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Visual servoing (VS) control has seen wide adoption in harvesting robots. However, parameter calibration is cumbersome, which makes the use of VS robotic systems inconvenient. Besides, dynamic fruits usually lead to a degeneration of control while tracking. To overcome the drawbacks, we present a new image-based uncalibrated visual servoing (IBUVS) control approach, consisting of a hybrid visual configuration and an adaptive tracking controller, referred to as hybrid-IBUVS. Specifically, our hybrid-IBUVS employs an eye-in-hand camera and a fixed red–green–blue-depth camera to construct a hybrid VS system, basing on multiobject detection and edge-computing technologies. Meanwhile, we also propose adaptive laws to online estimate the uncalibrated parameters of the cameras and robot dynamics. Furthermore, our hybrid-IBUVS uses an adaptive tracking controller to guarantee the harvesting robot to track a predefined trajectory to approach a fruit target. By Lyapunov stability theory, asymptotic convergence of the proposed control scheme is rigorously proven. Experimental results demonstrate the effectiveness of the proposed scheme. All shown results supported the research claims. [ABSTRACT FROM AUTHOR]

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions. [ABSTRACT FROM AUTHOR]

Most realtime human pose estimation approaches are based on detecting joint positions. Using the detected joint positions, the yaw and pitch of the limbs can be computed. However, the roll along the limb, which is critical for application such as sports analysis and computer animation, cannot be computed as this axis of rotation remains unobserved. In this paper we therefore introduce orientation keypoints, a novel approach for estimating the full position and rotation of skeletal joints, using only single-frame RGB images. Inspired by how motion-capture systems use a set of point markers to estimate full bone rotations, our method uses virtual markers to generate sufficient information to accurately infer rotations with simple post processing. The rotation predictions improve upon the best reported mean error for joint angles by 48% and achieves 93% accuracy across 15 bone rotations. The method also improves the current state-of-the-art results for joint positions by 14% as measured by MPJPE on the principle dataset, and generalizes well to in-the-wild datasets. [ABSTRACT FROM AUTHOR]

In this article, event-triggered (ET) observers and ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ controllers are investigated for physically interconnected nonholonomic mechanical agents in harsh conditions, such as skidding, slipping, and dead-zone disturbances. The agents’ models are presented by strict-feedback nonlinear large-scale systems, but unlike the existing studies, unknown dynamics with feedback outputs are considered, and assumptions of polynomial-type nonlinearities for physical interconnection functions are relaxed. Initially, the observer of unmeasurable states via the outputs is designed. Then, ET augmented controllers are established to transform the physically interconnected system into isolated subsystems connected by a communication network. By utilizing the local information of each agent and adaptive dynamic programming (ADP), ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ optimal control laws and disturbance rejection laws are derived. The parameters of the ET observers and controllers are synchronously updated online by event-triggering mechanisms; thus, it reduces computational complexity and communication. It is guaranteed that the states in the closed dynamics are $L_{2}$ -gain bounded and the Zeno behavior is avoided. Additionally, the convergence of cost functions to the near-optimal values is accelerated by a concurrent learning technique, avoiding dependence on the online examination of the persistence of excitation conditions. Finally, the control performance of a nonholonomic multirobot system in a comparative simulation study shows that the proposed observers and controllers are effective. [ABSTRACT FROM AUTHOR]

Most realtime human pose estimation approaches are based on detecting joint positions. Using the detected joint positions, the yaw and pitch of the limbs can be computed. However, the roll along the limb, which is critical for application such as sports analysis and computer animation, cannot be computed as this axis of rotation remains unobserved. In this paper we therefore introduce orientation keypoints, a novel approach for estimating the full position and rotation of skeletal joints, using only single-frame RGB images. Inspired by how motion-capture systems use a set of point markers to estimate full bone rotations, our method uses virtual markers to generate sufficient information to accurately infer rotations with simple post processing. The rotation predictions improve upon the best reported mean error for joint angles by 48% and achieves 93% accuracy across 15 bone rotations. The method also improves the current state-of-the-art results for joint positions by 14% as measured by MPJPE on the principle dataset, and generalizes well to in-the-wild datasets. [ABSTRACT FROM AUTHOR]

This article presents a redundant control strategy of fully-actuated underwater vehicles. The proposed strategy offers vehicles more control flexibility for addressing the position tracking problem under the attitudes and velocities constraints. In kinematics, position tracking is formulated as a convex optimization problem, where the velocity constraints are considered as a feasible region and the attitudes are bounded via a barrier function. Then, a conservative boundary is determined in the context of stability analysis of the dynamic controller with a disturbance observer and an auxiliary system so that kinematic variables lie in the constraints even with the velocity tracking error. Also, it is proven that the position tracking error can converge to a neighborhood about zero, which can be made arbitrarily small. The simulations and experimental results through multiple scenarios have illustrated the efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications. [ABSTRACT FROM AUTHOR]

Objective: The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington’s disease (HD) chorea and Parkinson’s disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance. Methods: Twenty subjects with HD and ten subjects with PD stood with both upper limbs in front of them for approximately 60 seconds. The three-dimensional velocity time-series of involuntary movements of both hands were segmented into one-dimensional sub-movements abutted by velocity zero-crossings. A combination of unsupervised and supervised machine learning algorithms was employed to automatically select data features extracted from sub-movements and distinguish the two types of involuntary choreic movements. Results: The trained model was able to accurately classify chorea vs. LID with an Area Under the Receiver Operating Characteristic Curve of 99.5%. A set of important features contributing to the construction of the classification model were identified and investigated. Conclusion: The trained model may serve as a tool for the automatic identification of different types of involuntary choreic movements, enabling continuous monitoring and personalized treatment for patients in various clinical settings. Significance: The results provide insights into kinematic characteristics of HD chorea and PD LID, which is the first step towards an improved general understanding of involuntary choreic movements. [ABSTRACT FROM AUTHOR]

