The Optimized Design of Soil-Touching Parts of a Greenhouse Humanoid Weeding Shovel Based on Strain Sensing and DEM-ADAMS Coupling Simulation.

To overcome the shortcomings of plowing and rotary tillage, a human-like weeding shoveling machine was designed. The machine's various moving rods were analyzed using Matlab R2019b(9.7.0.1190202) software to determine the appropriate entry and cutting conditions, as well as non-cutting conditions. It was concluded that a θ2 of 90° was optimal for cutting the soil and that the shoveling depth was suitable for greenhouse weeding. The Adams and DEM coupled discrete element simulation system was developed for this machine and was used to analyze the rotating shaft torque and shovel bending moment. A strain measurement system based on strain gauges was designed to measure the rotating shaft torque and shovel bar bending moment. A bending moment and torque measurement system was designed to perform field measurement tests for comparison with simulation results. The simulation system's rotating shaft had an average torque error of 6.26%, while the shovel rod's bending moment had an average error of 5.43%. The simulation accuracy was within the acceptable error range. Table U8 (81 × 44) of the Uniform Design of the Mixing Factor Level for the Homogeneous Virtual Simulation Test includes eight levels of forward machine speed ranging from 0.1 to 0.45 m/s and four levels of output shaft speed ranging from 90 to 165 r/min. Crank lengths were set at four levels ranging from 155 to 185 mm, while shovel lengths were set at four levels ranging from 185 to 230 mm. Four types of shovel shapes were proposed, including pointed curved shovels, pointed straight shovels, straight-edged curved shovels, and straight-edged straight shovels. A mathematical model was created via a regression analysis of the results of coupled simulation tests to establish the relationship between shaft torque and shovel rod bending moment, tool advance speed, shaft speed, crank length, tool length, and tool shape. The model was used to determine the optimum working parameters.

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings.

This article presents a developed motion control system for a robotic platform based on laser-ranging methods, a graph traversal algorithm and the search for the optimal path. The algorithm was implemented in an agricultural building and in the field. As a result, the most efficient algorithm for finding the optimal path (A*) for the robotic platform was chosen when performing various technological operations. In the Rviz visualization environment, a program code was developed for planning the movement path and setting the points of the movement trajectory in real time. To find the optimal navigation graph in an artificial garden, an application was developed using the C# programming language and Visual Studio 2019. The results of the experiments showed that field conditions can differ significantly from laboratory conditions, while the positioning accuracy is significantly lower. The statistical processing of the experimental data showed that, for the movement of a robotic platform along a given trajectory in the field, the most effective conditions are as follows: speed: 2.5 km/h; illumination: 109,600 lux; distance to the tree: 0.5 m. An analysis of the operating parameters of the LiDAR sensor showed that it provides a high degree of positioning accuracy under various lighting conditions at various speeds in the aisles of a garden 3 m wide with an inter-stem distance of 1.5 m and a tree crown width of 0.5 m. The use of sensors-rangefinders of the optical range-allows for the performance of positional movements of the robotic platform and ensures the autonomous performance of the basic technological operations of the units in intensive gardens with a deviation from the specified trajectory of no more than 8.4 cm, which meets the agrotechnical requirements.

Experimental Research of High-Quality Drilling Based on Ultrasonic Vibration-Assisted Machining.

Micro-hole is widely used in various fields, and common machining methods of micro-hole in factories are drilling and electrical discharge machining (EDM), but because of low machining efficiency, these methods cannot meet requirements. Therefore, it is urgent to investigate a high efficient micro-hole machining technology to meet the demands of micro-hole machining in factory. Over past few decades, ultrasonic machining technology has developed rapidly and achieved good results in solving many critical machining problems in field of difficult-to-cut materials. Therefore, this paper builds an ultrasonic vibration-assisted drilling (UAD) experiments platform to combine micro-fine small hole drilling with ultrasonic machining technology for micro-hole multi-factor experimental research. Results show that UAD machining of micro-hole below diameter 0.5 mm is comparable to conventional drilling machining because of its high-frequency pulse intermittent cutting process, stable change in machining diameter, good stability of parameters such as shape tolerance roundness and cylindricity, small cutting force during cutting, small tool wear, and small surface roughness of inner wall of micro-hole. Compared with EDM, UAD has high efficiency and good stability of parameters such as diameter and roundness of shape tolerance. Comprehensive analysis of UAD can be used as an alternative technology solution for machining small holes in non-special requirements of metal materials in factory and has technical feasibility of stable batch production.

Research on Accurate Motion Trajectory Control Method of Four-Wheel Steering AGV Based on Stanley-PID Control.

With the continuous progress and application of robotics technology, the importance of mobile robots capable of adapting to specialized work environments is gaining prominence. Among them, achieving precise and stable control of AGVs (Automated Guided Vehicles) stands as a paramount task propelling the advancement of mobile robotics. Consequently, this study devises a control system that enables AGVs to attain stable and accurate motion through equipment connection and debugging, kinematic modeling of the four-wheel steering AGV, and a selection and comparative analysis of motion control algorithms. The effectiveness of the Stanley-PID control algorithm in guiding the motion of a four-wheel steering AGV is validated through MATLAB 2021a simulation software. The simulation results illustrate the outstanding stability and precise control capabilities of the Stanley-PID algorithm.

