithm for computing three-finger force-closure grasps of 2D and 3D objects. In the case of a robot hand with three hard fingers and point contact with friction, new necessary and sufficient conditions for 2D and 3D equilibrium and force-closure grasps have been deduced, and a corresponding algorithm for computing force-closure grasps has been developed. Based on geometrical analysis, the algorithm is simple and only needs a few algebraic calculations. Finally, the algorithm has been implemented and its effectivity has been demonstrated by two examples.

This paper outlines the 2nd generation of multisensory hand design at DLR, based on the results of the DLR Hand I we analysed. An open skeleton structure for better maintenance with semi-shell housing and the new automatically reconfigurable palm have been equipped with more powerful actuators to reach 30 N on the fingertip. The newly designed sensors as the 6-DOF fingertip force torque sensor, the integrated electronics and the new communication architecture with a reduction of cabling to the hand to only 12 lines, are outlined. The Cartesian impedance control of all the fingers completes the new 13-DOF hand.

Describes design and development efforts in DLR's robotics lab towards a new generation of ultra-light weight robots with articulated hands. The design of fully sensorized joints with complete state feedback and the underlying mechanisms are outlined. The second light-weight arm generation is available now, as well as the second generation of a worldwide most highly integrated 4 finger-hand is available now. Thus we hope that important steps towards a new generation of service and personal robots have been achieved.

Presents a novel Cartesian impedance control for the DLR (German Aerospace Center) Hand based on joint torque measurements. The fingertip appears as mechanical impedance when it contacts with an unknown obstacle. The impedance parameters can be adjusted in a certain range as needed in any Cartesian coordinate system. There is no switching mode between the motions in the free space and in the constraint environment. The paper also gives a detailed analysis of the finger's kinematics and dynamics model. Experimental results have verified the effectiveness and robustness of the proposed scheme.

The main features of DLR's dextrous robot hand as a modular component of a complete robotics system are outlined in this paper. The application of robotics systems in unstructured servicing environments requires dextrous manipulation abilities and facilities to perform complex remote operations in a very flexible way. Therefore we have developed a multisensory articulated four finger hand, where all actuators are integrated in the hand's palm or the fingers directly. It is an integrated part of a complex light-weight manipulation system aiming at the development of robonauts for space. After a brief description of the hand and it's sensorial equipment the hard- and software architecture is outlined with particular emphasis on flexibility and performance issues. The hand is typically controlled through a data glove for telemanipulation and skill-transfer purposes. Autonomous grasping and manipulation capabilities are currently under development.

Gives a brief description of feedback control systems engaged in DLR's recently developed multisensory 4-finger robot hand. The work is concentrated on constructing the dynamic model and the control strategy for one joint of the fingers. One goal is to make the hand follow a dataglove for fine manipulation tasks. Our proposed strategy for this task is parallel torque/position control; sliding mode control is realized for the robust trajectory tracking in free space; while impedance control is provided for compliance control in the constrained environment; and an easily-designed parallel observer is used for the switch between these two control modes during the transition from or to contact motion. Some experimental results show the effectiveness of proposed strategy for the pure position control, torque control, and the transition control.