Objective: Rheumatoid arthritis (RA) is a chronic inflammatory syndrome that features painful and destructive joint disease. Aggressive disease-modifying treatment can result in reduced symptoms and protection from irreversible joint damage; however, assessment of treatment efficacy is currently based largely on subjective measures of patient and physician impressions. In this work, we address this compelling need to provide an accurate and quantitative capability for monitoring joint health in patients with RA. Methods: Joint acoustic emissions (JAEs), electrical bioimpedance (EBI), and kinematics were measured noninvasively from 11 patients with RA over the course of three weeks using a custom multimodal sensing brace, resulting in 49 visits with JAE recordings and 43 with EBI recordings. Features derived from all sensing modalities were fed into a linear discriminant analysis (LDA) model to predict disease activity according to the validated disease activity index (the DAS28-ESR). Erythrocyte sedimentation rate (ESR) was predicted using ridge regression and classified into a high or low class using LDA. Results: DAS28-ESR level was predicted with an area under the receiver operating characteristic curve (AUC) of 0.82. With JAEs alone, we were able to track intrasubject differences in the disease activity score as well as classify ESR level with an AUC of 0.93. The majority of patients reported both an interest and ability to use the brace at home for longitudinal monitoring. Conclusion: This work demonstrates the ability to detect RA disease activity using noninvasive sensing. Significance: This system has the potential to improve RA disease activity monitoring by giving treating clinicians objective data that can be acquired independent of a face-to-face clinic visit. [ABSTRACT FROM AUTHOR]

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to acquire the position of bronchoscopy, vision-based localization approaches are clinically preferable but are sensitive to visual variations. The static nature of pre-operative planning makes mapping of intraoperative anatomical features challenging for learning-based methods using visual features alone. In this work, we propose a robust navigation framework based on Vision Kinematic Interaction (VKI) for monocular bronchoscopic videos. To address visual-imbalance between the virtual and real views of bronchoscopy images, a Visual Similarity Network (VSN) is proposed to extract domain-invariant features to represent the lumen structure from endoscopic views, as well as domain-specific features to characterize the surface texture and visual artefacts. To improve the robustness of online estimation of camera pose, we also introduce a Kinematic Refinement Network (KRN) that allows progressive refinement of camera pose estimation based on network prediction and robot control signals. The accuracy of camera localization is validated on phantom and porcine lung datasets from a robotically controlled endobronchial intervention system, with both quantitative and qualitative results demonstrating the performance of the techniques. Results show that the features extracted by the proposed method can preserve the structural information of small airways in the presence of large visual variations along with the much-improved camera localization accuracy. The absolute trajectory errors (ATE) on phantom data and porcine data are 8.01 mm and 8.62 mm respectively. [ABSTRACT FROM AUTHOR]

In this article, a practical three-dimensional (3-D) guidance is designed to achieve trajectory constrained flight, i.e., arriving at the destination with desired approach angles under limited field-of-view (FOV) and varying velocity. For this goal, the nonlinear engagement model is first transformed to a set of new differential equations in terms of the line-of-sight (LOS) angles. Then, the approach angle constraints are handled by constructing two reference range profiles as cubic polynomials of LOS angles. By solving the unknown coefficients involved in the polynomials, a semianalytical 3-D guidance solution is derived from the second-order range dynamics. To meet the FOV limit and avoid the initial guidance saturation, the achievable approach angle set is determined for capturability analysis by introducing the look angle dynamics with respect to the range profiles. The technique does not involve the model linearization, guidance switching logic, constant velocity assumption, and LOS rate information. Notably, only the LOS angle measurement is required during the guidance process once the initial conditions are provided, which makes it preferable for vehicles equipped with passive angles-only sensors. Extensive numerical simulations and Monte Carlo test are conducted to validate effectiveness and robustness of the proposed technique. [ABSTRACT FROM AUTHOR]

Four-wheeled steerable mobile robots have been widely used in industrial fields. However, robust tracking control is hard to achieve with a fixed maneuver mode. Based on a novel internal coupled sliding mode control (ICSMC) method, this study handles the above challenge by constructing a mode-dependent switched mobile robot (MSMR), and then designing an asynchronous $H_{\infty}$ continuous stabilization control scheme. This method governs the system behaviors to perform adaptive-mode allocations asynchronously and smoothly. First, a unified expression of MSMR is formulated, which regards the maneuver modes as optional subsystems. Then, with the coupled sliding surfaces and modified reaching law, an enhanced chattering-free ICSMC technique is established to optimize continuous control inputs for each subsystem. Moreover, using a surveillant criterion that depicts the energy decaying rate, an evaluation rule is built to distinguish the mismatched subsystems and then realize autonomous mode switching for the concerned MSMR. Utilizing piecewise Lyapunov functional technique and mode-dependent average dwell time, new sufficient conditions are derived for global exponential stability and $H_{\infty}$ performance. Comparative experiments implemented on the developed MSMR are carried out to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article addresses cooperative path-following problem of autonomous surface vehicles (ASVs) over a wireless network with constrained resources. A network-based resource-aware control architecture is presented to avoid unnecessary communications, computations, and actuations. Specifically, an event-triggered cooperative control law is designed to deal with the limited network bandwidth. Each ASV broadcasts its cooperation variable to its neighboring ASVs based on predefined event conditions under directed communication links. Then, an event-triggered path-following control law is proposed based on a line-of-sight approach. An event-triggered extended state observer is developed to estimate the uncertainties and external disturbances without periodic estimation. Finally, an event-triggered kinetic control law with aperiodic actuation is developed. The input-to-state stability of the closed-loop path-following system is analyzed by employing cascade theory, and Zeno behavior is excluded. A salient feature of the proposed network-based resource-aware control architecture is that cooperative path following can be achieved with low network traffic and reduced computation. Experiment results are given to illustrate the effectiveness of the proposed network-based resource-aware control architecture for multiple ASVs by using aperiodic communications, estimations, and executions. [ABSTRACT FROM AUTHOR]

