Motion Algorithm Research Articles

High-precision deformation analysis of yingxian wooden pagoda based on UAV image and terrestrial LiDAR point cloud

The monitoring of wooden pagodas is a very important task in the restoration of wooden pagodas. Traditionally, this labor has always been carried out by surveying personnel, who manually check all parts of the pagoda, which not only consumes huge manpower, but also suffers from low efficiency and measurement errors. This article evaluates the feasibility of combining portable 3D light detection and ranging (LiDAR) scanning and unmanned aerial vehicle (UAV) photogrammetry to perform these inspection tasks easily and accurately. The wooden pagoda's exterior picture and inside point cloud are acquired using a UAV and a LiDAR scanner, respectively. We propose a feature−based global alignment method to register the site point cloud. The error equation of the column of observed values is utilized as the beginning value of the feature constraint for global leveling. The beam method leveling model solves the spatial transformation parameters and the unknown point leveling values. Then, the Structure from Motion (SfM) algorithm of computer vision is used to realize the fusion of the dense point cloud of the exterior of the wooden pagoda generated from multiple non−measured images by global optimization and the LiDAR point cloud of the interior of the wooden pagoda to obtain the complete point cloud of the wooden pagoda, which makes the deformation monitoring of the pagoda more detailed and comprehensive. After experimental verification, the overall registration accuracy of the Yingxian wooden pagoda reaches 0.006 m. Compared with the scanning point cloud data in 2018, the model is more accurate and complete. By analyzing and comparing the data of the second floor of the wooden pagoda, we knew that the inclination of a second bright layer and a second dark layer is still developing steadily. Overall, the western outer trough inclines thoughtfully, and the column frame slopes from southwest to northeast. Some internal columns showed a negative offset in 2020, and the deformation analysis of a single column was realized by comparing it with the standard column model. The main contribution of this method lies in the effective integration of UAV images and point cloud data to provide accurate data sources for good modeling. This research will provide theoretical and methodological support for the digital protection of architectural heritage and GIS data modeling. The analysis results can provide a scientific basis for the restoration scheme design.

Trajectory Planning and Collision Control of a Mobile Robot: A Penalty‐Based PSO Approach

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty‐based methodology. To deliver the best or collision‐free position to the robot, fitness values of the all‐random‐positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random‐positioned particles). The obstacle‐negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot′s best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real‐time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively.

Investigation of the effect of algorithms of positioning robots on their power and energy consumption

The paper is devoted to the study of motion algorithms that determine the power and energy consumption of process equipment and positioning robots, which are important factors affecting the environment and environmental safety of the technosphere. Based on the study of mathematical models of the degrees of mobility of the gantry robot drives, it is found that under the traditional laws of motion, a fairly significant peak in power consumption occurs at the end of acceleration. The paper shows that by separating in time the moments of the end of acceleration of different drives, it is possible to significantly reduce the maximum power of the robot. A comprehensive approach to the choice of an algorithm and laws of motion that provide an acceptable combination of energy consumption and maximum power consumption has been developed. The authors have shown that there are such laws of motion that ensure the constancy of power for the duration of the acceleration stage and its minimization. The relationship between the minimum instantaneous power and power consumption for such robots has been established. The results obtained can be used to optimize the operation algorithms of both of existing and newly +-robots.

Mobile Robot Motion Algorithm for Image Retrieval-based Localization in Nighttime

Mobile Robot Motion Algorithm for Image Retrieval-based Localization in Nighttime

Coil-globule transition in two-dimensional polymer chains in an explicit solvent.

The structure of two-dimensional polymer chains in a solvent at different temperatures is still far from being fully understood. Computer simulations of high-density macromolecular systems require the use of appropriate algorithms, and therefore the simulations were carried out using the Cooperative Motion Algorithm. The polymer model studied was exactly two-dimensional, coarse-grained and based on a triangular lattice. The theta temperature and temperature of coil-to-globule transition, and critical exponents were determined. The differences between the structure of such a disk and that of a chain in a dense polymer liquid were shown.