Subgraph Learning for Topological Geolocalization with Graph Neural Networks.

One of the challenges of spatial cognition, such as self-localization and navigation, is to develop an efficient learning approach capable of mimicking human ability. This paper proposes a novel approach for topological geolocalization on the map using motion trajectory and graph neural networks. Specifically, our learning method learns an embedding of the motion trajectory encoded as a path subgraph where the node and edge represent turning direction and relative distance information by training a graph neural network. We formulate the subgraph learning as a multi-class classification problem in which the output node IDs are interpreted as the object's location on the map. After training using three map datasets with small, medium, and large sizes, the node localization tests on simulated trajectories generated from the map show 93.61%, 95.33%, and 87.50% accuracy, respectively. We also demonstrate similar accuracy for our approach on actual trajectories generated by visual-inertial odometry. The key benefits of our approach are as follows: (1) we take advantage of the powerful graph-modeling ability of neural graph networks, (2) it only requires a map in the form of a 2D graph, and (3) it only requires an affordable sensor that generates relative motion trajectory.

PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior.

Objective.The evaluation of animals' motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate.Approach.Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function.Main results.PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill.Significance.The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals' behavior in challenging environments (e.g.Drosophila melanogasterand openfield-Pranav) with a high accuracy.

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network.

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent's attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the "feature store & return" module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics.

The short-term prediction of a person's trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton's laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human-robot interaction applications.

Motion and Load Analysis of the Flexible Platform Based on Noncontact Processing.

In this paper, we explore the applicability of the positioning stage based on flexible hinges for noncontact processing. According to the actual application of the positioning stage, Hooke's law, the Euler-Bernoulli beam theory, and the geometric relationship of the structure are applied to analyze the coupled displacement in the movement of the positioning stage and the changes in the performance of the positioning stage caused by external loads. The coupled-displacement matrix and the external-load matrix obtained from the analysis are substituted into the ideal-displacement expression of the positioning stage to obtain the displacement expression of the platform in noncontact machining. The platform trajectory obtained by the referenced curve is analyzed. In addition, the coupled displacement in the X- and Y-directions and the coupled displacement caused by the external load in the Z-direction are nanoscales and about one-thousandth of the output displacement, which meets the requirement of tracking accuracy for micron-level machining. Finally, we use finite element analysis (FEA) and experiments to prove the correctness of the theoretical analysis.

Robust motion trajectory optimization of overhead cranes based on polynomial chaos expansion.

In this paper, we present a polynomial chaos-based framework for the trajectory optimization of an overhead crane system under uncertainty. The main research described in this paper is as follows. First, the deterministic trajectory optimization problem formulation of a two-dimensional overhead crane model is constructed. Based on this basic mathematical formulation, the uncertainty trajectory optimization problem is formed considering the uncertainty of initial state and system parameter. Then, to solve the uncertainty trajectory optimization problem efficiently, a robust trajectory optimization problem formulation is proposed. However, it is difficult to solve the robust trajectory optimization problem directly because it contains stochastic function terms, such as stochastic dynamic equations, constraint functions and objective functions. We consider both the system state and control input as functions of uncertainty and use polynomial chaos expansion to quantify these stochastic functions. An augmented deterministic trajectory optimization problem which can be solved directly is finally obtained. Based on the proposed robust trajectory optimization formation, the motion trajectory optimization of an overhead crane system under two different uncertainty types of is solved. All simulation results are compared with traditional sampling-based Monte Carlo simulations to demonstrate the feasibility and effectiveness of the proposed method.

Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles.

Objective. Growing evidence suggests that electroencephalography (EEG) electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction, commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features.Approach. In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model.Main results. For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150, 116 and 84 ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person's correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r= 0.25 andr= 0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components.Significance. Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.

A Design of Assistive Device for Toilet Based on Sitting-to-stand Motion Trajectory / 中国康复理论与实践

Objective:To design a toilet assistant device according with an ergonomics based on the natural sit-to-stand transition motion trajectory, to improve the safety and comfort for the older adults. Methods:The sit-to-stand transition motion trajectory was obtained at different speeds using Inertial Measurement Unit from twelve healthy subjects. The finite element contrastive pressures on toilet seat at 0°, 10°, 20°, 30°, 40° sitting inclination angles were compared from this trajectory to the other research trajectory, as well as the balance analysis after standing. A total of 20 healthy subjects experienced the toilet assistant device and reported their satisfaction. Results:At four angles of inclination, the average pressure of this trajectory was less than that of the other research; while the vertical projection of center of mass was within the range of center of pressure, satisfying the balance condition. Most of the subjects were satisfactory to the toilet assistant device. Conclusion:A new sit-to-stand transition motion trajectory has been obtained. A toilet assistant device based on the motion trajectory has been designed, which is comfortable and steady.