Automated train control improves railroad operation by safeguarding the motion of trains while increasing efficiency by enabling motion within a safe envelope. Train controllers decide when to slow trains down to avoid collisions with other trains on the track, stay inside movement authorities, and navigate slopes, curves, and tunnels safely. These systems must base their decisions on detailed motion models to guarantee the absence of overshoot of the movement authority (safety) and limit undershoot (efficiency). This article is the first to formally verify the safety of the Federal Railroad Administration freight train kinematics model with all its relevant forces and parameters, including track slope and curvature, air brake propagation, and resistive forces as computed by the Davis equation. Due to the significant competing influence of these parameters on train stopping distances, even designing train controllers is a nontrivial control challenge, which we solve using formal verification. For increased generality at reduced verification effort, we verify symbolic mathematical generalizations of the train control models and subsequently apply efficient uniform substitutions to obtain verification results for physical train control models. [ABSTRACT FROM AUTHOR]

In this article, a simultaneous learning and control scheme built on the joint velocity level with physical constraints on the decision variable and its derivative, i.e., joint angle, joint velocity, and joint acceleration constraints, is proposed for the redundant manipulator control. The scheme works when the structure parameters involved in the forward kinematics are unknown or implicit. The learning and control parts are incorporated simultaneously in the scheme, which is finally formulated as a quadratic programming problem solved by a devised recurrent neural network (RNN). The convergences of learning and control abilities of the RNN are proved theoretically. Simulations and physical experiments on a 7-degrees of freedom (DOFs) redundant manipulator show that, aided with the proposed scheme and the related RNN solver, a redundant manipulator with unknown structure parameters can perform a given inverse kinematics task with high accuracy while satisfying physical constraints on the decision variable and its derivative. [ABSTRACT FROM AUTHOR]

Mobile soft continuum manipulator (MSCM) is more and more used in various applications of everyday life, such as logistics, farming, medical diagnosis, medical therapy, bakery, human collaboration, etc. However, the control of its shape remains a major challenge, especially when it is navigating in an unstructured environment. In this contribution, a kinematic-model-based shape control is designed in the space configuration for an MSCM, allowing its autonomous navigation in the presence of obstacles. For that purpose, the shape kinematic modeling is realized using a parametric spatial Pythagorean hodograph (PH) curve with a predefined length, whereas the artificial potential field algorithm acts on the control points of the PH curve for shape adaptation in presence of obstacles. The novelty of the proposed work resides in the use of parametric curve formalism to model the shape of an infinite degree of freedom mobile soft manipulator robot and its kinematic control by optimizing its bending potential energy during its motion. The shape optimization based on a minimal bending energy during the collision-free path is respecting a sliding mode strategy applied to the PH curve control points. Experimental results are obtained using a class of MSCM called RobotinoXT. These experiments show the advantages of using a reduced kinematic model based on PH curve to control the MSCM shape in dynamic motion. [ABSTRACT FROM AUTHOR]

Natural ester insulating oil not only has a biodegradation rate of almost 100% but also meets the carbon emission requirements of China’s “Carbon Peak and Carbon Neutrality” and the “European Green Deal” proposed by the European Union. It is considered to be a good substitute for mineral insulating oil. However, due to its higher kinematic viscosity than traditional mineral oil in low-temperature environments, it limits the safety promotion and application of transformers in cold regions. First, we design an experiment to test the kinematic viscosity and density of natural ester insulating oil. Under extremely harsh experimental conditions, we measure key experimental data such as kinematic viscosity. Second, the structure of the four main triglyceride molecules is optimized, and the molecular dynamics (MD) simulation technology is used to establish an MD model that can predict the kinematic viscosity of natural ester insulating oil (−20 °C to 20 °C). Finally, the model is further simplified by the free volume theory to better predict the kinematic viscosity of natural ester insulating oil. This study makes up for the lack of laboratory low-temperature testing of the kinematic viscosity of natural ester insulating oil and provides a convenient and reliable tool for predicting the kinematic viscosity of natural ester insulating oil. It can not only predict the natural ester insulating oil mixed in various proportions but also predict the kinematic viscosity of a certain natural ester molecule. Furthermore, it also provides a guiding direction for the improvement of low-temperature kinematic viscosity of natural ester insulating oil, as well as a strong reference for predicting other properties of other substances. [ABSTRACT FROM AUTHOR]

In relation to automated 3D concrete construction printing, a structure design of a suitable robot manipulator is presented as well as analytical solutions of related kinematics. The proposed structure has a higher number of degrees of freedom which can significantly increase the dexterity of a robot end-effector carrying a printing head. It should perform a continual motion along complicated large-scale printing trajectories. The related robot kinematics is derived by a novel representation of working layers in the complex plane using exponential functions. It leads to an explicit derivation of kinematic equations. The theoretical results are presented by examples of motion trajectories with typical configurations of the proposed structure. Note to Practitioners—This paper deals with a design of suitable robot structures for 3D concrete printing. Such concept is determined for on-site printing of residential houses. This area is developing a lot, but without a systematic design of energy-efficient, space-fitting robotic arms. A suitable structure is proposed, including a kinematic description to determine joint coordinates necessary for robot motion control. To ensure continuous energy-balanced motion during a large-scale printing, the redundant links in the structure are also used. Next research tasks focus on trajectory optimisation in complex print profiles. [ABSTRACT FROM AUTHOR]

This article takes a step to provide humanoid robots with adaptive morphology abilities. We present a systematic approach for enabling robotic covers to morph their shape, with an overall size fitting the anthropometric dimensions of a humanoid robot. More precisely, we present a cover concept consisting of two main components: a skeleton, which is a repetition of a basic element called node, and a soft membrane, which encloses the cover and deforms with its motion. This article focuses on the cover skeleton and addresses the challenging problems of node design, system modeling, motor positioning, and control design of the morphing system. The cover modeling focuses on kinematics, and a systematic approach for defining the system kinematic constraints is presented. Then, we apply genetic algorithms to find the motor locations so that the morphing cover is fully actuated. Finally, we present control algorithms that allow the cover to morph into a time-varying shape. The entire approach is validated by performing kinematic simulations with four different covers of square dimensions and having $3\times 3$ , $4\times 8$ , $8\times 8$ , and $20\times 20$ nodes, respectively. For each cover, we apply the genetic algorithms to choose the motor locations and perform simulations for tracking a desired shape. The simulation results show that the presented approach ensures the covers to track a desired shape with good tracking performances. [ABSTRACT FROM AUTHOR]