Asynchronous Motor in a Mild Hybrid Vehicle

Problem. Strengthening requirements for reducing CO2 emissions requires reducing fuel consumption in cars with internal combustion engines. One of the ways to solve this problem is to equip cars with a hybrid power plant that combines an internal combustion engine and an electric motor. A classic hybrid vehicle requires two energy sources: a fuel tank for the internal combustion engine and a power battery for the electric motor. Therefore, hybrid vehicle has a more complex design and a relatively high cost. A mild hybrid vehicle is a new concept of a hybrid vehicle for urban use that is emerging today. Such MHVs have a simpler design and also allow you to save up to 30% of fuel in urban driving mode. Goal. The purpose of the work is to improve the economic and environmental characteristics of a mild hybrid car, due to the use of an inexpensive asynchronous electric motor with frequency control in the scalar mode, as well as more effective use of power plant control system algorithms. Methodology. Analytical research methods were used to develop algorithms for effective use of the power plant in acceleration, uniform motion, regenerative braking, and stopping modes. Mathematical modeling and calculation methods were used to justify the use of an asynchronous motor for the power plant of a mild hybrid vehicle. Results. The use of a low-power asynchronous motor in the power plant of a mild hybrid vehicle is mathematically justified. The use of the principle of rational amplitude-frequency scalar control is proposed. An algorithm and a scheme for the implementation of an induction motor control system with support for the optimal power factor have been developed. Originality. Instead of using a valve synchronous electric motor with an expensive control system, it is proposed to use an asynchronous motor, which has advantages in the power plant of a mild hybrid vehicle. An asynchronous motor will allow the use of effective algorithms of uniform motion and regenerative braking modes. Practical value. The design of a soft hybrid vehicle with the use of a low-power asynchronous electric motor makes it efficient and cost-effective. The scalar control method of an asynchronous motor, with the slip control, makes it possible to obtain a system with high control quality and lower cost of implementation.

UAV Platforms and the SfM-MVS Approach in the 3D Surveys and Modelling: A Review in the Cultural Heritage Field

In recent years, structure from motion (SfM) and multi-view stereo (MVS) algorithms have been successfully applied to stereo images generated by cameras mounted on unmanned aerial vehicle (UAV) platforms to build 3D models. Indeed, the approach based on the combination of SfM-MVS and UAV-generated images allows for cost-effective acquisition, fast and automated processing, and detailed and accurate reconstruction of 3D models. As a consequence, this approach has become very popular for representation, management, and conservation in the field of cultural heritage (CH). Therefore, this review paper discusses the use of UAV photogrammetry in CH environments with a focus on state of the art trends and best practices in image acquisition technologies and 3D model-building software. In particular, this paper intends to emphasise the different techniques of image acquisition and processing in relation to the different platforms and navigation systems available, as well as to analyse and deepen the aspects of 3D reconstruction that efficiently describe the entire photogrammetric process, providing further insights for new applications in different fields, such as structural engineering and conservation and maintenance restoration of sites and structures belonging to the CH field.

A novel exponential degradation approach for predicting the remaining useful life of roadheader bearings

Based on the optimized exponential-degradation model (OEDM), a novel approach for predicting the remaining useful life(RUL) of roadheader bearings under different working conditions is proposed in this study. Specifically, the exponential process is used to construct the degradation process from a single performance characteristic under variable operating conditions, the generalized expectation maximization is employed to estimate model parameters, and the proposed degradation model is updated after new data is available. In the traditional exponential degradation method, the hyperparameters are only optimized, which leads to low calculation accuracy under severe working conditions. In the proposed method, the Bayesian algorithm and the Drift Brownian motion algorithm were respectively employed to optimize hyperparameters and stochastic parameters to ensure the high accuracy of the prediction results. In addition, degradation characteristics combined with sensory data acquired through condition monitoring were used to continuously update the RUL in the proposed degradation model. Finally, the effectiveness of the proposed model is verified by a simulation case and a case study. The results show that compared with the linear degradation model and the general exponential degradation model, the proposed OEDM performs well in practical applications and has a higher prediction accuracy. This study provides a reference for predictive maintenance of critical parts of tunneling machinery and cost reduction of tunneling.

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

Abstract Photogrammetric products obtained by processing data acquired with Unmanned Aerial Vehicles (UAVs) are used in many fields. Various structures are analysed, including roads. Many roads located in cities are characterised by heavy traffic. This makes it impossible to avoid the presence of cars in aerial photographs. However, they are not an integral part of the landscape, so their presence in the generated photogrammetric products is unnecessary. The occurrence of cars in the images may also lead to errors such as irregularities in digital elevation models (DEMs) in roadway areas and the blurring effect on orthophotomaps. The research aimed to improve the quality of photogrammetric products obtained with the Structure from Motion algorithm. To fulfil this objective, the Yolo v3 algorithm was used to automatically detect cars in the images. Neural network learning was performed using data from a different flight to ensure that the obtained detector could also be used in independent projects. The photogrammetric process was then carried out in two scenarios: with and without masks. The obtained results show that the automatic masking of cars in images is fast and allows for a significant increase in the quality of photogrammetric products such as DEMs and orthophotomaps.

New Security Concepts for Advanced Reactors

Advanced nuclear reactors have moved toward smaller, modular construction and will likely dominate the expansion of nuclear energy in the near future. These compact reactors need new physical protection approaches to reduce costs while still meeting regulatory requirements to allow nuclear to be competitive with competing sources of electricity. This paper presents new technologies and new physical protection approaches that can help optimize protection costs for new facilities. The deliberate motion algorithm for efficient intrusion detection and alternative response force strategies and options is presented.

