Cable-driven surgical instrument is a reusable and disposable component of Robot-assisted Minimally Invasive Surgery, and it is time-consuming to identify the motion hysteresis compensation model of each instrument in large-scale identification. Besides, the identification results failure caused by the loss of cable tension or lubrication conditions during repeated usage interferes with the accuracy of motion hysteresis compensation. To simplify the identification process and lengthen the instrument life, this paper leverages the relationship between actuate motor current and hysteresis phases, developing a novel motion hysteresis compensation method to compensate the motor reference trajectory. Firstly, prior knowledges including motor current and hysteresis curves of several instruments are collected to train the Hysteresis Identification model and Curve Generation model. Once the two models are well-trained in practical use, the only sensor data in need is the motor current for the Hysteresis Identification model, and the compensation curve will be generated pertinently for the instrument under current use. Finally, Feedforward Compensation scheme is conducted to compensate the reference trajectory of the actuate motor. Experiments study the range of hysteresis compensation errors when the method is applied to numerous surgical instruments with different motion hysteresis, and the feasibility and accuracy are verified. The method can be potentially applied to a wide range of cable-driven mechanisms facing the problem of large-scale identification or the identification results failure after repeated use. Note to Practitioners—This paper proposes a feasible motion hysteresis compensation method for cable-driven mechanism (CDM). For some CDMs like elongated cable-driven surgical instrument, their motion hysteresis characteristics will change during repeated use, and their distal angle and cable tension are not available in the environment of use. In these cases, the proposed method enables re-identification because the distal angle or cable tension are not needed during the practical use of our method. Besides, it is easy and low-cost for large-scale identification due to the simplicity of identification procedure. Once the Hysteresis Identification model and Curve Generation model are trained using the pre-collected prior knowledge including motor current and the angle of the end effector, the only sensor data in need to identify and compensate a certain CDM is the its motor current. Then, the pertinent compensation curve for the CDM device can be generated for the actuate motor to follow. The experimental results reveal that the competitive performance with other state-of-the-arts can be achieved. In the future research, we will integrate dynamic characteristics of CDM into the method to expand the scope of application.

In this study, we address the challenge of estimating 3D body pose, shape, and depth relationships from single RGB images in crowded scenes. The difficulty lies in the limited availability of in-the-wild training samples, which feature densely populated scenes. To mitigate this issue, we introduce a synthesis-based approach that fuses multiple human samples into a single composite scene. Our innovative scene-aware blending technique maintains human-scene consistency by positioning individuals within plausible locations and adjusting their scales to conform to 3D settings. Furthermore, our method enables flexible per-subject occlusion management during the blending process, bolstering the robustness of 3D human body representations through a novel de-occlusion training scheme. We present a one-stage model, CBD, designed to learn monocular regression of 3D people in crowds by leveraging blending and de-occlusion techniques. Our quantitative and qualitative evaluations on four benchmark datasets reveal that CBD surpasses existing state-of-the-art approaches in terms of 3D human pose and mesh regression accuracy, thereby establishing it as a promising solution for monocular 3D human mesh recovery in densely populated scenes.

Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of very different sizes, e.g. from infants to adults. To solve this, we need several things. First, we develop a novel method to infer the poses and depth of multiple people in a single image. While previous work that estimates multiple people does so by reasoning in the image plane, our method, called BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly reason about depth. BEV reasons simultaneously about body centers in the image and in depth and, by combing these, estimates 3D body position. Unlike prior work, BEV is a single-shot method that is end-to-end differentiable. Second, height varies with age, making it impossible to resolve depth without also estimating the age of people in the image. To do so, we exploit a 3D body model space that lets BEV infer shapes from infants to adults. Third, to train BEV, we need a new dataset. Specifically, we create a “Relative Human” (RH) dataset that includes age labels and relative depth relationships between the people in the images. Extensive experiments on RH and AGORA demonstrate the effectiveness of the model and training scheme. BEV out-performs existing methods on depth reasoning, child shape estimation, and robustness to occlusion. The code11https://github.com/Arthur151/ROMP and dataset22https://github.com/Arthur151/Relative_Human are released for research purposes.

