3D Point Cloud Data Research Articles

3D point cloud recognition of substation equipment based on plane detection

Substation equipment identification is a key step in the process of intelligent substation designing. The target recognition of 3D point cloud of substation equipment firstly uses a 3D laser scanner to obtain the 3D point cloud data that can express the surface shape and spatial position of a substation device, then identify the device information by matching it with template point cloud data. Because the point cloud data obtained by 3D laser scanner is affected by equipment attitude and substation environment, there is redundant, missing, and shadow phenomena in the data, resulting in a low algorithm recognition rate. This paper proposes a method combining plane detection and point cloud registration algorithm, achieving substation equipment identification better result. The main steps of the method include: point cloud preprocessing, plane detecting, preliminary filtering based on Umeyama registration method, and Iterative Closest Point (ICP) algorithm identifying. Plane detection is used for the pre-processed point cloud to obtain planar features, then samples and the planar feature templates in template library are preliminarily selected by Umeyama registration method, and substation equipment is finally identified by Iterative Closest Point algorithm. Experiments show that the recognition rate of devices point cloud reaches 92.2% and the recognition time is shorter, which proves that this recognition algorithm has a good recognition effect.

Extraction of key regions of beef cattle based on bidirectional tomographic slice features from point cloud data

As the world population grows and the increase in food consumption creates unprecedented pressure on beef producers, innovative technologies for sustainable and efficient beef production is quintessential. Therefore, a significant effort is ongoing on developing digital precision technologies for continuous beef cattle monitoring and accurate cattle growth estimation. One such technology relies on 3D visual sensor technologies. 3D point cloud data acquired with 3D cameras can be processed to evaluate the livestock’s growth condition with minimal human intervention. Essentially, the automatic measurement of animal body dimensions highly depends on the location accuracy of the key points or regions in point clouds. However, the location of specific points and regions requires extra assistance and usually leads to semi-automatic and low-accuracy solutions at best. To address these issues, we demonstrated a novel strategy for partial segmentation of beef cattle point clouds, based on the Bidirectional Tomographic Slice Segmentation (BTSS) algorithm. The cattle point clouds can be successfully segmented into the head and neck, front trunk, middle trunk, back trunk, lower leg, and hip and tail regions with high accuracies of 89%, 91%, 94%, 95%, 92%, and 95%, respectively. Consequently, the segmentation completion rate is 96%, and the average segmentation accuracy reaches 92.8%. When compared to traditional approaches, our method efficiently extracted abundant body characteristics from the beef cattle point cloud. Moreover, the proposed algorithm exhibited generalization capabilities to segment the point clouds of other cloven-hoofed livestock species. The accurate localization of key regions enabled body dimensions measurements and non-contact weighing, which can provide a solid support for breeding needs such as health evaluation, production performance measurement, and genetic breeding evaluation.

Research on Navigation Path Extraction and Obstacle Avoidance Strategy for Pusher Robot in Dairy Farm

Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems of labor-intensive, labor-intensive, and inability to push material in time at night, etc., in this study, an autonomous navigation pusher robot based on 3D lidar is designed, and an obstacle avoidance strategy based on the improved artificial potential field method is proposed. Firstly, the 3D point cloud data of the barn is collected by the self-designed pushing robot, the point cloud data of the area of interest is extracted using a direct-pass filtering algorithm, and the 3D point cloud of the barn is segmented using a height threshold. Secondly, the Least-Squares Method (LSM) and Random Sample Consensus (RANSAC) were used to extract fence lines, and then the boundary contour features were extracted by projection onto the ground. Finally, a target influence factor is added to the repulsive potential field function to determine the principle of optimal selection of the parameters of the improved artificial potential field method and the repulsive direction, and to clarify the optimal obstacle avoidance strategy for the pusher robot. It can verify the obstacle avoidance effect of the improved algorithm. The experimental results showed that under three different environments: no noise, Gaussian noise, and artificial noise, the fence lines were extracted using RANSAC. Taking the change in the slope as an indicator, the obtained results were about −0.058, 0.058, and −0.061, respectively. The slope obtained by the RANSAC method has less variation compared to the no-noise group. Compared with LSM, the extraction results did not change significantly, indicating that RANSAC has a certain resistance to various noises, but RANSAC performs better in extraction effect and real-time performance. The simulation and actual test results show that the improved artificial potential field method can select reasonable parameters and repulsive force directions. The optimized path increases the shortest distance of the obstacle point cloud from the navigation path from 0.18 to 0.41 m, where the average time is 0.059 s, and the standard deviation is 0.007 s. This shows that the optimization method can optimize the path in real time to avoid obstacles, basically meet the requirements of security and real-time performance, and effectively avoid the local minimum problem. This research will provide corresponding technical references for pusher robots to overcome the problems existing in the process of autonomous navigation and pushing operation in complex open scenarios.

