The Automated Guided Vehicle (AGV) is crucial for intelligent transportation in textile workshops. Existing centralized system planning research on multi-AGV conflict detection is still limited to simple node or line segment detection, making it difficult to meet the needs of large-scale simultaneous AGV operation to quickly detect whether a conflict exists. Aiming at this problem, this paper designs an R-tree Line-segment Bilayer Conflict Detection mechanism (RLBCD) for efficient collision detection among multi-AGVs by introducing R-tree indexing and analyzing AGV path characteristics, and proposes an Improved Ant Colony Optimization algorithm (IACO) to enhance the quality of AGV path planning, with time as the objective for multi-AGV priority and path strategy design, which ultimately results in collision-free path planning for multiple AGVs. By setting up multiple sets of experiments, it is proved that the RLBCD is not only able to identify the common types of AGV conflicts but also reduces the times of conflict detection comparisons by at least 88.2% and 78.1% compared to the Conflict Based Search and the Grid Time-window Conflict Detection algorithm; the proposed overall algorithm is 29.59 s faster and reduces the cumulative turning angle by 450 degrees compared with the baseline multi-AGV path-planning algorithm, confirming its effectiveness.

To enhance the operational performance of mobile manipulators in textile workshops and address the difficulty of inverse kinematics (IK) for this class of redundant manipulators, this paper leverages the robot’s structural symmetries and proposes a chaotic-mutation particle swarm optimization (CMPSO)-based IK algorithm for mobile manipulators, thus simplifying the solution process and ensuring balanced exploration of the search space. First, the coordinate–transformation relationships of the mobile manipulator are analyzed to establish its forward kinematic model. Then, a multi-objective constrained IK model is formulated according to the manipulator’s operating characteristics. The model incorporates a pose-error function, the ‘compliance’ principle, and joint-limit avoidance. To solve this model accurately, we refine the population initialization and boundary-violation handling of the particle swarm algorithm and introduce an asymmetric mechanism via an adaptive mutation strategy, culminating in a CMPSO-based IK solver. On this basis, single-pose IK tests and trajectory-planning experiments are conducted, and simulation results verify the effectiveness and stability of the proposed algorithm.

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization.

To explore the influence of the lateral sloshing and the time -varying mass of the liquid in the tank on the ride comfort of the high -clearance sprayer, a spring -mass -damping equivalent mechanics that can describe the lateral sloshing of the liquid under different filling ratios was constructed based on the equivalent criterion. The Fluent was used to simulate the moment acting on the wall of the tank by the lateral sloshing of the liquid, and then the parameters of the equivalent mechanical model are obtained by fitting and solving. Comparative analysis of Fluent simulation and bench test on lateral sloshing of tank liquid under different filling ratios. The results show that the lateral sloshing trend of the tank liquid level obtained from the Fluent simulation and the bench test was consistent, which proved the accuracy of the Fluent fluid simulation process and the correctness of the required equivalent mechanical model parameters. Incorporating a liquid sloshing equivalent model, a fourdegree -of -freedom vertical dynamic model of the sprayer half -car was established. Subsequently, the performance of the sprayer was systematically analyzed and compared under the excitation of a bump road and a random E -level road. This investigation took into account varying liquid filling ratios of 10%, 50%, and 90%. The focus lay on evaluating the vertical acceleration of the sprayer body, dynamic deflection of the suspension, and dynamic load on the tires in response to these road conditions. This analysis is conducted independently of the liquid sloshing factor. The results show that the lateral sloshing of the liquid medicine significantly reduces the ride smoothness of the machine, and makes the vibration response of the machine produce a certain hysteresis effect. With the reduction of the quality of the liquid medicine in the spray tank, the vibration amplitude of the sprayer body gradually decreases, the hysteresis effect is also gradually weakened. The results presented in this study offer a theoretical foundation for the analysis of ride comfort and the optimization of chassis structure in highclearance sprayers.

