Abstract: Unmanned aerial vehicle (UAV) remote sensing is a state-of-the-art technology that integrates UAV, remote sensing sensor, GPS differential positioning, communication, and other technologies to achieve rapid collection, processing, and analysis of geographic environmental information. UAV remote sensing is considered an important supplement of spaceborne remote sensing and is recently being widely used in the topographical surveying and mapping, precision agriculture, heritage inspection, and emergency rescue, etc. For the traditional mining industry, high-quality and real-time UAV remote sensing data can be obtained at reasonable costs and benefit the mining operations, particularly for numerous small- and medium-scale mining sites where equipments and professional expertise are expensive. However, application scenarios of UAV remote sensing in the mining industry are rarely reported and lack systematic review. Therefore, the definition, platform composition, current status, and general workflow of UAV remote sensing technology were summarized in this study. Then, through significant domestic and foreign literature surveys, the application scenarios and practical case studies of UAV remote sensing in the mining industry were systematically presented. Finally, the development trend was analyzed on the basis of the shortcomings of current technology. Results show that (1) UAV remote sensing technology has the advantages of low costs, strong maneuverability, flexible data sampling settings, timeliness, repeatability, and high resolution. (2) The current applications of UAV remote sensing in the mining industry mainly include the operations management of open-pit mines, safety monitoring of tailing ponds, emergency rescue, environmental monitoring of mining areas, and prevention and control of slope disasters. (3) The development trend of UAV remote sensing technology application will include the standardization of UAV supervision, simplification of UAV control mode, augmentation of UAV endurance time, improvement of the quality of results, and further expansion of application scenarios. UAV remote sensing technology has broad application prospects in the mining industry and is bound to become an indispensable part of smart mines.
Abstract: Artificial intelligence (AI), especially the rapid development of deep learning, has a profound impact on various industries and has continuously changed the traditional production methods and lifestyles. From passive learning with computing power to autonomous learning and enhanced learning, the development of machine intelligence is largely due to the innovation of the AI theory and practice. AI has also had a far-reaching impact on the military field, as it has provided modern warfare with new features such as intelligence, interconnectedness, and destructiveness. Winning in a military confrontation requires not only machine intelligence but also human wisdom. Therefore, human-machine collaboration would combine the strengths and complement the weaknesses of human and machine, which is the key to victory in the increasingly complex war environment. How to achieve a high degree of hybrid human–artificial intelligence to obtain a good result of “1+1>2” is also a problem that needs to be further explored in military confrontation. This paper reviewed the application of AI in military confrontation as the starting point and highlighted the important measures and achievements of representative countries in the use of AI technology in the military development process. Moreover, we analyzed the development status from the two perspectives of confrontation strategy and the three-tier architecture of the Internet of Things, revealed the shortcomings of using AI in the current military field, and analyzed the development trend of hybrid human–artificial intelligence in military confrontation. We also presented three possible technical schemes and detailed explanations and finally proposed future research directions. We believe that the future development trend of intelligent military may be based on the hybrid human–artificial intelligence, which will further improve the adaptability of machines to the combat environment and reveal the merits of the integration of human wisdom and machine intelligence; this integration may be the next step of AI research in military confrontation.
Abstract: Metal-organic frameworks (MOFs) are a new class of organic–inorganic hybrid materials that show great potential for gas adsorption and storage. However, the powder form of these materials limits the range of their applications. Integration of MOFs on polymer fiber scaffolds to increase the contact area between these frameworks and target molecules and improve the performance of the resulting material is expected to provide new application prospects in gas adsorption, membrane separation, catalysis, and toxic gas sensing. Electrochemical sensors with good flexibility and high sensitivity and selectivity are needed in environmental detection, disease diagnosis, food safety, and other fields. Flexible resistance sensors are sensitive, low cost, and can be produced on a large scale; thus, these sensors have received extensive attention from researchers. Preparing flexible resistance sensors with high sensitivity, high stability, and good wearing comfort is a current and popular area of research. In this paper, we summarized the research and application of MOFs materials combined with metal oxides, textiles and carbon-based conductive fiber materials in the field of resistance gas sensors. Metal oxides act as a conductive material in resistance sensors bearing a metal oxide-and-MOF design. In this design, MOFs play two roles. First, MOFs can wrap precious metals and form nanoparticles encasing these precious metals when calcined. Here, the precious metal functions as a catalyst while the MOF is used as a dispersant to distribute the metal evenly on the surface of the sensing material. Second, the MOFs are combined with the metal oxide by in situ growth or doping on the metal oxide surface. The MOF surface has a large specific surface area and numerous active sites that can bind with the target gas. Resistance sensing is achieved by changing the electronic distribution within the sensing material. When textiles and MOFs are combined, the resulting resistive sensing materials must have a certain electrical conductivity. However, common MOF materials have poor electrical conductivity. Therefore, developing a conductive MOF material in which 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaaminotriphenylene (HATP) show strong sensing performance for NO, H2S, and H2O is necessary. Carbon nanotube fibers and MOF materials can also be combined to obtain resistive sensor materials. Carbon nanotube materials are characterized by cross contact at the nanoscale and have good mechanical and electrical conductive properties. Thus, they feature certain advantages over other materials when applied to flexible resistive sensors.