Determining the relationship between the slope and directional distribution of the UAV point cloud and the accuracy of various IDW interpolation

Inverse Distance Weighted (IDW) based interpolation method is also widely used in earth science studies. In the classical IDW method, the directional distribution of the reference points around the point to be estimated within the critical circle and the slope differences are not taken into consideration. On the other hand, in the IDW-based method developed by Shepard, the ratio of the distances of the reference points within the critical circle to the critical circle radius (r), the anisotropy and the slope differences are taken into consideration. In this study, the results of the classical IDW method and Shepard method were compared to increase the accuracy of interpolation produced from UAV data. A software has been developed to make these comparisons in more detail. The classical IDW and Shepard based interpolation methods used in this software takes into consideration the anisotropy, the slope differences and the ratio of the distances to the critical circle radius. In this study, UAV flights were performed in three different study areas with different topographic features and 3D point cloud data were obtained in order to make detailed analyzes. Using developed software, data from three different study areas have been tested and the results from different Shepard interpolation models have been discussed. The major contribution of this paper is in evaluation of various IDW options when applied to UAV point data. As a result, especially in geodetic studies form UAV data, it was observed that the results improved with 11% to 37% by using the Shepard method with the suitable power parameter value considering the directional distribution of the reference points in the critical circle and the slope differences.

Measurement method of maize morphological parameters based on point cloud image conversion

The plant height (PH), stem thickness long axis (STLA), and stem thickness short axis (STSA) in maize morphological parameters can effectively reflect the growth, lodging resistance and yield information of maize plants. Terrestrial laser scanning (TLS) can achieve rapid measurement of crop phenotypic parameters. To address the problems of low automation and leaf interference in the existing measurement methods, TLS was used as the measurement sensor, and a morphological measurement method for PH, STLA and STSA of field maize based on point cloud image conversion was proposed. First, in the V3, V6, V9, and V12 stages, three-dimensional (3D) point cloud data of two maize varieties (Jingnongke 728 and Nongda 84) were obtained by TLS. Second, the point cloud processing software was used to match the collected maize point cloud data, obtain the registered multi-site cloud data, remove the background point cloud data, extract the maize row data, and carry out down-sampling. Programming was used to realize data format conversion and individual plant segmentation. Third, several methods, including plane segmentation, statistical filtering, pass filtering, maximum and minimum traversal, and Euclidean clustering were used to remove ground point clouds, judge whether there was maize plant, and extract area maize point clouds. A method of point cloud image conversion was proposed to realize the segmentation of maize stem and leaf. Finally, the height of the plant was measured by calculating the vertical distance from the highest point of the plant to its base. The point cloud of the stem at a specific location was identified, and the ellipse fitting method was used to measure the thickness of the long axis and the short axis of the stem. Compared with the measured value of artificial point cloud, the PH, STLA and STSA of maize in 4 growth stages were measured using automatic program. The root mean square error (RMSE) of the PH, STLA and STSA of Jingnongke 728 were 0.61 cm, 3.16 mm and 2.53 mm respectively, and the mean absolute percentage error (MAPE) were 0.52%, 7.90% and 9.70% respectively. The RMSE of Nongda 84 were 0.66 cm, 2.63 mm and 2.42 mm respectively, and the MAPE were 0.75%, 7.07% and 9.76% respectively. The results show that the point cloud image conversion method for measuring PH, STLA and STSA proposed in this article is suitable for maize of different growth periods and different maize varieties. It is highly consistent with the artificial point cloud measurement values and can replace manual measurement. It can provide breeders with a fast, automatic and accurate measurement program for the PH, STLA and STSA of maize.