This paper proposes a novel numerical inverse kinematics algorithm called the Quick Inverse Kinematics or QuIK method. The QuIK method is a third-order algorithm that uses both the first- and second-order derivative information to iteratively converge to a solution. Numerical inverse kinematics methods are readily implemented on any serial robot and do not rely on joint alignment. However, they typically are slower and less robust. The second-order derivative term allows the QuIK algorithm to converge more rapidly and more robustly than existing algorithms. A damped extension to the QuIK method is also proposed to increase reliability near singularities. The QuIK methods are tested in terms of evaluation speed, reliability, and singularity robustness against the Newton–Raphson method and several other modern algorithms. The proposed QuIK methods outperform all other tested algorithms in terms of speed and robustness, and have strong performance near singularities. The QuIK algorithms are proposed as faster and more robust “drop-in” replacements to the Newton–Raphson methods in inverse kinematics. C++ and MATLAB codebases are made available. [ABSTRACT FROM AUTHOR]

In minimally invasive robotic surgery, the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a remote center of motion (RCM). Serial-link manipulators can be used as macro robots on which microsurgical robotic instruments are mounted to increase the number of degrees of freedom of the system and ensure safe task and RCM motion execution. However, the surgical instrument needs to be placed in an appropriate configuration when completing the motion tasks. The contribution of this article is to present a novel framework that preoperatively identifies the best base configuration, in terms of Roll, Pitch, and Yaw angles, of the microsurgical instrument with respect to the macro serial-link manipulator’s end effector in order to achieve the maximum accuracy and dexterity in performing specified tasks. The framework relies on hierarchical quadratic programming for the control, genetic algorithm for the optimization, and on a resilience to error strategy to make sure deviations from the optimum do not affect the system’s performance. Simulation results show that the mounting configuration of the surgical instrument significantly impacts the performance of the whole macro–micro manipulator in executing the desired motion tasks, and both the simulation and experimental results demonstrate that the proposed optimization method improves the overall performance. [ABSTRACT FROM AUTHOR]

Narrow passages in free space pose great challenges to many sampling-based motion planners. In problems with high-dimensional configuration spaces (C-spaces), narrow passages in free space (C-free) can be hard to find and navigate and, sometimes, even counterintuitive. In this article, we present an algorithm to construct cones of available instantaneous velocities (“free velocity cones” or, briefly, “free cones”) on the boundary of C-free to facilitate finding and navigating narrow passages. This is accomplished by first developing free cones and associated measures of local C-space narrowness for a single rigid link. These results are then extended to kinematic chains (open and closed) of arbitrary degrees of freedom. It turns out that the degeneracy of the free cones dictates the existence of C-space narrow passages, and the locations and orientations of the links in the workspace relate intimately to the corresponding connected component of C-free. This observation leads us to a modified probabilistic roadmap (M-PRM) algorithm and a modified rapidly exploring random vine (M-RRV) algorithm of combining the enumeration of topological components and random sampling of the free cones. Experimental results from applying our algorithms to several challenging examples show that our new algorithms are more efficient than some variants of the PRM algorithms, and several variants of the rapidly exploring random tree (RRT) algorithms, such as RRT-CONNECT and RRV. [ABSTRACT FROM AUTHOR]

Objective: Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). Methods: We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). Results: PC1 explained $>80\%$ variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. Conclusion: The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. Significance: The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM. [ABSTRACT FROM AUTHOR]

This article investigates the cooperative guidance problem for multiple missiles to intercept a maneuvering target. We propose an optimal encirclement guidance, which is known to be suitable for improving the observability and limiting the evasive probability of the target. By formulating the optimization problem in a reference frame, the optimal guidance solution can be obtained without any linearization and explicit cooperation can be ensured in regulating the relative line-of-sight angles to achieve encirclement. The main advantage of the proposed approach lies in its nonlinear nature and hence it is more tolerable to disadvantageous initial homing conditions than the linear laws. The working principles and characteristics of the encirclement guidance law are analyzed to support its application. Extensive numerical simulations are performed to validate the proposed approach and demonstrate its advantages. [ABSTRACT FROM AUTHOR]

This article develops a three-dimensional (3-D) impact angle control guidance law with the field-of-view (FOV) constraint. The impact error is defined as the difference between the predicted impact angle by proportional navigation guidance and the desired impact angle. The derived guidance command enables the impact angle error trajectory to follow a desired impact angle error dynamic. It can guarantee that the impact error converges to zero before the time of interception. Meanwhile, zero terminal guidance command is achieved, and the FOV constraint is always met during the guidance process. Furthermore, the trajectory of a guided missile is not affected by the variation of missile speed even under the FOV constraint, which reveals that the impact angle control performance is independent of time-varying missile speed. Numerical simulations are conducted to demonstrate the effectiveness of the proposed guidance law. [ABSTRACT FROM AUTHOR]

It is generally considered that controlling a robot precisely becomes tough on the condition of unknown structure information. Applying a data-driven approach to the robot control with the unknown structure implies a novel feasible research direction. Therefore, in this article, as a combination of the structural learning and robot control, an acceleration-level data-driven repetitive motion planning (DDRMP) scheme is proposed with the corresponding recurrent neural network (RNN) constructed. Then, theoretical analyses on the learning and control abilities are provided. Moreover, simulative experiments on employing the acceleration-level DDRMP scheme as well as the corresponding RNN to control a Sawyer robot and a Baxter robot with unknown structure information are performed. Accordingly, simulation results validate the feasibility of the proposed method and comparisons among the existing repetitive motion planning (RMP) schemes indicate the superiority of the proposed method. This work offers sufficient theoretical and simulative solutions for the acceleration-level redundancy problem of redundant robots with unknown structure and joint limits considered. [ABSTRACT FROM AUTHOR]