In this paper, we focus on simultaneously recovering the 3D mesh of multiple body parts from a single RGB image. One of the main challenges is that available datasets with full-body 3D annotations are very limited. This results in poor generalization ability of existing learning-based methods. Existing optimization-based methods iteratively fit the 3D mesh to the 2d pose, which is very time-consuming. To address these limitations, we propose to integrate multiple 3D single-body-part datasets to create a highly diverse whole-body 3D motion space for learning from controllable synthetics. Compared with the learning-based approaches, the proposed method greatly alleviates the reliance on training data. Compared with the optimization-based approaches, the proposed method is a hundred times faster. Our proposed method also outperforms previous state-of-the-art methods on CMU Panoptic dataset.

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset's quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMG-Map input. (ii) Deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

This paper focuses on the regression of multiple 3D people from a single RGB image. Existing approaches predominantly follow a multi-stage pipeline that first detects people in bounding boxes and then independently regresses their 3D body meshes. In contrast, we propose to Regress all meshes in a One-stage fashion for Multiple 3D People (termed ROMP). The approach is conceptually simple, bounding box-free, and able to learn a per-pixel representation in an end-to-end manner. Our method simultaneously predicts a Body Center heatmap and a Mesh Parameter map, which can jointly describe the 3D body mesh on the pixel level. Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map. Equipped with such a fine-grained representation, our one-stage framework is free of the complex multi-stage process and more robust to occlusion. Compared with state-of-the-art methods, ROMP achieves superior performance on the challenging multi-person benchmarks, including 3DPW and CMU Panoptic. Experiments on crowded/occluded datasets demonstrate the robustness under various types of occlusion. The code, released at https://github.com/Arthur151/ROMP, is the first real-time implementation of monocular multi-person 3D mesh regression.

In this paper, the configuration transformation of Wheel-Legged Robot (WLR) is studied, which can enable the robot to change its multilinks configuration on Inverted Equilibrium Manifold (IEM), while keeping balance with a small location drift on the floor. First of all, the general form of dynamics equation of planar Articulated Wheeled Inverted Pendulum (AWIP) with a wheel and n − 1 rigid links, is derived. The Partial Feedback Linearization (PFL) combined with a Sliding Mode Control (SMC) is used to design the inverse dynamics controller of AWIP, while considering full dynamics terms. The well-known WLR model is used as a simple example of AWIP to accomplish the configuration transformation task. An optimization based configuration transformation algorithm is proposed to realize a comprehensive optimization of the shortest path in joint space and the minimum location drift of WLR on the floor. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation to implement the configuration transformation task.

We describe an end-to-end method for recovering 3D human body mesh from single images and monocular videos. Different from the existing methods try to obtain all the complex 3D pose, shape, and camera parameters from one coupling feature, we propose a skeleton-disentangling based framework, which divides this task into multi-level spatial and temporal granularity in a decoupling manner. In spatial, we propose an effective and pluggable “disentangling the skeleton from the details” (DSD) module. It reduces the complexity and decouples the skeleton, which lays a good foundation for temporal modeling. In temporal, the self-attention based temporal convolution network is proposed to efficiently exploit the short and long-term temporal cues. Furthermore, an unsupervised adversarial training strategy, temporal shuffles and order recovery, is designed to promote the learning of motion dynamics. The proposed method outperforms the state-of-the-art 3D human mesh recovery methods by 15.4% MPJPE and 23.8% PA-MPJPE on Human3.6M. State-of-the-art results are also achieved on the 3D pose in the wild (3DPW) dataset without any fine-tuning. Especially, ablation studies demonstrate that skeleton-disentangled representation is crucial for better temporal modeling and generalization.

The performance of traditional hydraulic robots is often limited by their hoses across moving joints or connecting hydraulic drive units, which would reduce their mobility and impede their ability to operate in complex environment. In response to this deficiency, this paper introduces the WLR-II (the second generation of wheel-legged robot), a novel hydraulic wheel-legged robot developed by using hose-less design approach which is focused on improving the reliability of the hydraulic system and perfecting the appearance of the robot. As its notable features, seven Hydraulic Hose-less Joints (HHJ) that include a pair of high and also low pressure oil pipes based on rotary seal, Cylinder-Valve-Skeleton (CVS) integration thighs and arms which are produced by subtractive manufacturing as well as oscillating cylinders driven by gear rack transmission are included. In addition to a description of its design, experimental characterizations of rough pavement adaptability and payload capability together with the achievement of the reliability of hydraulic system are also demonstrated. As a result, we confirmed effectiveness of the hose-less design by moving on the rugged ground, climbing slope, squatting with load, dragging and picking up a heavy load. To the authors' best knowledge, this is the first time that the design of a hose-less hydraulic wheel-legged robot has been presented.