DeepPCD

3D Point Cloud Data (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But the measured PCD is often incomplete and sparse due to the sensor occlusion and poor lighting conditions. To automatically reconstruct complete PCD from the incomplete ones, we propose DeepPCD, a deep-learning-based system that reconstructs both geometric and color information for large indoor environments. For geometric reconstruction, DeepPCD uses a novel patch based technique that splits the PCD into multiple parts, approximates, extends, and independently reconstructs the parts by 3D planes, and then merges and refines them. For color reconstruction, DeepPCD uses a conditional Generative Adversarial Network to infer the missing color of the geometrically reconstructed PCD by using the color feature extracted from incomplete color PCD. We experimentally evaluate DeepPCD with several real PCD collected from large, diverse indoor environments and explore the feasibility of PCD autocompletion in various ubiquitous sensing applications.

DeltaConv

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global coordinate system for tangential directions on surfaces. We introduce DeltaConv, a convolution layer that combines geometric operators from vector calculus to enable the construction of anisotropic filters on point clouds. Because these operators are defined on scalar- and vector-fields, we separate the network into a scalar- and a vector-stream, which are connected by the operators. The vector stream enables the network to explicitly represent, evaluate, and process directional information. Our convolutions are robust and simple to implement and match or improve on state-of-the-art approaches on several benchmarks, while also speeding up training and inference.

Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments

There has been growing interest in increasing the application of robotic and automation technologies for building indoor inspection. However, much previous research on indoor robotic applications was limited to a single type of unmanned aerial/ground vehicle (UAV/UGV), each of which has certain limitations and constraints. Besides, the robotic systems suffer from inefficient control within cluttered indoor environments containing many obstacles. This paper presents a multi-agent robotic system (MARS) for automatic UAV-UGV path planning and indoor navigation to automate sensory data collection. The proposed MARS consists of a new system architecture that defines the attributes and data requirements for UAV and UGV indoor path planning. To improve indoor navigation in cluttered environments, an enhanced shunting short-term memory model is established to optimize the pathfinding of UAV/UGV for data collection. Assessment of indoor navigation is conducted with a simulation-based approach and LiDAR SLAM. A mediating agent, which harnesses a control algorithm and information exchange mechanism, is proposed to interoperate UAV and UGV for automated data collection. The proposed new MARS is examined in experiments, in which a single UAV, dual UAVs, and combined UAV-UGV are tested in a research laboratory. The result indicates that the MARS can support automated path planning and indoor navigation for 2D image and 3D point cloud data collection.

Study on the Determination Method of Cast Blasting Stockpile Forms in an Open-Pit Mine

Cast blasting–dragline stripping technology is the most advanced mining technology used in open-pit mines. For a long time, however, its precision has been hindered. In this paper, we aim to improve the precision of cast blasting–dragline stripping technology and promote its intelligent design. We present a method to determine cast blasting stockpile forms. First, the 3D point cloud data for the Heidaigou open-pit mine from recent years were collected and counted, and a 3D mathematical model of overcasting stripping steps was constructed. Then, data classification and multivariate statistical analysis were used to establish a cast blasting stockpile characteristic parameter database. Next, locally weighted linear regression was used as the fitting method to achieve shape fitting under different cast blasting step heights. Finally, interval estimation was used as the fitting result test method to verify the morphology of the acquired cast blasting stockpile form. The research results show that the cast blasting stockpile form obtained by fitting can truly reflect the cast blasting effect of the Heidaigou open-pit mine and ensure the reliability and accuracy of the subsequent design of cast blasting–dragline stripping technology.

Road marking degradation analysis using 3D point cloud data acquired with a low-cost Mobile Mapping System

Road maintenance is an important task that ensures the availability and correct function of the road infrastructure. This work presents a methodology that, first, offers an empirical radiometric analysis of the Velodyne VLP-32C laser scanner. Second, it defines a road marking degradation model that estimates the coefficient of retroreflected luminance (night visibility, RL) using the intensity attribute of 3D point clouds acquired with this low-cost system. This model is validated and applied to a case study road section of about 6 km in length, where a continuous degradation map is defined together with data to assist decision-making for preventive and corrective maintenance. This validation shows that the proposed method is capable of offering good qualitative visualizations of the degradation status of the road markings, as well as detecting those areas with high degradation (RL< 100 mcd/m2/lx) that require corrective maintenance.