Over the past 30 years, there has been continuous progress in global science and technology. However, many agricultural products still heavily rely on traditional methods of manual and mechanical harvesting, facing challenges such as high costs and low efficiency. To address these challenges, researchers have developed various harvesting robots to handle diverse tasks in complex farm environments. This study analyzed pertinent papers on harvesting robots retrieved from the Web of Science (WOS) core database and the China National Knowledge Infrastructure (CNKI) database, spanning the years 1993 to 2022. Using specialized software such as CiteSpace and VOSviewer, a bibliometric analysis was conducted to examine the research progress and hotspots in the field of harvesting robots. The analysis of 517 English papers indicated a continuous expansion in the research scale of harvesting robots. Furthermore, the research history can be divided into three distinct periods. Currently, research on harvesting robots is experiencing a rapid growth phase, with the number of related papers steadily increasing each year. In the year 2022 alone, 151 English papers were published. This growth is attributed to close collaborations among different countries/regions, institutions, and authors. China, the United States, and Japan play crucial roles in the research of harvesting robots. Notably, China has published 326 English papers, ranking first globally. Through analysis, it was also found that Chinese papers focused on harvesting robots earlier, thereby promoting the development of agricultural robots. Additionally, bibliometric analysis revealed that the research hotspots of harvesting robots mainly include system and structure design, object recognition and localization, and multi-robot coordination, among others. In the future, development trends of harvesting robots will focus on: 1) diversifying robot types, 2) expanding application scenarios,3) enhancing overall performance to reduce losses, and 4) reducing manufacturing costs. In conclusion, through a comprehensive bibliometric analysis, this study has provided valuable insights to advance the automation of harvesting.

To improve the workshop production efficiency of textile enterprises and balance the total operating time of all machines in each operation, this paper proposes a modified algorithm based on the combination of the ant colony optimization (ACO) algorithm and production products, which we call the product ant colony optimization (PACO) algorithm. The local pheromone update rule in the ACO algorithm is modified through the close relationship between textile machinery and production products in the textile workshop; the pheromone is then introduced into production products based on the constraints of the textile machine. A heuristic function is designed to improve the utilization rate of textile machines to increase the heuristic value of machines that are less frequently used in the algorithm iteration process. In addition, this paper combines the convergence speed and the global search ability of the algorithm with a designed variable pheromone evaporate parameter. The comparison among the initially designed PACO algorithm, the separately modified PACO algorithm, and the integratively modified PACO algorithm demonstrates that the proposed enhancement effectively addresses scheduling issues in textile flexible workshops and various workshops with similar constraint conditions.

In this study, an air-assisted mechanical seed-metering device for millet (Setariaitalica) was developed. The discrete element method (DEM) and response surface method (RSM) were used to research the influences of the side length, depth, and oblique angle of the shaped hole on the seeding performance (quality, multiples, and miss indices) of the seed-metering device, and the parameters of the shaped hole were optimized. Furthermore, after determining the size of the shaped hole, the influence of negative pressure on the quality index was studied under the condition of the higher rotational speed of the seed-sowing wheel. At the rotational speed of 20 r/min, the optimal values of the side length, depth, and oblique angle of the shaped hole were found to be 3.55 mm, 2.1 mm, and 109°, which resulted in a quality index of 94%. The optimal parameters were consistent with the simulated values and bench test values, with a relative deviation of 5.05%. Moreover, under the condition of a rotational speed of 40 r/min, the application of appropriate negative pressure to the seeds was found to promote seed entry into the shaped hole, thus significantly reducing the miss index and increasing the quality index. At the negative pressure of −90 Pa, the quality index was found to exceed 90%. These results provide a theoretical basis for future studies on a seed-metering device for millet (Setaria italica).

为了对苹果采摘机的适用性能进行有针对性的评价,构建采摘机适用性能的树状层次指标体系,并选择带有可拓区间的层次分析法和模糊综合评价法对其进行分析.借助可拓层次分析法求解各级指标因子的权重系数,利用可拓区间替代传统方法中的单一数值,确保主观评价中模糊不确定性的合理存在;借助模糊评价方法对建立的层次体系进行反推评价,保证整体评价过程的科学、客观与准确.计算表明,目标苹果采摘机的一级影响因素中采摘环境、底座、执行器、ECB的权重分别为0.075,0.170,0.326,0.429,整体评估分数为83.26,对应适用性能等级为Ⅱ级,说明采摘机工作性能良好,可以保障长时间工作.研究为智能果园苹果采摘提供技术支撑.