Aiming at the requirements for endoscope in minimally invasive surgery, a dual neural network (DNN) controller is designed for a flexible endoscopic robot (FER) with 10 DOF. First, the FER's kinematics model with remote center of motion (RCM) constraints is established. Then, a quadratic programming control scheme involving the tracking error, joint motion, and RCM constraints of the FER is proposed. A DNN solver of the control scheme, which is activated by the sum of linear and sign-bi-power activation function with adjustable parameters (LSB-AF-AP), is designed, and its convergence rate (CR) and antinoise ability can be improved via adjusting the parameters of LSB-AF-AP. The DNN can converge rapidly in finite time and it is proved by utilizing the Lyapunov theory. Compared with the previous linear AFs applied to the DNN, theoretical analysis indicates that the DNN activated by the LSB-AF-AP has an accelerated CR. Meanwhile, it is proved that the solution obtained by using the designed DNN is the optimal solution of the control scheme. Finally, simulations using the ROS and Gazebo, and an experiment performed on a real FER are conducted. The simulative and experimental results verify that the control scheme can complete the target tracking task well, and the CR and antinoise ability of the algorithm can be further improved via adjusting the parameters of LSB-AF-AP. [ABSTRACT FROM AUTHOR]

This article is based on an experimental study on the aerodynamic performance of a flapping wing rotor (FWR) and enhancement by passive pitching angle variation (PPAV) associated with powered flapping motion. The PPAV (in this article, 10o–50o) is realized by a specially designed sleeve-pin unit as part of a U-shape flapping mechanism. Through experiment and analysis, it is found that the average lift produced by an FWR of PPAV is >100% higher than the baseline model, the same FWR of a constant pitching angle 30o under the same input power. It is also noted that the lift–voltage relationship for the FWR of PPAV is almost linear and the aerodynamic efficiency is also over 100% higher than the baseline FWR when the input voltage is under 6 V. The aerodynamic lift or efficiency of the FWR of PPAV can be also increased significantly by reducing the weight of the wings. An FWR model is fabricated and achieved vertical take-off and free flight powered by 9 V input voltage. The mechanism of PPAV function provides a feasible solution for aerodynamic improvement of a bioinspired FWR and potential application to micro air vehicles. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) has been considered one of the most promising approaches to estimating high-resolution subsurface parameters, which takes advantage of the kinematics and dynamics information of seismic data. However, FWI is greatly dependent on the accuracy of the initial model and vulnerable to the issue of local minimum. Moreover, the multisource and multiparameter crosstalk artifacts make multisource elastic FWI (MS-EFWI) more likely to trap into a suboptimal inversion result. To remedy this defect, this study proposes an efficient EFWI paradigm that combines the crosstalk-free MS-EFWI method and a well-guided initial model-building algorithm. Specifically, we apply a harmonic wavelet encoding technology to MS-EFWI, by which the multisource wavefields can be completely deblended without crosstalk noise. The well-guided structure-oriented interpolation, with the aid of the dip information derived from the initial migration images, is designed to build a satisfactory initial model and, therefore, reduce the risk of cycle skipping. Numerical examples based on the 2-D overthrust model and the Marmousi model further demonstrate the feasibility and robustness of the proposed method with a relatively little number of iterations. [ABSTRACT FROM AUTHOR]

In this article, we investigate the adaptive visual tracking problem for uncertain robotic system with time-varying depth, in the case of no image-space velocity information. Existing works dealing with this problem either assume the estimated time-varying depth is invertible or require the priori information of the system kinematics. This case potentially leads to the singularity of the control scheme and requirement for a high gain feedback. In this brief, a new image-space observer which does not involve the inversion of the estimated depth is proposed, upon which, we design an adaptive visual tracking controller without using the image-space velocity information. More importantly, the observer employs a passive image Jacobian matrix-based constant feedback instead of high gain feedback and the priori information is no longer demanded. Additionally, the proposed control scheme enjoys a desirable separation property for the robotic dynamics and kinematics which makes it applicable to most industrial/commercial robots having a closed torque control loop and only permitting the joint velocity command. By using the Lyapunov stability analysis, the asymptotical convergence of both image-space tracking errors and observation errors are obtained. Numerical simulations and implementation issues concerning the application to industrial/commercial robots are presented to verify the efficacy of the control scheme. [ABSTRACT FROM AUTHOR]

In this article, a composite controller based on an extended state observer (ESO) and sliding-mode control (SMC) is presented in order to deal with the angular speed fluctuation caused by the kinematic error of a harmonic reducer in the gimbal system of a control moment gyro. First, based on the analysis of the kinematic error derived from the relationship between the angular speed of the motor and that of the load, a second-order gimbal system model is established based on the Lagrange function and the Rayleigh dissipation function. Second, a three-order linear ESO is designed to observe the uncertain disturbances of the gimbal system, and the disturbances are rejected via the proposed composite controller combining the ESO with SMC. Third, we prove the overall system stability by using the Lyapunov approach and the final value theorem. Finally, both simulation and experimental results verify the effectiveness of the proposed method in attenuating gimbal system disturbances. [ABSTRACT FROM AUTHOR]