Abstract: To further reveal the internal mechanism of the granular media flow process under the flexible isolation layer, numerical experiments on the evolution characteristics of bulk media flow force chain under the flexible isolation layer were carried out based on the discrete element software PFC. Based on a combination of contact mechanics and statistical mechanics, the evolution characteristics of the force chain length, quantity, strength, direction, and the collimation coefficient of the internal bulk medium system in the multi-funnel ore drawing process were quantitatively studied. It is found that the proportions of the strong contact and the force chain contact is found to be relatively stable in the multi-funnel ore drawing process; the proportion of strong contact is stable at about 33%, that of the force chain contact is stable at about 16%, and the fluctuation amplitude is not more than 2%. The total number of force chains decreases with the increase in ore drawing times, and it is stable at 790 strips in the later stage of ore drawing. The probability distribution of the force chain length is almost the same under different ore drawing times, and it decreases exponentially with the increase in the force chain length. The probability distribution of the force chain strength first increases exponentially with the increase in the ore drawing times and then decreases exponentially; it reaches a peak value at 0.7$\bar F$ ($\bar F$ is the average contact force). In the initial ore drawing stage, the force chain is mainly distributed along the vertical direction, and the force chain direction distribution is similar to a peanut shape. After that, with the continuous release of ore particles, the phenomenon of local stress concentration in the granular media system becomes remarkable, and the main direction of the force chain distribution changes to become four (vertical direction, horizontal direction, and angles of ±60° to the horizontal). The force chain collimation coefficient increases exponentially with the increase in drawing times and gradually becomes stable.
Abstract: Slot blasting is widely used in mining and tunnel construction, municipal demolition, water conservation, hydropower, and other related projects due to its low cost and high efficiency. In the slot-blasting technique, it is necessary to break the rock efficiently and minimize the damage to the area surrounding the rock. Therefore, improving the blasting efficiency and explosive energy utilization rate as well as reducing the blasting vibration and excessive crushing of rocks are of great significance to the development of blasting engineering. When air spaced uncoupling medium is used in slot blasting, its rock-breaking efficiency is significantly low due to various factors such as generation of shock waves, low quasi-static pressure, low energy utilization rate of explosive, and weak rock-breaking ability. To improve the rock-breaking load of slot blasting, the slot-hydraulic blasting method was proposed. In this method, water is utilized as the uncoupling medium for slot blasting as water has better microcompressibility and high energy transfer efficiency; in addition, research on its characteristics under rock-breaking load was investigated. Slot blasting with air spaced uncoupling charge and slot-hydraulic blasting tests were carried out under the independently developed slot blast load test system. The test results show that the shockwave pressure of slot-hydraulic blasting tests is approximately 35 times that of the air uncoupling blasting method because of the generation of high-pressure shockwaves and the higher incident efficiency. The hydraulic blasting quasi-static pressure is 37–46 times that of the air spaced uncoupled blasting, the quasi static pressure drop of hydraulic blasting is slow, and the pressure holding time is longer. The research results reveal that the energy utilization rate in the slot-hydraulic blasting is high and the blasting load improvement is significant. These results may help to better understand the rock-breaking load characteristics of slot-hydraulic blasting and provide theoretical and experimental support for utilizing the method in engineering applications.