Aiming at the problems of high damage rate and loss rate of the existing safflower harvesting equipment, this study designed a rotary cutting safflower harvesting end effector by combining the growth characteristics and mechanical properties. Through force analysis of the cutting tool, the key factors, which affects the harvesting performance, were clarified to be the blade inclination and the knife shaft speed. The Fluent software was used to analyze the flow field of the harvesting chamber, which aims to determine the appropriate wind speed. To improve the working performance of the rotary cutting safflower harvesting end effector, a three-factor, three-level orthogonal test was carried out with the blade inclination, knife shaft speed and wind speed as the influencing factors, and with the recovery rate, damage rate and loss rate as the response indexes. A regression model for the three-factor interaction was developed and optimized based on the results of the Box-Behnken test. The optimal parameter combination is: the blade inclination is 15°, the knife shaft speed is 1570 r/min, and the wind speed is 6 m/s. A test of the optimization results showed that the recovery rate was 91.47%, the damage rate was 7.51%, and the loss rate was 4.67%. This study can provide theoretical basis and technical reference for the mechanized harvesting of safflower.

The metering device is the central part of the seeder discharging granular fertilizers or seeds from the hopper to the colter passing through the tube. Depending on the metering device design, the batches of particles are discharged or discharged evenly. This research analyzes existing metering devices, and a new pin-roller metering device is recommended to discharge evenly high doses of granular fertilizers and wheat seeds at low rotation speeds. The objective was to adapt the metering device to precision agriculture so that a little electric motor containing gearbox drives every metering device. Therefore, the pin-roller metering device parameters were investigated to apply high doses of granular fertilizers and wheat seeds evenly. The optimal pin positions were determined according to response surface methods (RSM) by simulating the granular fertilizers and wheat seeds' behavior on DEM. The coefficient of variation (CV) and the slip rate (SR) of the particles between the pins were chosen as indicators for evaluating the pin-roller. The shape of the pin was specified, and then the number of lines, the number of pins in a line, and the pin height were chosen to optimize. The analysis of the simulation results shows the optimal parameters: the number of pins in a line is four, and the number of lines is sixteen. The SR of granular fertilizers and wheat seeds were 8% and 2%, respectively. The pin-roller metering device is compared with the six-grooved and twelve-grooved metering devices. The comparison results show that the pin-roller metering device distributes twice more uniformly than other metering devices. The CV of the granular fertilizer distribution for six-grooved, twelve-grooved, and pin-roller metering devices was 111.13%, 80.74%, and 37%, respectively. The CV of the wheat seed distribution for twelve-grooved and pin-roller metering devices was 96%, and 37%, respectively. As long as the particles interact with the tube, leaving the metering device, the effect of tube type and position is investigated. As a result, it was determined that the tube has minimal effect on the pin-roller metering device while positively impacting the six-grooved and twelve-grooved metering devices, improving the CV of the particle distribution into the soil.

Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts of research were conducted as following. First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. Second, the recursive feature elimination (RFE) algorithm was utilized to screen the VIs and texture features derived from multispectral images: three Vis, including modified simple ratio (MSR), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI); three texture features, including correlation in the NIR band (corr-NIR), correlation in the red-edge band (corr-Red-edge) and homogeneity in the NIR band (hom-NIR), showed higher contribution to chlorophyll content estimation. Finally, a stacking model was constructed with K-Nearest Neighbor (KNN), a light gradient boosting machine (light-GBM), SVM algorithm as the base model and linear fitting as the metamodel, and four machine learning algorithms (SVM, KNN, light-GBM and stacking) were used to build the chlorophyll content estimation model suitable for different growing seasons. The results were: (1) The performance of the estimation model could be improved based on both VIs and texture features over using single-type features, and the stacking algorithm yielded the highest estimation accuracy with an R-2 value of 0.694 and an RMSE value of 0.553; (2) When FVC was added, the estimation model accuracy was further improved, and the stacking algorithm also produced the highest estimation accuracy with R-2 value of 0.739, RMSE value of 0.511 (3) When comparing modeling algorithms, stacking algorithms had greater advantages in the estimation chlorophyll content with potato plants than using single machine learning algorithms. This study indicates that taking into account the combination of VIs reflecting spectral characteristics, texture features reflecting spatial information and the FVC reflecting canopy structure properties can accomplish higher chlorophyll content estimation accuracy, and the stacking algorithm can integrate the advantages of a single machine learning model, with great potential for estimation of potato chlorophyll content.