Robot end-effector tracking of a specific spatial path to fulfill certain requirements is a topic of great interest. First, considering both dynamic constraints and kinematic constraints, the planned velocity has characteristics of bounded high-order derivatives to ensure smooth motion. Second, to meet high-efficiency requirements, the feedrate of the end-effector should be planned within the best possible time. Although some studies have shown that they obtained the time-optimal solution by considering the second-order constraints, this is still an open problem for third-order constraints. Therefore, this article proposes an approximate time-optimal feedrate (ATOF) planning algorithm that considers third-order constraints. This algorithm is primarily based on the bidirectional scanning method, which includes two steps. First, we calculate the second-order time-optimal velocity (TOV2) curve under the third-order maximum velocity curve. Then, considering TOV2, we propose an ATOF algorithm that includes single-direction speed planning and speed curve smoothing. It is more practical for not only considering the Coulomb viscous friction and torque rate but also avoiding difficult calculations at the singular point and solves discontinuous joint-acceleration under second-order constraints. Finally, we design two sets of experiments to verify the higher calculation efficiency and effectiveness of the proposed ATOF algorithm. [ABSTRACT FROM AUTHOR]

This article presents a software platform tailored for prehensile manipulation planning named humanoid path planner. The platform implements an original way of modeling manipulation planning through a constraint graph that represents the numerical constraints that define the manipulation problem. We propose an extension of the RRT algorithm to manipulation planning that is able to solve a large variety of problems. We provide replicable experimental results via a docker image that readers may download to run the experimental results by themselves. [ABSTRACT FROM AUTHOR]

No proper norm exists to measure the distance between two object poses essentially because a general pose is defined by a rotation and a translation, and thus, it involves magnitudes with different units. As a means to solve this dimensional-inhomogeneity problem, the concept of characteristic length has been put forward in the area of kinematics. The idea consists of scaling translations according to this characteristic length and then approximating the corresponding displacement defining the object pose in ${\mathbb {R}}^3$ by a rotation in ${\mathbb {R}}^4$ , for which a norm exists. This article sheds new light on this kind of approximations which permits simplifying optimization problem whose cost functions involve translations and rotations simultaneously. A good example of this kind of problems is the pointcloud registration problem in which the optimal rotation and translation between two sets of corresponding 3-D point data, so that they are aligned/registered, have to be found. As a result, a simple closed-form formula for solving this problem is presented which is shown to be an attractive alternative to the previous approaches. [ABSTRACT FROM AUTHOR]

RF sensors have been recently proposed as a new modality for sign language processing technology. They are noncontact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. The following two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs. [ABSTRACT FROM AUTHOR]

As oil and gas exploration moves toward complicated geological environments, high-resolution and true-amplitude seismic imaging becomes increasingly important for detecting and evaluating hydrocarbon reservoirs. Traditional ray-based and wave-equation imaging methods can be considered as the adjoint operator of seismic forward modeling, which are difficult to produce high-quality images in complicated structures because of limited frequency band, unbalanced illumination, and irregular acquisition. Least-squares migration (LSM) generates an inverse solution for subsurface reflectivity model with high image resolution and balanced amplitudes. Previous studies on LSM mainly focused on the developments of theoretical and practical strategies, but few on error and uncertainty analysis. We present a quantitative analysis method to evaluate the errors of LSM results. The $\phi _{\mathrm{ data}}$ and $\psi _{\mathrm{ data}}$ functions are first computed based on the local similarity and $L_{2}$ -norm misfit between observed and synthetic data. They are used as data-domain kinematic and dynamic errors, respectively. Then, these local functions are mapped to the subsurface using a Kirchhoff-integral relation to calculate image-domain errors. Numerical examples for synthetic and field data demonstrate that as the iteration of LSM increases, the total kinematic and dynamic errors are reduced, and they vary in different locations. For low signal-to-noise-ratio (SNR) field data, LSM might enlarge image errors at large iterations because of the overfitting issue, and proper regularization is very important to facilitate the convergence of LSM to a good solution. [ABSTRACT FROM AUTHOR]

A cable-driven segmented manipulator (CDSM) has superior dexterity for operations in confined space due to its light-slender body and redundant degree of freedoms (DOFs). However, its inverse kinematics resolving and configuration planning are very challenging due to the complex structure and strict constraints. In this article, we propose a two-layer geometric iteration (TLGI) method for inverse kinematics resolving and configuration-constrained Cartesian path planning. The computation efficiency is largely improved and singularities are avoided. First, the end-effector attitude is decomposed into a direction vector and a rotation angle. The former and the end-effector position are combined into state variables of the inner layer, and the latter is treated separately as the state variable of the outer layer. Then, the TLGI method enables to rapidly reach the desired 6-DOF pose by two-layer iterations, i.e., the inner and outer loop iteration. Second, during the inner loop iteration, the CDSM is modeled as an equivalent articulated arm whose end-effector position and direction is the same as that of CDSM, but its links length and joint angles depend on the current configuration of CDSM. Then, the efficient forward and backward reaching inverse kinematics (FABRIKs) method is extended to apply on CDSM so that it can fast reach the inner state variables. During the outer loop iteration, three different rotation cases, i.e., the rotating around the end, root, and both end and root, are designed to switch automatically to reach the outer state variable iteratively. Moreover, by parameterizing geometric constraints of the environment, a TLGI-based configuration-pose simultaneous planning method is also put forward to efficiently achieve additional configuration constraints for operations of CDSM in confined space. Finally, the proposed method is verified by both the simulations and experiments. [ABSTRACT FROM AUTHOR]

This article presents redundancy resolution and disturbance rejection via torque optimization in hybrid cable-driven robots (HCDRs). To begin with, we present a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: 1) joint-space torque optimization for actuated joints (TOAJ) and 2) joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this article provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this article develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ. The results show the effectiveness of the aforementioned approaches. [ABSTRACT FROM AUTHOR]

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method. [ABSTRACT FROM AUTHOR]

Cable-driven parallel mechanism has been widely studied due to its advantages of fast response and large workspace. The structural uncertainties in the system often introduce internal disturbance and unavoidably influence its motion precision. Generally, to reduce the influence, robust control is adopted and the boundary of the disturbance is regarded as a fixed value given by rough estimation. This method often causes large vibration of the motion component, which certainly compromises the dynamic performance of the system. Therefore, to solve this problem, a variable boundary analytical scheme based on the decoupling of control law and internal disturbance is proposed in this article. Specifically, sufficient condition for the solvability of disturbance boundary is proved by the boundedness of the structural matrix error. Then, the disturbance boundary is modeled analytically, so that its particular value can be determined with respect to the system status. Following that, a robust adaptive control algorithm based on the variable boundary of disturbance is developed. It is verified that the tracking error is globally uniformly bounded. The experimental results show that the proposed control method can effectively reduce the tracking errors and attenuate the tension chattering compared with the conventional robust adaptive control scheme with a roughly estimated boundary. [ABSTRACT FROM AUTHOR]