Abstract: Coal–oxygen reaction theory, which is widely accepted, considers the reaction of coal and oxygen during combustion. In this research, the characteristics of spontaneous coal combustion were assessed at a high temperature to investigate the internal relationship between the gaseous products of this reaction and the functional groups in coal molecules and to further reveal the micro-characteristics of spontaneous coal combustion. Our self-developed temperature-programmed experimental system and in situ diffuse reflectance infrared Fourier transform spectroscopy were adopted to analyze the correlation between the contents of gaseous products and active functional groups. Results reveal that the contents of indicator gases, such as CO and C2H4, increase and show a parabolic curve. In terms of active functional groups, as temperature increases, the content of aliphatic hydrocarbons initially increases and then decreases gradually. The content of C=C groups decreases throughout this study, and the content of oxygen-containing functional groups gradually increases after equilibrium is reached. Five characteristic temperatures are obtained on the basis of the variation in gaseous products, and four oxidation stages are further divided. The relationship between active functional groups and gases during different temperature stages is determined. At the critical temperature stage, the main active functional group affecting the release of CO, CO2, CH4, and C2H6 is carbonyl. Numerous alkyl chains and bridge bonds are broken at the crack?active?speedup temperature stage, and the primary active functional groups influencing the gas products are aliphatic hydrocarbons and carbonyl groups. The concentration of gases at the speedup?ignition temperature stage is negatively correlated with carbonyl and carboxyl groups. Therefore, the crack?active?speedup temperature stage in high-temperature oxidation is dangerous, and oxidation should be controlled before this stage to reduce the loss of personnel and materials.
Abstract: Viscosity is a physical property of fluids and shows resistance to flow. In metallurgical slag, it directly affects various parameters of a metallurgical process such as reaction rate, separation effect, etc. The estimation of viscosity by models during a production process is considered to be much more effective owing to the production fluctuations and complexities of the slag composition. Many viscosity models have been developed in the past, such as the structural model with a wide range of adaptability and complex calculation process and the empirical and semiempirical models with simple structure and a narrow range of adaptability. The present paper proposed a new method to calculate the structural parameter (NBO/T) ratio (the amount of nonbridging oxygen per tetrahedral-coordinated atom), based on which the relationship between the viscosity of molten slag and (NBO/T) ratio was investigated. First, the viscosity model was applied to SiO2–ΣMxO slags, with the model parameters obtained by fitting the viscosity data of pure oxide and SiO2–MxO binary slag, and the average deviations were in the range of 9%–18.5%. Then, the model was extended to calculate the viscosity of SiO2–Al2O3–ΣMxO, a multicomponent complex aluminosilicate system, and Al2O3 was split into acid and basic oxides. Then the oxides were used for calculating the (NBO/T) ratio and viscosity activation energy based on the amphoteric behavior of Al2O3 in SiO2–Al2O3–MxO ternary slag system. Using the parameters of a SiO2–MxO binary system, the model parameters of the Al2O3-containing slag system were obtained by fitting the viscosity data of the SiO2–Al2O3–MxO ternary slag system with average deviations between 10% and 25%. In addition, the viscosity of a multi-complex system (SiO2–Al2O3–CaO–MgO–FeO–Na2O–K2O–Li2O–BaO–SrO–MnO) and its subsystems were also calculated using the model proposed in this paper, and the average deviations is less than 25%, which shows relatively accurate prediction results. The present model is based on the analysis of a slag structure and processing of data by an empirical method. This method has a better prediction effect and wider application range compared with the traditional empirical model, and it uses a simpler calculation process compared with the structure model.
Abstract: Microwave heating was used in this study to chlorinate the extraction of Fe, Mn, V, and Cr from vanadium slag using AlCl3 molten salt at a temperature range from 500 to 800 °C. Microwave heating chlorination kinetics was studied in a non-isothermal mode. The effects of the AlCl3/vanadium slag mass ratio and molten salt ratio on the extraction rate of chlorination peoducts were investigated. The structure and morphology evolution of microwave heating chlorination products were characterized by X-ray diffraction and scanning electron microscope with energy dispersive spectrometer. The results show that the highest extraction rate of the five elements (Fe, Mn, V, Cr, and Ti) can be achieved as 91.66%, 92.96%, 82.67%, 75.82% and 63.14%, when the mass ratios of AlCl3/vanadium slag and NaCl–KCl/AlCl3 are 1.5 and 1.66, respectively. These extraction rates in the microwave heating mode for 30 min are reached and exceeded by 6 h in the conventional heating method. Microwave heating will minimize the chlorination time and reduce the volatilization of AlCl3. Based on the thermodynamics and kinetic analysis, the different phases of vanadium slag can be chlorinated using AlCl3 in the range from 400 to 800 °C, and the olivine phase is superior to the spinel phase in chlorination. In addition, the chlorination rates of V and Cr are slower than those of Fe and Mn, and increasing the reaction time is advantageous for the chlorination of V and Cr. The chlorination of Fe and Mn is controlled by diffusion, and the non-isothermal diffusion activation energies of Fe and Mn are 17.02 and 17.10 kJ·mol?1, respectively. In contrast, the chlorination of V and Cr is limited in the interfacial chemical reaction step, for whom the activation energies give 40.00 and 50.92 kJ·mol?1, respectively. The combination effect of the microwave and molten salt on the chlorinating vanadium slag can be attributed to the enhancement of the diffusion and local chemical reaction.
