Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

Improving Machining Accuracy for a Robotic Arm with Hybrid Kinematic Chains Based on Deformation Characteristics

The joint deformation has great influence on machining accuracy for a robotic arm. In this paper, the deformation characteristics of the robotic arm with hybrid kinematic chains is investigated in order to improve its machining accuracy. Firstly, the deformation model of the joints has been established based on the Strain energy method and Castigliano theorem according to the robot structure. Secondly, the deformation influence coefficient (DIC) is defined to investigate the deformation influence of main components on the end-effector, and the deformation characteristics are evaluated by the simulation. Finally, a small size robotic arm prototype is established and robotic drilling comparative experiments are conducted. The theoretical and experiment results show that the machining method can be selected according to the DIC, which the force can be applied to the components with better stiffness. On the other hand, the deformation of driving components can also be reduced when the DIC cannot be adjusted to meet the accuracy requirement.

Improved BEC SMOS Arctic Sea Surface Salinity product v3.1

Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) is low (about 0.5 K / psu), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K). Moreover, the sensitivity of the TB to SSS at cold waters is even lower (0.3 K / psu), making the retrieval of the SSS in the cold waters even more challenging. Due to this limitation, ESA launched a specific initiative in 2019, the Arctic+Salinity project (AO/1-9158/18/I-BG), to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product (Martínez et al., 2020) . The product consists of 9-day averaged maps in an EASE 2.0 grid of 25 km. The product is freely distributed from the Barcelona Expert Center (BEC, http://bec.icm.csic.es/) with the DOI number: 10.20350/digitalCSIC/12620. The major change in this new product is its improvement of the effective spatial resolution that permits better monitoring of the mesoscale structures (larger than 50 Km), which benefits the river discharges monitoring.

On the accuracy requirements of terrestrial laser scanning for solving engineering and geodetic tasks using BIM

Terrestrial laser scanning and digital information modeling are increasingly practiced every year to solve application tasks at various stages of the industrial facility’s life cycle. In this regard, the task of formulating the requirements for the accuracy of performing terrestrial laser scanning for the subsequent forming digital information models becomes more and more calling. In this article we analyzed the types of engineering and geodetic works by which engineering tasks are solved at various stages of the industrial facility’s life cycle in order to create an accuracy requirement. An analysis of the regulatory and technical documentation that specifies doing these works was also made. Basing on it, the relationship between the measurement accuracy characteristics specified in the regulatory and technical documentation (design, construction and operational) and the mean square errors in determining the position of points is described. The authors propose a scheme for transition from the characteristics of the measurements accuracy to the mean square errors of determining the position of points for each type of engineering and geodetic work. The results of this study can be used at planning terrestrial laser scanning of industrial facilities. Basing on the above requirements for the accuracy of the geodetic work, it is possible to formulate a methodology for carrying out each stage of the TLS technological scheme.

Enabling Retrain-free Deep Neural Network Pruning Using Surrogate Lagrangian Relaxation

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.

Validation of Soil Moisture Data Products from the NASA SMAP Mission

NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m³/m³). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Validation of Soil Moisture Data Products from the NASA SMAP Mission

NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m³/m³). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Mobile 3D Mapping with a Low-Cost UAV-Based LIDAR System

Recently, many UAVs (unmanned aerial vehicles) based on LiDAR (light detection and ranging) systems have been developed for various purpose because of the effective of LIDAR technique and low-cost UAV. In this study, the accuracy of point clouds generated by the developed for a low-cost UAV-based LiDAR systems is evaluated. The system consisting of a multi-beam laser scanner- Velodyne VLP 16 and DJI M600 UAV. The experimental site is undulation with less object in Nagaoka city, Niigata Prefecture, Japan Twelve reflectance makers are arranged as ground control point for the positioning evaluating process. The observed data was collected on Nov. 8th, 2019 with three different flight height at 10m, 20m and 30m. For generating the point clouds, the mounting parameters and sensor parameters are combined. The generated point clouds are corrected by applying bias correction and the 7 parameters transformation. The result is validated using three different experimental setups with three various flight height which indicate that the most accurate and reliable results are obtained. As a result, the point clouds after calibrating attained an accuracy of approximate 0.2 m in vertical and horizontal for both correction methods. In conclusion, the point cloud accuracy is not good enough for generating the topographic map at large scale. However, the stable results and the present accuracy are good for other purposes with less accuracy requirement such as monitoring the crop growth.

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control

Purpose Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, challenges of the manual procedure may degrade the efficacy of the technique, such as the high accuracy requirement and radiation exposure to the surgeons. This paper aims to develop a robotic system and its control methods for assisting surgeons on the treatment. Method We present an interventional robotic system, which consists of a 5 Degree-of-Freedom (DoF) positioning robotic arm (a 3-DoF translational joint and a 2-DoF revolute joint) and a needle actuator used for needle insertion and radioactive seeds implantation. Control strategy is designed for the system to ensure the safety of the motion. In the designed framework, an artificial potential field (APF)-based motion planning and an ultrasound (US) image-based contacting methods are proposed for the control. Result Experiments were performed to evaluate position and orientation accuracy as well as validate the motion planning procedure of the system. The mean and standard deviation of targeting error is 0.69 mm and 0.33 mm, respectively. Needle placement accuracy is 1.10 mm by mean. The feasibility of the control strategy, including path planning and the contacting methods, is demonstrated by simulation and experiments based on an abdominal phantom. Conclusion This paper presents a robotic system with force and US image feedback in assisting surgeons performing brachytherapy on liver tumors. The proposed robotic system is capable of executing an accurate needle insertion task with by optical tracking. The proposed methods improve the safety of the robot’s motion and automate the process of US probe contacting under the feedback of US-image.

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).