Realistic Rendering Algorithm for Bubble Generation and Motion in Water

A simplified bubble model and its solver optimization method are proposed to solve the problem of poor realistic simulation and complex solutions for bubble-motion behavior in water. Firstly, the internal velocity of the bubble was avoided, and the bubble model was established by only considering the net flux of the inlet and outlet bubbles, which reduced the computational complexity. The bubble constraint was then introduced into the motion equation of water, and the mixed Euler–Lagrangian method was used to solve it. FLIP particles tracked the bubble position, velocity, and deformation, and the mesh updated the vector field. At the same time, the viscosity term was simplified. Finally, it was combined with implicit incompressible SPH particles to achieve the purpose of volume correction. The experimental results show that the method in this paper can present a simulation effect of bubbles in water with rich detail and a realistic sense, whether compared with actual pictures or with existing methods.

Abstract 13431: Comparative Evaluation of Diastolic Left Heart Dynamics Post-Mitral Valve Interventions

Introduction: Mitral valve (MV) leaflet morphology plays an important role insuring efficient, unidirectional blood flow from the left atrium (LA) to ventricle (LV). When treating severe mitral regurgitation (MR), surgical repair, valve replacement, or transcatheter edge-to-edge (E-2-E) repair all provide valid options depending on anatomy and clinical circumstances. These interventions alter the MV morphology, impacting LV filling dynamics. We used fluid-structure interactions (FSI) to evaluate LV filling dynamics after MV interventions, taking into consideration full left-heart (LH) dynamics. Methods: 3-D MV model of a patient suffering from severe MR was reconstructed from echocardiographic data. A LH wall motion algorithm was developed and applied to CT-derived LA and LV walls to realistically represent LH dynamics. LH wall motion, MV dynamics and blood flow in LA and LV during MV function was simulated using a strongly coupled fluid structure interaction approach. Virtual interventions using a surgical 33mm bioprosthetic valve (BPV) and transcatheter E-2-E repair were performed and evaluated. Results: Our FSI model predicted a physiological vortex ring developing in the LV for the pre-op MV during early diastole. BPV produced complex asymmetrical vortices. Due to its double orifice, the E-2-E clip produced two separate vortex rings with the largest velocity magnitude. Of all the three treatments, BPV had the greatest leaflet deformation rate. Conclusion: This model provides realistic quantitative insights into post-op filling dynamics that can be used to personalize and optimize valve intervention. These methodologies may help clinicians to realistically predict and evaluate patient-specific LV filling dynamics to tailor MV interventions.

Framework to Georeference Point Cloud in GPS Shadowed Urban Environment by Integrating Computer Vision Algorithms and Photogrammetric Techniques

The integration of computer vision algorithms and photogrammetric techniques has become an alternative to the high-cost Mobile Mapping Systems (MMS) and point cloud generation through Structure from Motion (SfM) algorithm is the best example of it. The point cloud generated using SfM is an arbitrary coordinate system and for its georeferencing known global coordinates of the camera exposure stations, rotational and translational parameters are required. The global coordinates of exposure stations are obtained through GNSS (Global Navigation Satellite System). GPS (Global Positioning System) is widely used for getting the positional information of a point. The problem in georeferencing the point cloud arises if the coordinates of a few camera exposure stations are unknown due to GPS shadowing or poor GDOP (Geometric Dilution of Precision). This issue is common in MMS that use laser scanners, GNSS and IMU (inertial measurement unit). In this paper, efforts are made to develop a methodology for handling GPS shadowing or poor accuracy for the georeferencing of arbitrary point clouds generated through SfM. The adopted method uses a blend of photogrammetric techniques of space resection and space intersection to determine the unknown camera exposure stations' coordinates. Bundle adjustment is applied to improve the accuracy of the results obtained. The developed methodology is well analyzed in different cases, and the results show good accuracy in respective cases.

A Novel Highly-Efficient Inexact Full Adder Cell for Motion and Edge Detection Systems of Image Processing in CNFET Technology

In this paper, a novel and highly efficient inexact Full Adder cell by exploiting two logic styles including conventional CMOS (C-COMS) and pass transistor logic (PTL) are presented. The so-called carbon nanotube field-effect transistor (CNFET) technology is used to implement circuits at the transistor level. To justify the efficiency of our design, extensive simulations are performed at the transistor level as well as application level. Transistor-level simulations which are carried out by the HSPICE 2008 tool, demonstrate at least 12% higher performance in terms of power-delay-area product (PDAP) of the proposed circuit compared to the latest designs. At the application level, by using the MATLAB tool, inexact Full Adders are employed in the structure of the ripple carry adder (RCA) that is applied in motion and edge detection algorithms. Computer simulation results confirm the appropriate quality of the output images in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) criteria. At last, to make a compromise between hardware and application level parameters, the power-delay-area-1/PSNR product (PDAPP) and power-delay-area-1/SSIM product (PDASP) are considered as figures of merit. The proposed circuit shows remarkable improvement from the PDAPP and PDASP points of view compared to its counterparts.