A grinding method of fused glass is proposed through the dry chemical modification of atmospheric pressure plasma. Before grinding, the surface of fused glass is chemically modified by atmospheric pressure plasma, then the grinding experiment is carried out without any coolant. The objective is to explore the grinding characteristics of the dry chemically modified grinding by atmospheric pressure plasma. First, the experiment on the dry chemical modification of fused glass was carried out with different parameters of atmospheric pressure plasma. Secondly, a #600 diamond wheel was used to perform the grinding experiment with and without modification. Then, the element content, morphologies, and nanoindentation test of the dry chemically modified surface were investigated with reference to the parameters of atmospheric pressure plasma. Finally, the grinding roughness and morphologies were investigated with reference to the modified degree. It is shown that the modified degree of fused glass is influenced by the flow rate of CF4 and the feed speed of plasma. The mechanical property of material changes with the change of the modified degree, which plays a great influence on grinding processing. The grinding roughness can reduce by 32.7% with modification than one without modification. Moreover, the grinding roughness and morphology are related to the modified degree of fused glass. It is confirmed that the grinding processing of fused glass may be improved through the dry chemical modification of atmospheric pressure plasma.

Range issue has become the concern focus in the field of electric vehicles. In contrast to the generally used State-of-Charge (SoC), battery State-of-Energy (SoE) is regarded more appropriate in representing the remnant driving range by taking account of the voltage decline across the discharging process. In this paper, a SoE estimator is constructed using a pseudo power definition upon battery open-circuit-voltage (OCV) to exclude the energy loss on internal resistance; simultaneously, by combining with an equivalent circuit model (ECM), the unscented particle filter (UPF) is exploited to deal with problems of model nonlinearities, internal interferences, sensor noises and accumulated errors. Further, to adapt to battery time-variant features, the ECM parameters are on-line identified resorting to the recursive least square with forgetting factor algorithm. Finally, SoE estimation experiments using the proposed estimator on a LiFePO 4 battery show superior performance regarding robustness and accuracy against high-dynamic loads and various temperatures.

In this study, we established an effective finite element model for branch-stem-fruit systems to: (i) simulate responses under different loads; and (ii) intuitively predict the breaking process of branch-stem joints. The constitutive parameters of fruit branches and apple stalks were measured based on the orthotropic transverse constitutive model. Moreover, the maximum traction force, maximum traction displacement and specific fracture energy of the abscission zone were estimated based on the cohesive zone model (CZM). The experimental and simulation results of the branch-stem sample tensile process and horizontal pull test demonstrated the more effortless ability of the tangential load to detach the fruit. Moreover, compared with the horizontal pull tests and simulation results, the maximum relative deviation was less than 10%, revealing that the branch-stem-fruit finite element model can accurately reflect the process of fruit detachment. This research provides a reliable finite element model of the branch-stem-fruit system for future studies on apple harvesting through tree shaking or robotic apple harvesters.

A self-propelled crawler and potato harvester was designed, with the terrain characteristics of both hilly and mountainous areas considered, to address the low degree of mechanization, markedly low potato harvesting rate, and high labor intensity of potato harvesting in hilly and mountainous areas. The harvester could complete the tasks of digging potatoes, separating them from the soil, transporting potatoes, and collecting them in a single operation. Finite element analysis was conducted on major parts, such as the digging shovel and the frame, based on the overall structure and working principle of the harvester. A field experiment was then conducted. The results of the finite element analysis showed that the maximum stress of the digging shovel was 37.969MPa, the maximum strain was 1.846×10-4, and the total deformation was 0.8041mm. These measurements were within a safe range. The field experiment results showed that potato harvesting rate, bruising rate, and damage rate were 98.54%, 1.51%, and 1.31%, respectively that is, higher than the national standards for potato harvesters. The potato harvester exhibited reliable walking performance and harvesting performance, which could provide a reference for research on the mechanization of potato harvesting in hilly and mountainous areas.