Understanding the Imperfection of 3D point Cloud and Semantic Segmentation algorithms for 3D Models of Indoor Environment

Abstract. Point clouds data provides new potentials for automated construction of more geometrically accurate and semantically rich 3D models for indoor environments. Recent advances in deep learning methods on point cloud semantic segmentation demonstrated impressive accuracy in labeling points of 3D surfaces with object classes. However, it remains challenging to reconstruct the shape of semantic objects from semantically-labeled 3D points, due to imperfection of such data and the under-determination of object construction algorithms. We have little empirical knowledge about how data imperfections affect the reconstruction of 3D indoor room objects. This paper contributes to understanding the nature of such imperfection of 3D point cloud data and semantic segmentation algorithms by analyzing the reconstructability of indoor room objects from semantically-labeled point cloud. 181 rooms from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) were used in our experiment. After generating semantic labels on point-clouds using PointNet++ segmentic segmentation algorithm, we use human coders to judge the reconstructability of indoor objects, following a qualitative coding scheme. Human exploration of object shape imperfection was assisted by a visual analytic tool in making their judgement. We found that high point-level accuracy achieved through semantic segmentation of point cloud data does not guarantee high object-level accuracy. The extent of this problem varies widely among different spatial settings and configurations. We discuss the significance of these findings on the choice of 3D reconstruction methods.

Full-Scale Highway Bridge Deformation Tracking via Photogrammetry and Remote Sensing

Recent improvements in remote sensing technologies have shown that techniques such as photogrammetry and laser scanning can resolve geometric details at the millimeter scale. This is significant because it has expanded the range of structural health monitoring scenarios where these techniques can be used. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be used to evaluate the performance of a highway bridge during a static load test. Various point cloud registration and deformation tracking algorithms are explored. Included is an introduction to a novel deformation tracking algorithm that uses the interpolation technique of kriging as the basis for measuring the geometric changes. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. The results demonstrate a pathway from the collection of digital photographs to a mechanical analysis with results that capture the bridge deformation within one standard deviation of the mean reported value. These results are promising given that the midspan bridge deformation for the load test is only a few millimeters. Ultimately, the approaches evaluated in this work yielded errors on the order of 1 mm or less for ground truth deflections as small as 3.5 mm. Future work for this method will investigate using these results for updating finite element models.

Automatic extraction and evaluation of pavement three-dimensional surface texture using laser scanning technology

Laser scanning technology has been increasingly utilised for the detection of pavement three-dimensional (3D) surface texture. This paper aims to develop a 3D laser scanning system and corresponding methods for the precise extraction and evaluation of the mean texture depth (MTD). The contributions are as follows: 1) an improved Steger method is developed to extract the centreline of the laser stripe; 2) 3D point cloud data of the pavement surface are processed using the coordinate transformation and cubic spline interpolation; 3) Monte Carlo expectation method is employed to evaluate pavement MTD, and an appropriate subblock size is obtained based on the optimal reference surface. Based on the sand-patch testing with 35 testing samples, the mean absolute error and Pearson correlation coefficient are 0.017 mm and 0.9864, respectively, indicating the accuracy of the proposed methods for MTD evaluation. Thus, the proposed methods provide a reference for autonomous detection of pavement performance.

Development of a validation technique for road surface profile applicable to point cloud data

In recent years, various information and communication technology (ICT) devices measuring three-dimensional (3D) point cloud data have been developed and widely used for the application of pavement surface investigation. However, ICT devices have generally been developed not only for measuring road surface profiles but for various geo-reference point clouds. In this background, the validation of surface profiles acquired with ICT devices fulfils an important role in proving the reliability of measurement result composed by point clouds. This study proposes a wavelet transform agreement (WTA) which involves a normalization factor of profile amplitude for further improvement in the wavelet-based coherence technique. The WTA analysis allows evaluating similarity/dissimilarity of two profiles considering both the location and wavelength simultaneously. For this purpose, a terrestrial laser scanner (TLS), a mobile mapping system (MMS), and an unmanned aerial vehicle (UAV) are employed to prove the advantage of WTA in practical applications. As a result, the advantages of WTA analysis are clearly recognized in the optimization for the measurement interval of TLS, the multi-line measurement of MMS for ride quality improvement of a pavement, and the efficient operation of UAV in terms of the flight altitude. This paper also shows the identification of aging development for surface roughness over time in terms of locations and wavelengths. These findings help not only to guarantee the accuracy of profile measurements but to realize the sophisticated way of using 3D point clouds acquired with ICT devices. The outcomes of this study contribute to the increase of productivity for pavement works with improving the quality of surface profile measurement. • Wavelet agreement modifies wavelet transform coherence for road profile analysis. • Profile validation becomes reliable with wavelet agreement analysis. • Wavelet agreement performs profile validation keeping locations and wavelengths. • Wavelet-based method has an affinity with three-dimensional point clouds. • Profile validation improves operational efficiency of ICT devices.