Abstract: With the increasing demand for improvements in the temperature capability of aero-engines, there is an urgent need to develop new-generation turbine blade materials. Compared with Ni-based superalloys that have a lower melting point (~1300 ℃), the higher melting point (>1750 ℃), lower mass density (6.6–7.2 g·cm–3), and high-temperature strength of the Nb–Si based alloys make them one of the most promising of the new-generation high-temperature structural materials. A directional solidification process can further enhance the performance of Nb–Si based alloys and lay a foundation for replacing the Ni-based single-crystal superalloys in service at higher temperatures. Accurately determining the thermal property parameters of Nb–Si based alloys and their interfacial heat transfer behavior during solidification is the key to their numerical simulation, which could accelerate the development of Nb–Si based alloys. As yet, however, there has been no research reported in relation to this issue. In this study, we used the directional solidification process of Nb–Si based alloys as the research object and the experimental testing and reverse methods to determine the thermal properties of Nb–Si based alloys and their shells as well as the boundary conditions of the heat transfer coefficient at the interface during the solidification process. To simulate the temperature field of the solidification process of Nb–Si based alloys at different drawing rates, we used ProCAST software. The results reveal that as the withdrawal rate increased from 5 to 10 mm·min?1, the distance between the solid/liquid interface and the surface of the liquid metal tin decreased from 12.1 to 8.2 mm, and the average width of the mushy zone gradually narrowed from 11.5 mm to 10.4 mm. The discrepancy in the spacing of the primary dendrites between the numerical simulation and the actual experimental results at a withdrawal rate of 5 mm·min?1 was within 6%, which verifies the correctness of the temperature-field simulation results. These results provide reference for the determination of the directional solidification casting parameters of turbine blades made of Nb–Si based alloys.
Abstract: The texture of Zr–4 alloy not only affects its irradiation growth performance, but also affects mechanical properties, stress corrosion cracking, and water-side corrosion. Therefore, it is important to control the texture of Zr–4 alloy during processing. The effect of the applied external stress, annealing temperature, and annealing time on texture evolution and recrystallization of Zr–4 alloy is still unclear. Based on controllable process conditions, the stress annealing process of zirconium alloy in practical production was simulated by designing a simple experimental device. The texture and recrystallization behavior of Zr–4 alloy after annealing at different temperatures and stresses were studied by X-ray diffraction (XRD) and electron backscatter diffraction (EBSD) techniques. The results show that applying external stress and increasing annealing temperature significantly change the evolution of recrystallized texture. With an increase in stress and annealing temperature, the texture of the zirconium alloy ($\overline 1 2\overline 1 5$)[$10 \overline 1 0$], and the polar density decreases, thereby resulting in a decrease in material anisotropy. The annealing temperature has a significant effect on the amount of small-angle grain boundary and recrystallization ratio during material recrystallization. With an increase in applied stress and annealing temperature, dynamic recovery and recrystallization occur inside the material. The sub-structures in dynamic recovery and the dislocation sub-structures in the grains that undergo dynamic recrystallization gradually disappear. The small-angle grain boundary in the material recrystallization process is reduced significantly. The process is accelerated and the recrystallization ratio of the material is significantly increased. The application of applied external stress and the increase of annealing temperature are beneficial to the acceleration of the internal recrystallization process of the material. The main results from this paper can guide the optimization of annealing treatment of Zr–4 alloy, and provide a scientific basis for solving the problems encountered in the engineering application of Zr–4 alloy.
