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.

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.

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.

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.

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.

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

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.

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.