Lidar based map construction fusion method for underground coal mine shaft bottom

Intellectualization of coal mine is the technical support for high-quality development of coal industry, and robot replacement of key posts is the development trend of realizing efficient mining of coal with few people and no people. Simultaneous localization and mapping (SLAM) is one of the key technologies for autonomous movement and navigation of coal mine robots. The environment of underground coal mine is a typical unstructured environment, with narrow space, complex and changeable structure and uneven lighting, posing a severe challenge to the realization of SLAM in the underground coal mine. The research status of the map construction of the underground coal mine is summarized. In view of the shortcomings of the loopback detection of the LeGO-LOAM algorithm, the SegMatch algorithm is used to improve the loopback detection module of the LeGO-LOAM, the ICP algorithm is used to optimize the global map, and an improved algorithm integrating LeGO-LOAM and SegMatch is proposed, and the principle and implementation of the algorithm are discussed. The underground simulation scene experiments of coal mine were carried out, the mapping effect and accuracy of SLAM algorithm before and after the improvement were compared and analyzed, and the results showed that the map loopback effect constructed by the improved algorithm was better, and the estimated trajectory was smoother and more accurate. The construction method of two-dimensional occupied grid map is studied aiming at the navigation requirements, and the accuracy of the grid map constructed by this method is verified through experiments. The results show that the grid map after effectively filtering outliers such as dynamic obstacles has a mapping accuracy of 0.01 m, and the required storage space is 3 orders of magnitude lower than that of the point cloud map. The research results are helpful to the realization of SLAM and real-time positioning and autonomous navigation of the coal mine robot under the unstructured environment of the underground coal mine

Applications of 2D-layered palladium diselenide and its van der Waals heterostructures in electronics and optoelectronics

The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe2) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe2. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe2, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe2 nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe2 and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe2 van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community.Web of Science131art. no. 14

LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for Underground Coalmine

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed

Research on Underground Coal Mine Map Construction Method Based on LeGO-LOAM Improved Algorithm

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China’s national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles

The Oxygen Reduction Reaction Rate of Metallic Nanoparticles during Catalyzed Oxidation

This work reports the oxygen reduction reaction (ORR) kinetics of metal nanoparticle catalysts between 500 and 600 °C at low oxygen partial pressures. Ex situ and in situ TEM measurements demonstrate catalyzed nanowire growth initially follows linear kinetics; characteristic of being ORR rate limited. The ORR rates of Ag, Au, Cu, Ni, Pd, Rh and Pt measured at 600 °C form a volcano plot versus relative oxidation potential. Cu nanoparticles produce the maximum ORR rate under these conditions

A Gene-Switch Platform Interfacing with Reactive Oxygen Species Enables Transcription Fine-Tuning by Soluble and Volatile Pharmacologics and Food Additives

Synthetic biology aims to engineer transgene switches for precise therapeutic protein control in cell-based gene therapies. However, off-the-shelf trigger-inducible gene circuits are usually switched on by single or structurally similar molecules. This study presents a mammalian gene-switch platform that controls therapeutic gene expression by a wide range of molecules generating low, non-toxic levels of reactive oxygen species (ROS). In this system, KEAP1 (Kelch-like ECH-associated protein 1) serves as ROS sensor, regulating the translocation of NRF2 (nuclear factor erythroid 2-related factor 2) to the nucleus, where NRF2 binds to antioxidant response elements (ARE) to activate the expression of a gene of interest. It is found that a promoter containing eight-tandem ARE repeats is highly sensitive to the low ROS levels generated by the soluble and volatile molecules, which include food preservatives, food additives, pharmaceuticals, and signal transduction inducers. In a proof-of-concept study, it is shown that many of these compounds can independently trigger microencapsulated engineered cells to produce sufficient insulin to restore normoglycemia in experimental type-1 diabetic mice. It is believed that this system greatly extends the variety of small-molecule inducers available to drive therapeutic gene switches.ISSN:2198-384

Evolution of molecular switches for regulation of transgene expression by clinically licensed gluconate

Synthetic biology holds great promise to improve the safety and efficacy of future gene and engineered cell therapies by providing new means of endogenous or exogenous control of the embedded therapeutic programs. Here, we focused on gluconate as a clinically licensed small-molecule inducer and engineered gluconate-sensitive molecular switches to regulate transgene expression in human cell cultures and in mice. Several switch designs were assembled based on the gluconate-responsive transcriptional repressor GntR from Escherichia coli. Initially we assembled OFF- and ON-type switches by rewiring the native gluconate-dependent binding of GntR to target DNA sequences in mammalian cells. Then, we utilized the ability of GntR to dimerize in the presence of gluconate to activate gene expression from a split transcriptional activator. By means of random mutagenesis of GntR combined with phenotypic screening, we identified variants that significantly enhanced the functionality of the genetic devices, enabling the construction of robust two-input logic gates. We also demonstrated the potential utility of the synthetic switch in two in vivo settings, one employing implantation of alginate-encapsulated engineered cells and the other involving modification of host cells by DNA delivery. Then, as proof-of-concept, the gluconate-actuated genetic switch was connected to insulin secretion, and the components encoding gluconate-induced insulin production were introduced into type-1 diabetic mice as naked DNA via hydrodynamic tail vein injection. Normoglycemia was restored, thereby showcasing the suitability of oral gluconate to regulate in situ production of a therapeutic protein.ISSN:1362-4962ISSN:0301-561

An electrogenetic interface to program mammalian gene expression by direct current

Wearable electronic devices are playing a rapidly expanding role in the acquisition of individuals’ health data for personalized medical interventions; however, wearables cannot yet directly program gene-based therapies because of the lack of a direct electrogenetic interface. Here we provide the missing link by developing an electrogenetic interface that we call direct current (DC)-actuated regulation technology (DART), which enables electrode-mediated, time- and voltage-dependent transgene expression in human cells using DC from batteries. DART utilizes a DC supply to generate non-toxic levels of reactive oxygen species that act via a biosensor to reversibly fine-tune synthetic promoters. In a proof-of-concept study in a type 1 diabetic male mouse model, a once-daily transdermal stimulation of subcutaneously implanted microencapsulated engineered human cells by energized acupuncture needles (4.5 V DC for 10 s) stimulated insulin release and restored normoglycemia. We believe this technology will enable wearable electronic devices to directly program metabolic interventions.ISSN:2522-581