Abstract: CO2-enhanced oil recovery (CO2-EOR) technology is the process of capturing CO2, transporting the captured CO2 to a storage site, and injecting the captured CO2 into an oil field to enhance oil recovery. CO2-EOR technology can greatly increase the profitability of oil fields. It is also a promising method for reducing CO2 emission and improving the environment. For these reasons, this technology has become increasingly important for the development of the global oil industry and has been widely explored. However, CO2 injection significantly increases the risk of corrosion failure of tubing steel. As such, the effect of CO2 on the stress corrosion behavior of tubing steel should be investigated. In this study, the effect of CO2 partial pressure ($P_{{\rm{CO}}_2} $) on the stress corrosion behavior of N80 steel was examined using an immersion test, a surface analysis technique, and an electrochemical technology. Results reveal that the influence of $P_{{\rm{CO}}_2} $ on the corrosion rate has an inflection point of approximately 1 MPa. When $P_{{\rm{CO}}_2} $ is <1 MPa, a corrosion product film (FeCO3) forms slowly, and the coverage rate is low. As $P_{{\rm{CO}}_2} $ increases, the corrosion current density of N80 steel increases. When $P_{{\rm{CO}}_2} $ is >1 MPa, the corrosion product film can form at a faster rate, and the corrosion current density of N80 steel decreases as $P_{{\rm{CO}}_2} $ increases. The pH of the solution decreases continuously when CO2 is dissolved in solution. Consequently, the stress corrosion cracking (SCC) of N80 tubing steel occurs in an annulus environment. The SCC mechanism of N80 steel in the annulus environment of CO2 injection wells is the combination of anodic dissolution (AD) and hydrogen embrittlement (HE). Localized AD (pitting) is dominant in SCC at the initiation stage, and SCC is most likely initiated at $P_{{\rm{CO}}_2} $ of 1 MPa. At the crack growth stage, HE has a stronger effect on SCC than AD, the SCC easily grows with a high $P_{{\rm{CO}}_2} $, and SCC sensitivity further improves.
Abstract: The effect of solid-state phase transformation during heat treatment on the friction and wear properties of Cu–3Fe–0.18C alloy prepared by vacuum melting was studied. The as-cast structure, deformed structure, Fe–C phase morphology, mechanical properties, and the friction and wear behavior of Cu–Fe–C alloy were studied by optical microscopy (OM), scanning electron microscopy (SEM), nano-mechanical probe analysis, mechanical properties test, and friction and wear experiments, respectively, at room temperature. The results show that micro- and nano-sized Fe–C phases are dispersed in the Cu–(Fe–C) alloy, and the micron-sized Fe–C phase undergoes solid-state transformation during quenching and tempering, which is similar to the martensite transformation and tempering transformation in steel. After quenched at 850 ℃ and tempering at 200, 400 and 650 ℃, the Fe–C phase gradually transforms from acicular martensite to granular tempered sorbite. The corresponding nano-hardness of the Fe–C phase is 9.4, 8, 4.2 and 3.8 GPa, respectively, and the hardness of the strengthening phase is controlled. Through an analysis of tensile fracture, a large number of dissociation surfaces appear on the fracture surface of the quenched alloy. The crack source is located at the interface between the Fe–C phase and the matrix. With an increase in the tempering temperature, the dissociation surface of the fracture surface of the tempered alloy gradually decreases until it disappears, and the crack source gradually transfers to the matrix. The evolution of fracture surface indicates that the bonding between Fe–C phase and matrix in the quenched alloys is poor. With the increase of the tempering temperature, the bonding interface between the Fe–C phase and the matrix is improved. The experimental results of friction and wear at room temperature show that with the increase of tempering temperature, the wear mechanism of the alloy gradually changes from ploughing to adhesion wear and severe plastic deformation, which results in a decrease in the alloy wear resistance. This paper can provide a reference for controlling the friction and wear properties of Cu–(Fe–C) alloys by the solid-state transformation of the Fe-C phase martensitic decomposition.
Abstract: In the context of the global response to environmental pollution and climate change, countries have begun to pay attention to energy system reform and economic development to ensure low carbon transition. Among them, the development of low carbon transportation has become an important aspect of green transportation system construction. The development of electric vehicle technology can effectively reduce energy consumption and environmental pollution. However, with the recent reports of new energy vehicle safety accidents at home and abroad, the safety of lithium-ion batteries has attracted increasing attention from the industry. To prevent overcharging and overdischarging from affecting battery life and safety during use, a complete battery management system is required to control and manage a lithium-ion battery. The state of charge (SOC) used to reflect the remaining capacity of a battery is one of the key parameters. Therefore, an accurate SOC value is of significance to the safety of lithium-ion battery use and the safety performance of new energy vehicles. The low online estimation accuracy of the SOC of lithium-ion batteries and the estimation accuracy of the equivalent circuit model method are inconsistent with the model complexity. This study improved the extended Kalman filtering (EKF) algorithm and established a SOC estimation error prediction model based on the extreme learning machine (ELM) algorithm, which used the operating voltage and current of the battery as input and the SOC estimation error of the equivalent circuit model method as the output. On the basis of the physical data fusion method and the error prediction model, the online estimation model of the lithium-ion battery SOC based on the equivalent circuit model method combined with the ELM was established. The simulation results showed that the improved EKF algorithm enhances the estimation precision of the algorithm. Moreover, the physical data fusion model reduces the estimation error introduced by voltage and current measurements, overcomes the contradiction between the estimation accuracy and complexity of the equivalent circuit model method, improves the estimation accuracy of the SOC, and meets the application requirement that the estimation error must be less than 5%.