LIDAR-BASED MULTI-SENSOR MODULE DEVELOPMENT AND ASSESSMENT OF APPLICABILITY FOR DAMAGE INVESTIGATION OF INDOOR DISASTER

Abstract. As the risk of disaster scenes increases, the number of cases of acquiring disaster scenes information using unmanned robots is increasing. Because unmanned robots can be remotely controlled, sensors such as LiDAR and optical cameras are installed so that investigators can safely observe disaster scenes and acquire information. In particular, the information that can be acquired is different depending on the sensor characteristics, and a sensor module suitable for the purpose is being developed. Accordingly, the National Disaster Management research Institute(NDMI) also developed a investigation robot capable of acquiring information on disaster sites independently. Based on 3D point cloud data, we developed a multi-sensor module and SLAM algorithm customized to the investigation robot to collect quantitative information on the damage situation. To test the performance of the independently developed multi-sensor module, SLAM mapping was performed in a disaster building reproduced like a disaster scene, and various SLAM algorithms and distance comparison were performed. As a result, PackSLAM developed in this study showed the lowest error. In the future, to increase applicability at disaster sites, more precise experiments will be conducted by conducted by establishing a rough terrain environment.

SMALL UAVS-BASED DISASTER DAMAGE INVESTIGATION: FOCUSED ON FLOODING DISASTER AND CHEMICAL ACCIDENT

Abstract. Disasters that occur in recent years are linked to various factors and occur in a chain, and the scale is also increasing. In order to solve the more complex and serious disaster problem, it is necessary to expand the affect scope of the disaster and to acquire and manage information by integrating and operating various sensors. With the introduction of UAVs for disaster work, various studies such as investigation of the affected area, rescue of survivors, and establishment of emergency communication networks are being conducted. This is useful for strengthening the field of disaster management because it is very effective in terms of time and human resources. In addition, it is possible to acquire high-resolution images, and by using the 3D point cloud data and ortho-mosaic imagery generation, it is possible to produce a disaster scene close to the real world. Therefore, if UAV information can be linked to disaster types and characteristics prior to supporting disaster damage investigation, it is possible to establish a response method according to the disaster damage situation and help decision-making. In order to examine the applicability of UAV technology for actual disaster sites, this study conducted data collection and analysis of damaged areas targeting sites damaged by heavy rain and chemical accidents. In the heavy rain damage site, flooding damage occurred in the ginseng cultivation patches due to river overflow caused by the torrential rain, and the damaged area and lots were investigated. The site of the chemical accident was damaged by hydrochloric acid gas leaking into the atmosphere, and the surrounding forest, crops, and residential facilities were identified and the affected area was calculated. As a result of investigating the damage from heavy rain and chemical accidents using UAV, it was possible to quickly collect data on a wide disaster site. In addition, it was easy to calculate the damaged area based on ortho-imagery, and the limitations of spatial analysis were reduced, so that it could be used more specifically and efficiently to the disaster site.

A METHOD FOR EXTRACTING DEFORMATION FEATURES FROM TERRESTRIAL LASER SCANNER 3D POINT CLOUDS DATA IN RGIPT BUILDING

Abstract. With the advancement of measuring technology and the incorporation of photogrammetry into architectural applications, architectural documentation applications have taken on a new viewpoint. Terrestrial laser scanning is a modern approach employed in documentation research today in this field. The following are the most significant advantages of terrestrial laser scanning in this study.The point cloud data obtained by terrestrial laser scanners (TLS) allows for quick access to the correct data at the specified frequency, generating relevant and practical findings for the targeted study, and the ability to use scanners in a variety of working environments. Laser scanners have become one of the most popular tools for obtaining effective and successful outcomes in architectural documentation projects such as surveys, restitution, and renovation. Standardized building rules and design procedures have been developed to create structures that are safe for public usage. Structures are frequently subjected to severe loading situations and difficult environmental situations that were not anticipated during design, resulting in a long-term structural deformation. In this paper, the “RGIPT”, which is one of the educational institutes in Uttar Pradesh, India, was scanned with a terrestrial laser scanner for this study, and 3D sketching, and modelling were done with the SCENE software utilizing only 3D point cloud data. Few columns of the structure have been considered for this paper and their behavior with vibration loading is observed. To conduct this study variation of variances for X-direction and Y-direction is observed for millions of points with increasing height of the structure to predict the upcoming deformation in studied columns of the structure.