Wheel-legged hybrid robot with multi-modal locomotion can efficiently adapt to different terrain environments, as well as realize rapid maneuver on flat ground. We have developed a novel hydraulic wheel-legged robot (WLR) combined with a humanoid structural design. This robot can assist to emergency scenarios where the high mobility, adaptability and robustness are required. The paper introduces the details of the WLR, highlighting the innovative design and optimization of physical construction which is considered to maximize the mobile abilities, enhance the environmental adaptability and improve the reliability of hydraulic system. Firstly, maximizing the mobile abilities includes optimizing the configuration of each actuator and integrating them with the structure, so as to achieve a large range of movement and also reduce the mass and inertia of the legs. Secondly, the environmental adaptability can be ensured with a magnetorheological (MR) fluid-based damper and direct-drive wheels. Thirdly, improving the reliability of hydraulic system involves using the selective laser melting (SLM) technology to integrate hydraulic system and reducing the number of exposed tubes. The maneuverability of the WLR is demonstrated with a series of experiments. At present, the WLR can perform the following operations, including moving on the flat ground, squatting, and picking up a heavy load.

According to the characteristics of airfield and harbor from remote sensing images, a method of large target recognition based on the combination of target region and shape features is presented. First, edge detection and improved Hough transform are used to select line segments, the region including regular-array line segments in image is considered as region of interesting (ROI). ROI detection is the base for recognition. Target geometry shape is extracted from ROI using optimum threshold segmentation, which removes location effect and improves efficiency. As calculating shape principal orientations, all shapes are rotated to the same horizontally right to avoid rotation effect. The features extracted from shape implement multi-levels representation with moment features, normalized moment of inertia, length-width ratio and compact ration. Finally, feature vectors are normalized to measure similarity between target and template. Experiments show that target regions can be located accurately using ROI detection and it is effective for target recognition. Besides, the extracted features have good invariability with respect to rotation, translation and scaling, and they comprise local and overall consistency of the target, therefore, the recognition results meet expectations well.

In order to offer a method for the rehabilitation of injured fingers and a means of quantitative detection and evaluation, an exoskeleton based continuous passive motion (CPM) machine is presented in this paper. Corresponding to each finger of human hand, the CPM machine has 4 degrees of freedom (DOF) driven by two DC motors. The joint force and position sensors are all integrated into the machine. The device can be easily attached and also be adjusted to fit different hand sizes. During the injured fingerpsilas flexion and extension motion the machine can always exert perpendicular forces on the finger phalanges, meanwhile it can achieve the precise control of scope, force and speed of the moving fingers. In order to control the CPM machine, we have also designed an embedded control system based on S3C2410 (a kind of 32-bit RISC microprocessor). The whole system is open-ended for new functions and applications. The function modularization method provides a new thinking of design for the control system.

In order to control the continuous passive motion (CPM) machine for injured fingers, we develop an embedded control platform. We first bring forward the philosophy of function modularization design for control platforms. Then we actually begin to develop an embedded control platform of the CPM machine by using the method of function modularization. The core of the control platform consists of two main parts: the data acquisition function module and the motor control function module, both are based on the serial peripheral interface (SPI) network. The whole control platform is open-ended for new functions and applications. It can be easily expanded if we add new modules to the SPI network. Primary experiments have proved that the control platform works well and the design method of function modularization provides a new method for the design of control platforms

In this paper, a sensor-based motion planning method for robot arm manipulators operating among unknown obstacles of arbitrary shape is presented. It can be applied to on-line collision avoidance with no prior knowledge of the obstacles. The sensitive skin is used to build a description of the robot’s surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The motion planning algorithm consists of three phases. In each phase, the point automation moves along a specified plane, on which the mapping of the obstacles into configuration space can be simplified. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of simulations.

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. The sensitive skin is used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path searching algorithm based on the square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of simulations.