Human-robot collaboration is becoming increasingly popular in the manufacturing industry, opening the door to a large range of applications by combining the complementary skills of the human worker and the robot. Collaborative robots are also a solution to decrease the operator workload and indirectly reduce the risk of occupational injuries such as musculoskeletal disorders (MSDs). The latter represents one of the major causes of absenteeism at work. Thanks to the development of human tracking devices, it is possible to monitor the operator, analyze the postures, and assess the associated MSD risk. In this paper, we present a novel ergonomics optimization framework that performs postural optimization based on the virtual element method. A feedback interface is developed whereby the user is informed about non-ergonomic postures and an improved body pose is proposed. The workpiece position controller module acts on the cobot end-effector and indirectly on the co-manipulated part in such a way that the operator’s posture is improved. The framework was validated by a user study performed on a human-robot collaboration task whereby the subject polishes a part hold by the robot. The conducted study of the user’s perception and REBA scores showed promising results. Note to Practitioners—This paper is motivated by the problem of non-ergonomic posture of workers in hybrid workcells. The proposed approach makes use of virtual elements (springs and dampers) to build a mechanical model of the human body posture and perform postural optimization. The obtained body joint angles are fed into two modules of the framework. First, a graphical interface displays the current pose of the user and proposes to him the improved posture. Second, a controller adapts the pose of the workpiece hold by the collaborative robot. This is realized by computing a displacement vector between the wrist current and optimized positions. The use of such a framework was demonstrated on a collaborative polishing task whereby the robot adjusts the position of the workpiece. After a user study test with 10 participants, joint data were collected and the REBA scores of different subtasks were measured and compared. The results from these preliminary experiments showed that the proposed approach improves the human body postures and offers a promising solution to enhance ergonomics by the robot assistance in case of robotic workcells. The conducted survey also shows an overall positive subject’s perception of the system. [ABSTRACT FROM AUTHOR]

Although surface with multiple holes (SMHs) is widely used in industrial components, the precise inspection of SMH is a knotty task due to the inefficiency of traditional surface inspection technology. Recently, a five-axis surface sweep scanning approach is emerging and shows great potential to boost the surface inspection efficiency by sensing surface in a continuous sweep scanning way. However, an efficient sweep scanning path should cater to the unique kinematic characteristics of the five-axis inspection system. In this article, we present an approach to automatically generate efficient five-axis sweep scanning paths for inspecting SMH. First, the skeleton of SMH is extracted along with the surface partitioned into several patches, based on which a guiding curve-based sweep scanning path can be defined for each surface patch. By modeling the skeleton of SMH as a directed Euler graph and finding a proper sequence to traverse the graph, a continuous five-axis sweep scanning path is then generated to sweep all patches without any transition among them. The nonsweeping time could be eliminated so that the sweep scanning efficiency is drastically improved in this way. Simulation and physical inspection experiments are conducted on two SMHs, showing that our method significantly outperforms existing approaches, such as the popular zigzag method and the method from the leading commercial software of RENISHAW. Note to Practitioners—This article aims to generate an efficient sweep scanning path of a five-axis coordinate measuring machine (CMM) for inspecting the SMHs of a precision component, such as an engine block or a gearbox cover. Although traditional five-axis CMM path generation methods can be applied to SMH inspection, most of them are designed for the point-by-point inspection mode, which intrinsically suffers from extremely low efficiency. Recently, an emerging five-axis surface sweep scanning technology shows great potential for boosting the efficiency of surface inspection by using a continuous surface sensing stylus mounted on a rapid rotary probe head. The realization of high-speed five-axis surface scanning depends on a proper sweep scanning path which takes advantage of the superior kinematic performance of the rotary probe head as much as possible and accordingly avoids assigning heavy workload to the three linear axes with weaker kinematic performance. However, up to date, only a handful of five-axis sweep scanning path planning methods are reported and mainly focus on the simple and compact surface. Therefore, existing methods are incompetent to generate a suitable sweep scanning path for high-speed inspection of SMH. For these reasons, we present a novel and practical approach to automatically generate a continuous five-axis sweep scanning path for complex SMH. The generated path exactly meets the unique kinematic requirements of high-speed five-axis inspection and eliminates all noninspection operations. In this way, the SMH inspection can be accomplished with high efficiency. [ABSTRACT FROM AUTHOR]

For extrusion-based additive manufacturing, the variation in material deposition can significantly affect printed material distribution, causing infill nonuniformity and defects. These variations are induced by kinematic variations of the printer extruder. Such infill nonuniformity is more significant in an application of collaborative printing systems by which multiple printers’ extrudes co-create the same structure since more accelerate–decelerate kinematic cycles are involved. There is a lack of a quantitative understanding of the impact of printing kinematics on such variations to guide the printing process control. This article deals with the challenge by establishing a mathematical model that quantifies the printing width variations along the printing paths induced by printing speed and acceleration. The model provides vital information for predicting infill pattern nonuniformity and potentially enables using G-code adjustment to compensate for the infill errors in future research. In addition, since the model captures the mechanism of kinematics-induced variations, it provides a way of between-printer knowledge transfer on estimating printing errors. This article further proposes an informative-prior-based transfer learning algorithm to improve the quality prediction model for a printer with limited historical data by leveraging the shared data from interconnected 3-D printers. A case study based on experiments validated the effectiveness of the proposed methodology. Note to Practitioners—This article quantitatively studies the impact of extruder kinematics on geometric variations and printing quality in extrusion-based 3-D printing processes. The model can help predict the geometric printing quality and related defects, such as overfill or underfill problems given kinematics setup by G-code. This study can expedite the learning process of printing variations induced by kinematics for new printers to set up monitoring and G-code adjustment for process control in the early stage of production when the data are limited. In the long run, such between-printer transfer learning has the potential to enable the transfer learning for interconnected collaborative 3-D printing systems with improved printing efficiency and quality. [ABSTRACT FROM AUTHOR]