ROBUST INDOOR POINT CLOUD CLASSIFICATION BY FUSING LSTM NEURAL NETWORKS WITH SUPERVOXEL CLUSTERING

Abstract. To address the problems of lack of training data and inaccurate classification of existing 3D point cloud data segmentation and classification methods, this paper proposes a high-precision classification algorithm for indoor point clouds by fusing LSTM neural network and super voxels. The algorithm first performs super voxel segmentation on the original point cloud and uses it as the basic unit for machine learning classification, and then introduces LSTM (Long Short-Term Memory) neural network to model the super voxel domain relationship and optimize the classification results. Finally, the accuracy of the proposed method is evaluated based on open dataset, and the experimental results show that 83.2% classification accuracy can be achieved in the open dataset.

Stem-Level Bucking Pattern Optimization in Chainsaw Bucking Based on Terrestrial Laser Scanning Data

Cross-cutting of a tree into a set of assortments (»bucking pattern«) presents a large potential for optimizing the volume and value recovery; therefore, bucking pattern optimization has been studied extensively in the past. However, it has not seen widespread adoption in chainsaw bucking, where time consuming and costly manual measurement of input parameters is required for taper curve estimation. The present study investigated an alternative approach, where taper curves are fit based on terrestrial laser scanning data (TLS), and how deviations from observed taper curves (REF) affect the result of bucking pattern optimization. In addition, performance of TLS was compared to a traditional, segmental taper curve estimation approach (APP) and an experienced chainsaw operator’s solution (CHA).A mature Norway Spruce stand was surveyed by stationary terrestrial laser scanning. In TLS, taper curves were fit by a mixed-effects B-spline regression approach to stem diameters extracted from 3D point cloud data. A network analysis technique algorithm was used for bucking pattern optimization during harvesting. Stem diameter profiles and the chainsaw operator’s bucking pattern were obtained by manual measurement. The former was used for post-operation fit of REF taper curves by the same approach as in TLS. APP taper curves were fit based on part of the data. For 35 trees, TLS and APP taper curves were compared to REF on tree, trunk and crown section level. REF and APP bucking patterns were optimized with the same algorithm as in TLS. For 30 trees, TLS, APP and CHA bucking patterns were compared to REF on operation and tree level.Taper curves were estimated with high accuracy and precision (underestimated by 0.2 cm on average (SD=1.5 cm); RMSE=1.5 cm) in TLS and the fit outperformed APP. Volume and value recovery were marginally higher in TLS (0.6%; 0.9%) than in REF on operation level, while substantial differences were observed for APP (–6.1%; –4.1%). Except for cumulated nominal length, no significant differences were observed between TLS and REF on tree level, while APP result was inferior throughout. Volume and value recovery in CHA was significantly higher (2.1%; 2.4%), but mainly due to a small disadvantage of the optimization algorithm.The investigated approach based on terrestrial laser scanning data proved to provide highly accurate and precise estimations of the taper curves. Therefore, it can be considered a further step towards increased accuracy, precision and efficiency of bucking pattern optimization in chainsaw bucking.

Teleoperation Robot Control of a Hybrid EEG-Based BCI Arm Manipulator Using ROS

The development of assistive robots is gaining momentum in the robotic and biomedical fields. An assistive robotic system is presented in this research paper for object manipulation to aid people with physical disabilities. The robotic arm design is imported to a simulated environment and tested in a virtual world. This research includes the development of a versatile design and testing platform for robotic applications with joint torque requirements, workspace restrictions, and control tuning parameters. Live user inputs and camera feeds are used to test the movement of the robot in the virtual environment. To create the environment and user interface, a robot operating system (ROS) is used. Live brain computer interface (BCI) commands from a trained user are successfully harvested and used as an input signal to pick a goal point from 3D point cloud data and calculate the goal position of the robots’ mobile base, placing the goal point in the robot arms workspace. The platform created allows for quick design iterations to meet different application criteria and tuning of controllers for desired motion.