六足仿生机器人因其灵活度好、可靠性高、适应性强等特点而得到广泛应用;针对六足仿生巡检机器人,从结构设计、步态规划、系统仿真和实物构建等方面,探索一般意义上系统设计和实现方法;首先设计了六足仿生机器人的多关节机械结构,并给出了此类系统的量化建模方法;然后采用了重心随动的三角步态规划方法,对系统稳定性和典型步态规划进行了量化分析;在此基础上基于标准D-H参数法建立了机器人的运动学模型,并且通过仿真实现了六足机器人向前纵向行走和向右横向行走的直线平稳运动;最后通过六足仿生巡检机器人实物测试,验证了所设计的结构和步态规划方法的可行性和有效性.

In order to improve the safety of agricultural vehicle in the field, we established a vehicle kinematics model for hanging agricultural tools, and comprehensively considered driving speed, the agricultural tool rotation radius, and vehicle movement trend to propose an agricultural vehicle field operation cross- boundary warning method based on a Robot Operating System (ROS). Furthermore, we designed a set of agricultural vehicle safety warning systems and employed Qt Creator to develop the agricultural vehicle warning system operation interface. Following this, a test platform was built based on the Oubao 4040 tractor and unilateral cross-boundary warning tests were conducted. Test results demonstrate the ability of the proposed cross-boundary warning system to: i) correctly determine the warning area at different speeds (low (3.6km/h±0.5km/h), medium (10.8km/h±1.0km/h) and high (18.0km/h±1.5km/h)) and driving paths ("V" and "U"-shaped routes); ii) and to prompt the operator in a timely manner. The proposed framework exhibits strong applicability and improves the safety of agricultural vehicle hanging agricultural tools

The application of fertilizer to the soil for the subsequent extraction by plants is a method to increase crop production. Fertilizer spreaders with an outer groove wheel (OGWFSs) have a large volume, and current metering devices are suitable for precision farming systems. However, the study of OGWFSs has shown that fertilizer metering is impaired due to sudden releases of fertilizer batches. Research has shown the performance advantage of using a pin-roller metering device whose pins are configured in the form of a truncated pyramid. This configuration not only provides highly uniform fertilizer discharge but is also advantageous for use with wet fertilizers and prevents clogging. In this study, an orifice was added between the pins to increase the working volume of the metering device and the amount of fertilizer discharge. The uniform distribution of the fertilizer particles and the amount of fertilizer discharge depends on the pin parameters. Therefore, the objective of this study was to determine the pin-roller parameters to increase the uniformity of the distribution and the amount of discharged fertilizer for variable-rate fertilizer applications. The pin-roller configurations that were investigated included the alpha angle, height of the roller pin, beta angle, and radius between two pins for six right and left entry lines. As a preliminary test, 25 pin-roller configurations were designed and evaluated for fertilizer distribution at three rotation speeds of the metering device. The uniformity of the fertilizer distribution was evaluated in a laboratory test, and the coefficient of variation was used as an evaluation index. A compound fertilizer, N-P2O5-K2O, was used in the experiment. The results indicated that different pin-roller characteristics of the metering device had significant effects on the uniformity of the distribution and discharge capacity of the fertilizer. Based on the results of the preliminary test, a new pin-roller design was developed. The performance of this pin-roller was evaluated and compared to that of the OGWFS. The coefficient of variation and the amount of discharged fertilizer was evaluated at three different rotational speeds. The discharge capacity of the new pin-roller was not as high as that of the OGWFS at all rotation speeds of the metering device, but the coefficient of variation was higher for the pin-roller than the OGWFS at lower speeds. However, there was no significant difference in the coefficient of variation between the metering devices at higher speeds.

In this paper, according to the design technical requirements, the completion of the structural scheme design of compound-driven in-pipe robot, the establishment of the cam active support diameter reducing mechanism mathematical model, obtain the relationship curve between its parameters, and completed the optimization of the cam contour. The resident performance of the robot in the pipeline is analyzed, and the feasibility of the robot's active resident in the pipeline is verified. This paper explores the relationship between the running speed of the robot in the pipeline and the leakage clearance under the condition of the fluid driven, and through the analysis and calculation of the different fluid velocity in the pipe, the reasonable range of the leakage clearance of the robot can be determined, for this range the robot can run smoothly. It has important theoretical value and engineering practical significance for the design and practical application of compound-driven pipeline robot.