In this article, the role of the positrons shock waves generation in weakly relativistic laser-created plasma is investigated. The plasma considered is generated on the rear face of a target, irradiated by a high intensity short pulse laser, in the framework of target normal sheath acceleration (TNSA) mechanism. This plasma is composed of Boltzmann distributed positrons, hot and cold super-thermal electrons, and dynamic ions. Kinematic viscosity among the plasma constituents has been also considered. With the purpose of studying the behavior of nonlinear ion acoustic shock waves (IASHWs), the reductive perturbation method (RPM) has been employed and Korteweg-de Vries Burgers equation has been derived. It was observed that different plasma parameters (namely, electrons super-thermality, number of positrons, ion temperature, kinematic viscosity, etc.) have a real impact on the propagation of IASHWs. As electron–positron plasmas with ion possessing relativistic velocities are often detected in laboratory experiments, astrophysical, and space environments, the present work will help to explain the different types of collective processes and instabilities in regions where e-p-i plasmas can exist. [ABSTRACT FROM AUTHOR]

Automatically recognising surgical gestures from surgical data is an important building block of automated activity recognition and analytics, technical skill assessment, intra-operative assistance and eventually robotic automation. The complexity of articulated instrument trajectories and the inherent variability due to surgical style and patient anatomy make analysis and fine-grained segmentation of surgical motion patterns from robot kinematics alone very difficult. Surgical video provides crucial information from the surgical site with context for the kinematic data and the interaction between the instruments and tissue. Yet sensor fusion between the robot data and surgical video stream is non-trivial because the data have different frequency, dimensions and discriminative capability. In this paper, we integrate multimodal attention mechanisms in a two-stream temporal convolutional network to compute relevance scores and weight kinematic and visual feature representations dynamically in time, aiming to aid multimodal network training and achieve effective sensor fusion. We report the results of our system on the JIGSAWS benchmark dataset and on a new in vivo dataset of suturing segments from robotic prostatectomy procedures. Our results are promising and obtain multimodal prediction sequences with higher accuracy and better temporal structure than corresponding unimodal solutions. Visualization of attention scores also gives physically interpretable insights on network understanding of strengths and weaknesses of each sensor. [ABSTRACT FROM AUTHOR]

Geometry calibration is a critical problem in vision-based robot systems. The calibration objects of existing methods are limited to regular shapes. In this article, a general calibration method using an arbitrary free-form surface is proposed to simultaneously calibrate the geometry parameters. By incorporating a shape matching algorithm, each measured point on the surface can be regarded as a feature point to compare with the design model for a closed-form initial solution and an iterative fine solution. In the objective function of fine solution, the residual is described by the point-to-tangent distance, and the solution is proved to be Gaussian-Newton method with second-order convergence. The geometry and matching errors are iteratively compensated to improve the calibration accuracy. The characteristics of the method are large number of feature points, no need for specific features, no limitations on the size of the free-form surface and convenient robot pose control. Finally, simulations and experiments verify the availability of the proposed method in the presence of measuring noise, robot repeated positioning error, and a small number of robot poses. [ABSTRACT FROM AUTHOR]

Harmonic drive is usually installed in the gimbal system of single gimbal magnetic suspended control moment gyroscope (SGMSCMG) not only to meet the requirements on the volume and weight but also output large torque. However, its nonlinear transmission characteristics are also introduced into the gimbal system and make the output speed fluctuate, which will seriously affect the output torque accuracy of SGMSCMG. Torque feedback control is an effective technique for achieving high-performance control for the above-mentioned problem. Nevertheless, it is difficult to add torque sensors in the gimbal system. In order to obtain nonlinear transmission torque without torque sensors, a novel online modeling method based on the Lagrange equation is put forward. Besides, to restrain the main torque disturbance, the gear-wave torque model is extracted. According to the above-mentioned models, a model-based feed-forward control method with phase advance is presented. Plentiful simulation and experiments on an SGMSCMG are performed, and the results show that the load speed fluctuation can be effectively suppressed. [ABSTRACT FROM AUTHOR]

The intention-detection strategy used to drive an exosuit is fundamental to evaluate the effectiveness and acceptability of the device. Yet, current literature on wearable soft robotics lacks evidence on the comparative performance of different control approaches for online intention-detection. In the present work, we compare two different and complementary controllers on a wearable robotic suit, previously formulated and tested by our group; a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a force control that estimates human torques using an inverse dynamics model (dynamic arm). We test them on a cohort of healthy participants performing tasks replicating functional activities of daily living involving a wide range of dynamic movements. Our results suggest that both controllers are robust and effective in detecting human–motor interaction, and show comparable performance for augmenting muscular activity. In particular, the biceps brachii activity was reduced by up to 74% under the assistance of the dynamic arm and up to 47% under the myoprocessor, compared to a no-suit condition. However, the myoprocessor outperformed the dynamic arm in promptness and assistance during movements that involve high dynamics. The exosuit work normalized with respect to the overall work was $68.84 \pm 3.81\%$ when it was ran by the myoprocessor, compared to $45.29 \pm 7.71\%$ during the dynamic arm condition. The reliability and accuracy of motor intention detection strategies in wearable device is paramount for both the efficacy and acceptability of this technology. In this article, we offer a detailed analysis of the two most widely used control approaches, trying to highlight their intrinsic structural differences and to discuss their different and complementary performance. [ABSTRACT FROM AUTHOR]

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parameterized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this article, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parameterize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques and substantially outperforms them on problems that involve many workspace constraints. [ABSTRACT FROM AUTHOR]