Motion Trajectory of 3-PH/R Ankle Rehabilitation Robot / 医用生物力学

Objective To study the influence of different trajectories of 3-PH/R ankle rehabilitation robot on joints and muscles. Methods The 3-PH/R ankle rehabilitation robot was simplified and imported into biomechanical modeling software by analyzing the kinematics principles. Using the actual motion trajectory of ankle rehabilitation robot as model driving, the joint and muscle forces were compared under three different trajectories, namely, dorsiflexion/plantarflexion, inversion/eversion and nutation. The correlation analysis on three motion trajectories was conducted. Results Nutation could satisfy the function of both plantar dorsiflexion/plantarflexion, and inversion/eversion, and made the ankle muscles fully exercised. The maximum difference in joint force under three different rehabilitation trajectories was 0.3 N. Different muscles had different sensitivity to trajectories. Conclusions The continuous dynamic analysis of muscle force and joint force under three kinds of rehabilitation trajectories was implemented. The results have certain theoretical significance and clinical reference value for the clinical application of ankle rehabilitation robot and the formulation of rehabilitation trajectory.

Wearable Wrist Movement Monitoring Using Dual Surface-Treated Plastic Optical Fibers.

Regarding high-sensitivity human wrist joint motion monitoring in exercise rehabilitation; we develop a pair of novel wearable and sensitivity-enhanced plastic optical fiber (POF) strain sensors consisting of an etched grating fiber and a side-polished fiber stitched into a polyamide wrist brace. The two flexible and surface-treated fibers are; respectively; featured with an etched periodic gratings with a pitch of 6 mm and a depth of 0.5 mm and a D-shaped side-polished zone of ~300 µm depth and ~30 mm length; which, correspondingly, show the sensitivities of around 0.0176/° and 0.0167/° in a normalized bending angle by far larger than a conventional commercial POF, because it achieves a more sensitive strain-induced evanescent field interaction with the side-machined fibers. Moreover, in terms of the sensor response to bending deformation in the range of -40°~+40°, the former exhibits a better sensitivity in lower angle change, while the latter is superior as the bending angle increases; thereby arranging the two modified POFs separately at the side and back of the human wrist, in order to decouple the wrist joint behaviors induced by typical flexion-extension or abduction-adduction movements. Then, the circular and pentagonal wrist motion trajectory patterns are investigated, to demonstrate the maximum average single-axis motion error of 2.94° via the transformation of spatial angle to plane coordinate for the fabricated couple of POF sensors, which is lower than a recognized standard of 5°, thus suggesting the great potential in wearable exercise rehabilitation of human joints in the field of medical treatment and healing.

Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

Human-centered artificial intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities, or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, the interclass and intraclass spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model, which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as their transitioning over time from one state to another. Then, the assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated for both recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human-Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.

Erratum: Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

[This corrects the article DOI: 10.3389/frobt.2020.00080.].

Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study.

Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

Unsupervised Moving Object Segmentation from Stationary or Moving Camera based on Multi-frame Homography Constraints.

Moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the designed cumulative acknowledgment strategy. (2) In the second stage, a super-pixel-based Markov Random Fields model is used to refine the spatial accuracy of initial segmentation and obtain final pixel level labeling, which has integrated trajectory classification information, a dynamic appearance model, and spatial temporal cues. The proposed method overcomes the limitations of existing object segmentation algorithms and resolves the difference between stationary and moving cameras. The algorithm is tested on several challenging open datasets. Experiments show that the proposed method presents significant performance improvement over state-of-the-art techniques quantitatively and qualitatively.

Analysis of natural finger-press motions for design of trackball buttons.

This study analysed natural press motions of the index, middle and ring fingers for ergonomic design of the positions and surface angles of the left, middle and right trackball buttons. Finger motions of 26 male participants for naturally pressing the trackball buttons were recorded after the participants adjusted the trackball buttons to their preferred locations for comfortable pressing. The natural positions of the finger pulps formed a symmetrically rainbow-shaped reach zone for the fingers. The natural press angles of the fingers' motion trajectories to the vertical reference line ranged from 14.2° to 20.5°, suggesting an 18-degree surface from the horizontal line for the trackball buttons. Regression formulas (adjusted R2 = 0.90 ± 0.07 and mean squared error = 8.55 ± 7.52 mm) were established to estimate the natural positions of finger pulps from hand segment lengths and joint angles for a population having different hand sizes from this study. Relevance to industry.

Directional Bias for Vertical Integration of Motion Trajectories.

Here we report a new ambiguous continuous motion display, in which two objects appear at the diagonally opposite corners of an imaginary square, move along the diagonal axis toward each other, and after meeting in the center, shift their trajectories to the other two diagonal corners. This display can be seen as two objects' colliding and bouncing off each other, with two competing interpretations of trajectory configuration requiring either vertical or horizontal integration of trajectory segments. Despite the fact that both percepts are equally plausible, the current study revealed a perceptual preference toward a vertical integration interpretation. We compared this bias with the similar vertical bias in a bistable apparent motion quartet, which suggests that the directional anisotropy found here is quite a new, and distinct phenomenon in both its perceptual characteristics and underlying mechanism.