Abstract: Clustering is an important task in the field of data mining. Most clustering algorithms can effectively deal with the clustering problems of balanced datasets, but their processing ability is weak for imbalanced datasets. For example, K–means, a classical partition clustering algorithm, tends to produce a “uniform effect” when dealing with imbalanced datasets, i.e., the K–means algorithm often produces clusters that are relatively uniform in size when clustering unbalanced datasets with the data objects in small clusters “swallowing” the part of the data objects in large clusters. This means that the number and density of the data objects in different clusters tend to be the same. To solve the problem of “uniform effect” generated by the classical K–means algorithm in the clustering of imbalanced data, a clustering algorithm based on nearest neighbor (CABON) is proposed for imbalanced data. Firstly, the initial clustering of data objects is performed to obtain the undetermined-cluster set, which is defined as a set that consists of the data objects that must be checked further regarding the clusters in which they belong. Then, from the edge to the center of the set, the nearest-neighbor method is used to reassign the data objects in the undetermined-cluster set to the clusters of their nearest neighbors. Meanwhile the undetermined-cluster set is dynamically adjusted, to obtain the final clustering result, which prevents the influence of the “uniform effect” on the clustering result. The clustering results of the proposed algorithm is compared with that of K–means, the imbalanced K–means clustering method with multiple centers (MC_IK), and the coefficient of variation clustering for non-uniform data (CVCN) on synthetic and real datasets. The experimental results reveal that the CABON algorithm effectively reduces “uniform effect” generated by the K–means algorithm on imbalanced data, and its clustering result is superior to that of the K–means, MC_IK, and CVCN algorithms.
Abstract: Land resources are the fundamental and basic requirements for human survival and development as well as for the agricultural production and industrial construction. In recent years, due to the impact of industrial construction and chemical pollution, the cultivable land area is gradually decreasing, and the available agricultural land may be gravely insufficient for food production in the future. In China, the amount of abandoned mine land has increased significantly because of China’s national supply-side structural reform program. The abandoned mine land can be transformed into agricultural land to effectively alleviate food crisis and the contradictory relationship existing between people and land, and improve the ecological environment of mining area. Abandoned mine land refers to the land that has lost its economic value due to a series of production operations and also the land that has not been artificially restored to original conditions after mining. Abandoned mine land is a large, external, and unstructured environment with multiple obstacles and uncertainties and cannot be accessed by humans. Therefore, mobile robots are used to access those areas, and even for mobile robots, planning their coverage path in those areas is difficult. In this paper, the boustrophedon cellular decomposition (BCD) method and biologically inspired neural network (BINN) algorithm were combined to complete the coverage path planning of mobile robots on abandoned mine land. First, for the known environment of the abandoned mine land, the BCD method was used to make regional decomposition of the complex environment. The map with comprehensive complexity was decomposed into several subregions without any obstacles. Second, an undirected graph (i.e., a set of objects called vertices or nodes that are connected together, where all the edges are bidirectional) was constructed according to the adjacency relationship of the subregions, and the depth first search algorithm was used to determine the transfer order between subregions. Finally, the BINN algorithm was used to determine the internal walking mode of and the regional transfer path between the subregions. Simulation results show that the BINN algorithm is of higher efficiency than any other path planning algorithms used to solve the robot path transfer problem. Moreover, the proposed method in this paper could work in complex, unstructured environments to complete the coverage path planning of mobile robots.
Monthly, started in 1955 Supervising institution：Ministry of Education Sponsoring Institution：University of Science and Technology Beijing Editorial office：Editorial Department of Chinese Journal of Engineering Publisher：Science Press Chairperson：Ren-shu Yang Editor-in-Chief：Ai-xiang Wu ISSN 2095-9389CN 2095-9389
、
、
、
、
、
、
、
、
、
、
、
、
、
、