Development of Air Hockey Robot with High-Speed Vision and High-Speed Wrist

Our research group has developed an air hockey robot with high-speed vision capable of visual recognition at 500 Hz, which exceeds that of humans. Although this robot uses high-speed vision to increase its reaction speed and improve its performance, it has a low speed to compete with humans because its hand speed is still not sufficiently fast. In this study, a high-speed wrist rotation mechanism using a direct drive motor was introduced to the air hockey robot to improve the hitting speed through the snapping motion of the wrist. Furthermore, we developed a hitting motion algorithm that utilizes high-speed visual feedback. The effectiveness of the proposed system was verified experimentally. The proposed system exhibits an improved motion performance.

Unmanned aerial systems for modelling air pollution removal by urban greenery

Urban greenery plays an important role in reducing air pollution, being one of the often-used, nature-based measures in sustainable and climate-resilient urban development. However, when modelling its effect on air pollution removal by dry deposition, coarse and time-limited data on vegetation properties are often included, disregarding the high spatial and temporal heterogeneity in urban forest canopies. Here, we present a detailed, physics-based approach for modelling particulate matter (PM10) and tropospheric ozone (O3) removal by urban greenery on a small scale that eliminates these constraints. Our procedure combines a dense network of low-cost optical and electrochemical air pollution sensors, and a remote sensing method for greenery structure monitoring derived from Unmanned aerial systems (UAS) imagery processed by the Structure from Motion (SfM) algorithm. This approach enabled the quantification of species- and individual-specific air pollution removal rates by woody plants throughout the growing season, exploring the high spatial and temporal variability of modelled removal rates within an urban forest. The total PM10 and O3 removal rates ranged from 7.6 g m-2 (PM10) and 12.6 g m-2 (O3) for mature trees of Acer pseudoplatanus to 0.1 g m-2 and 0.1 g m-2 for newly planted tree saplings of Salix daphnoides. The present study demonstrates that UAS-SfM can detect differences in structures among and within canopies and by involving these characteristics, they can shift the modelling of air pollution removal towards a level of individual woody plants and beyond, enabling more realistic and accurate quantification of air pollution removal. Moreover, this approach can be similarly applied when modelling other ecosystem services provided by urban greenery.

Forest degradation in the Dry Chaco: A detection based on 3D canopy reconstruction from UAV-SfM techniques

Forest degradation is a gradual process through which the forest’s biomass declines, the species composition and vertical complexity change and the soil physicochemical properties degrade. Evaluating forest degradation is challenging, as it needs measurable indicators in order to compare different states of degraded forest with forests in optimal conditions. The UAV-SfM technique, combining imagery taken from unpiloted aerial vehicles (UAV) and Structure from Motion (SfM) algorithms, offers the opportunity to model forests in three dimensions and to derive ecological indicators describing forest conditions. The research objective of this paper is to evaluate to what extent information on forest degradation can be derived with UAV-SfM techniques.The Dry Chaco ecosystem in Argentina offers us the possibility to study the effects of a long history of anthropogenic disturbances on subtropical broadleaf dry forests. We surveyed a sample of 54 forest plots with different degradation histories with a UAV. Based on existing literature on forest ecology, two main degradation mechanisms were defined: loss of vertical integrity and vertical complexity. Multivariate statistical techniques were applied on structural indicators extracted from the 3D canopy model to classify the forest plots into groups or clusters with similar forest conditions. Next, the structural characteristics of the clusters were discussed in the light of a conceptual framework on forest degradation in the Dry Chaco. The results show that objective criteria derived from UAV-SfM 3D canopy models can be used to differentiate (1) forests in good condition from (2) forests that were subject to extensive processes of degradation, (3) forest with a dominance of shrubs and low trees, (4) forests intensively disturbed and (5) low vegetation dominated by bare soils with shrubs.

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study.

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

One-particle Green's functions from the quantum equation of motion algorithm

Many-body Green's functions encode all the properties and excitations of interacting electrons. While these are challenging to be evaluated accurately on a classical computer, recent efforts have been directed towards finding quantum algorithms that may provide a quantum advantage for this task, exploiting architectures that will become available in the near future. In this work we introduce a novel near-term quantum algorithm for computing one-particle Green's functions via their Lehmann representation. The method is based on a generalization of the quantum equation of motion algorithm that gives access to the charged excitations of the system. We demonstrate the validity of the present proposal by computing the Green's function of a two-site Fermi-Hubbard model on a IBM quantum processor.

Collision-Free Motion Algorithms for Sensors Automated Deployment to Enable a Smart Environmental Sensing-Net

As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds.