To solve the path planning problem of mobile robots in complex orchard environments such as irregular and rugged terrain in hilly and mountainous areas, an improved grey wolf algorithm based 3D path planning method for mobile robots was proposed. By simulating the actual geographical environment, a three-dimensional orchard terrain and obstacle model was established, and a path planning objective function model was structed. By introducing the sparrow search algorithm (SSA) the initialization method, convergence factor, local search ability, and global search ability of the standard grey wolf optimization (GWO) were improved. The simulation experimental results show that the proposed algorithm has the advantages of fast optimization speed, optimal path planning distance, and fast convergence speed compared to other algorithms, indicating the effectiveness and superiority of the proposed method.

The sigmoid iteration ACO(ant colony algorithm) was optimized for the problems of poor environmental adaptability, high number of inflection points and high computational complexity that exist in the traditional ACO(ant colony algorithm)in route planning. Firstly, the Sigmoid activation function distribution strategy was adopted to improve the initial pheromone spread through the position of the mesh nodes, and the initial concentration of the pheromone was assigned by the sigmoid, which reduced the blindness of the algorithm’s pre-search. Secondly, the adaptive factor was introduced to dynamically regulate the heuristic function, which increased the degree of expectation of the ants in choosing the globally optimal node, and reduces the convergence time of the algorithm. Lastly, a statistical analysis was carried out in each generation of the ant, and the three characteristic parameters of ant path optimal, worst and average were extracted in each generation, and the pheromone updating function was dynamically adjusted according to the number of iterations to give full play to the parallelism characteristics of the algorithm. The results prove that the improved algorithm shortens the optimal path length by 2.7%, 3.2%, and 5.4%, reduces the average number of iterations by 42%, 53%, and 62%, and shortens the worst path length by 49%, 62%, and 73%, respectively, when compared with the ant colony system, the elite ranking algorithm, and the traditional ACO. The study prove that the optimized algorithm has stronger global optimality seeking ability and better application value.

The classical earth pressure theory only calculates the ultimate earth pressure in translational mode of the retaining wall. In order to get closer to the real engineering situation, it is necessary to develop the theory of non-ultimate earth pressure of the cohesive fill behind the wall under arbitrary load action and arbitrary displacement mode. The concept of equivalent uniform load was adopted to convert any type of load acting on the earth filling surface into uniform load. Based on the classical earth pressure theory, the rigid retaining wall was regarded as a combination of an ideal rigid plastic body and a series of springs, and the soil stiffness coefficient of the clay was obtained, which improved the Coulomb earth pressure theory and derives the general calculation method of the non-ultimate passive earth pressure of the soil in any displacement mode that can consider the arbitrary load.The research results indicate that the distribution and magnitude of passive soil pressure, the position of the resultant force point are closely related to the displacement mode of the retaining wall, the magnitude of cohesive force, and the load acting on the fill surface. When the equivalent uniform load and cohesive force are both 0, this method degenerates into Coulomb soil pressure theory.The model validation results show that the theoretical calculated values are in good agreement with the experimental measured values, proving that this calculation method has certain theoretical significance for calculating the passive soil pressure of cohesive soil under non limit states, and also has practical value for engineering practice.

To enhance the synchronization performance of multiple clocks in robotic communication systems, a study was conducted on a distributed clock synchronization compensation scheme. Quantitative compensation and dynamic compensation algorithms for clock errors were designed. The synchronization compensation scheme, based on the IgH EtherCAT communication protocol stack, was implemented, and trajectory tracking and communication performance tests were conducted on a six-axis robotic arm. The results show that the application of this synchronization scheme improves the circular trajectory precision of the robotic arm’s end effector compared to non-IgH synchronization schemes. The clock synchronization error is reduced to 54 ns, with the error tolerance within ±200 ns. It is evident that the compensation scheme enhances clock synchronization performance and meets the application requirements of the robotic arm.

A stepped-frequency radar transmission and reception system based on RF direct sampling technology was designed to address the issues of slow switching speed of sub pulses and incoherent phase of transmission and reception signals in stepped-frequency radar. This system performs RF direct acquisition of C-band radar signals, and all signal processing processes were carried out in the digital domain. It also had high integration and low power consumption. It was generated stepped-frequency signals by fast frequency hopping, frequency sweep of 1.8 GHz bandwidth within 7 ms was gotten. The system operates in the frequency range of 200~2 000 MHz with a frequency step of 2 MHz. At the same time, the parameters of the stepped-frequency signal in this system can be changed according to detection requirements. The test results show that the stepped-frequency radar system can achieve one-dimensional ranging function.

In response to the tasks of detection and maintenance in small-diameter pipeline transportation, a small-scale soft pneumatic pipeline crawling robot was designed and developed. The robot employed the technique of analytical implicit triply periodic minimal surface offset to form a solid structure with thickness, and achieved the anisotropy of structural stiffness by adjusting the parameters of implicit equations. Based on this, the radial and linear actuators were designed to meet the flexible motion requirements of the robot inside the pipeline. To optimize the performance of the robot, a joint parameter optimization framework based on MATLAB, Rhino, and ABAQUS was proposed, and an automated parameter optimization process was implemented using Python scripts. Through this framework, the motion performance and adaptability of the robot could be effectively improved. Based on two types of deformation actuators, the overall structure of the pipeline crawling robot was designed, and the manufacturing method and motion control gait were elaborated in detail. Experimental results show that the designed pipeline robot can stably move along the pipeline in two different postures and under certain load conditions, verifying the feasibility of the joint parameter optimization framework. The research results provide valuable references for the parameterized design and optimization research of subsequent pipeline crawling robots, which contributes to the application development of soft robots in the field of small-diameter pipeline detection and maintenance.

Aiming at the problem of frequent switching due to transient link changes in the heterogeneous network of indoor visible light communication (VLC) and radio frequency (RF) communication, a decision-making mechanism based on spatio-temporal characteristics for VLC/RF networks were proposed. Firstly, in the offline sampling stage, the database was constructed by processing and converting feature data such as spatial location, bandwidth, received signal strength, delay and signal-to-noise ratio of indoor grid points into proportional weights in the controller. Then, in the real-time decision-making phase, the terminal feeded the collected feature data back to the controller to determine the location of the user terminal by comparing it with the information in the database of the controller, and the prioritized link of the current location in the database was extracted and connected. Finally, the link decision model was used to derive the network communication index in real time, and the optimal link was solved by aggregating the proportional weights of the links in the database with the network communication index through the decision function, and the communication quality was improved using the resident timer. Simulation results show that the switching algorithm effectively reduces the average number of switching times and switching interruption probability compared with the traditional fuzzy logic switching algorithm and the vertical switching decision algorithm with equivalent signal-to-noise ratio (SNR). The algorithm can realize intelligent management of indoor heterogeneous networks and improve the quality of user experience.

Land use and traffic safety is a hot topic of mutual concern in the fields of urban geography and transportation. However, existing research on the impact of land use on pedestrian traffic accidents often incorporates a unified framework of built environment and mainly adopts measures such as land use mix or the proportion of land use types, lacking detailed analysis on land use types, thus making it difficult to translate their findings into actionable design strategies. Taking Yuzhong District, Chongqing City as an example, the land use types were finely characterized based on point of interest (POI) data, and the extreme gradient boosting (XGBoost) model was applied to explore the nonlinear relationships between land use types, pedestrian, road conditions, road environment, and the severity of pedestrian traffic accidents. The study finds these as follows. ①Land use types play an important role in the severity of pedestrian traffic accidents, with hospitals, residential areas, and educational land being the most influential. The presence of hospitals, residential areas, and educational land within a 300-meter buffer zone around accident sites reduces the severity of pedestrian traffic accidents. ②Road sections with curved and sloped road alignments are high-risk areas for severe pedestrian traffic accidents; entrances and exits of road sections, narrow road sections, and intersections have a mitigating effect on the severity of pedestrian traffic accidents. The findings provide policy insights for refined land use planning and governance to reduce the severity of pedestrian traffic accidents.

In order to reasonably and efficiently carry out the risk assessment of road tunnel construction collapse, the risk assessment model of road tunnel construction collapse was studied by rough set (RS), grid search method(GS) and support vector classification (SVC). Firstly, the index system of highway tunnel construction collapse risk evaluation was constructed by integrating advanced geological prediction. At the same time, the information of 100 tunnel collapse cases was collected and the index data was discretized. Secondly, attribute reduction was conducted based on the condition information entropy of rough set to obtain the reduced core index set. Then, grid search method was used to find the optimal parameters of the support vector classification training set, the risk assessment model of highway tunnel construction collapse based on rough set-grid search-support vector classification (RS-GS-SVC) was established. Finally, the model was used to predict the test samples. The results show that under the condition of the same learning sample, compared with rough set-genetic algorithm-support vector classification (RS-GA-SVC) model and Rough set-particle swarm optimization-support vector classification (RS-PSO-SVC) model, RS-GS-SVC model has higher classification accuracy; Under the same proportion of training set and test set, the prediction accuracy of RS-GS-SVC model is higher than that of GS-SVC model, with the accuracy rates of 93.33% and 90% respectively, and the operation time of RS-GS-SVC model is shorter. It can clearly be seen that the model complexity is effectively reduced and the classification accuracy is improved through the reduction of rough set conditional information entropy attributes.

Under the background that the accuracy of voltage amplitude detection is increasing year by year, a voltage amplitude detection method based on improved second-order generalized integrator was proposed to solve the problem that the existing voltage amplitude detection methods were susceptible to the interference of harmonic and DC components, which leaded to insufficient detection accuracy. First, the composition change of voltage after fault was analyzed, and the existence of harmonic component and DC component in fault voltage was proved. Then, the principle and performance of voltage amplitude detection by sine-cosine component method and traditional second-order generalized integral method were analyzed. Then, the output of traditional second-order generalized integrator without DC component was taken as the input of the improved second-order generalized integrator’s second module. In this way, the effective filtering of the DC component was realized, and the Bode diagram, zero pole and step response of the improved second-order generalized integrator under different gain coefficients were analyzed. The optimal gain coefficient was selected considering the influence of filtering effect, stability and response speed. Finally, the simulation results show that the method proposed in this chapter has stronger filtering ability and higher precision than the other two methods.