Kekun Zhang

Aiming at the high risk and low efficiency of traditional artificial inspection methods in the complex industrial scene, this study focuses on the inspection robot and conducts research on path planning through simulation and experiment. Firstly, according to the unstructured characteristics of complex industrial scenes, in order to reduce the loop error, the Lazy Decision algorithm is introduced to improve loop detection of Cartographer algorithm. Secondly, at the level of global path planning, a feasible path objective model under multiple constraints is constructed, and the genetic algorithm is reasonably improved through adaptive adjustment of crossover and mutation probabilities, design of composite selection strategy and node deletion operator, to enhance the efficiency and smoothness of global path planning. Then, at the level of local path planning, in order to improve the safety of robot driving, path point and trajectory pose point constraints are introduced into the TEB algorithm, and an obstacle evaluation sub-function is constructed to optimize its obstacle avoidance performance, achieving the collaborative fusion of global and local path planning. Finally, the simulation and experiment of path planning are carried out. The results show that the optimized Cartographer algorithm has higher mapping accuracy, and the relative errors in the simulation and experimental scenes are reduced by 34.25% and 45.09% respectively, which effectively alleviates the problem of map ghosting. The path length, number of inflection points, and planning time of the improved genetic algorithm are reduced by up to 16.32%, 59.10%, and 42.10%, respectively. The local path planned by the improved TEB algorithm can also follow the global path well and the safety distance from the turning point is increased by 41.37%. The fusion algorithm reduces path length and driving time by 31.91% and 23.76% respectively, balancing the safety and efficiency of path planning for the inspection robot.

The core risks of unmanned truck formations in narrow curved sections of open-pit mines mainly include lateral trajectory deviation risk and longitudinal following coordination risk, which can lead to inaccurate heading angle and improper following control. Therefore, a multi-vehicle formation collaborative control method based on LQR and DMPC is proposed. The research objective is to design a collaborative control strategy that is suitable for narrow curved sections of open-pit mines, achieving precise lateral trajectory tracking and safe longitudinal following. Firstly, a multi-vehicle collaborative control scheme is designed by analyzing the transportation conditions. Secondly, the lateral dynamics model and the preview error model are established. Besides, the lateral controller based on feedforward LQR is designed. Then, based on the longitudinal dynamic characteristics of the vehicle, the multi-vehicle formation longitudinal controller based on DMPC is designed. Next, the multi-vehicle formation collaborative controller is established. Finally, the Simulink/TruckSim joint simulation model is constructed, and the unmanned experimental vehicle and open-pit mining experimental platform are designed for conducting simulations and experiments. The simulation results show that collaborative control reduces the lateral error of the following vehicle by 37.20% and the heading angle error by 36.40% when driving downhill with no load. Furthermore, collaborative control reduces the lateral error of the following vehicle by 21.95% and the heading angle error by 39.54% when fully loaded uphill. At the same time, the longitudinal following vehicle maintains a speed error of no more than 0.35 km/h and a spacing error of less than 0.30 m compared to the leading vehicle. The experimental results show that the designed formation controller controls the lateral error of the following vehicle within ±0.035 m and the heading angle error does not exceed 0.15 rad, verifying the effectiveness and adaptability of the studied formation control method.

Aiming at the practical problems such as complex technology and large cost investment existing in the traditional environmental perception method in obtaining the operating status of underground unmanned vehicles, an innovative perception method of key operating status for underground unmanned vehicle based on dynamic wind field in roadway is proposed, which is verified by simulation and experiment. Firstly, the typical roadway airflow structure is analyzed based on the restricted jet theory, and the wind field disturbance element model of roadway-vehicle system is constructed based on the roadway piston wind effect. Secondly, the distribution characteristics of roadway airflow are analyzed through fluid simulation software. Then, the axial and radial wind field structure of the roadway containing the unmanned vehicle is simulated and analyzed, and the internal relationship between the dynamic wind field and the key operating status of unmanned vehicle is revealed. Finally, the experimental scene of circular wind tunnel is built, and the perception of the safe and abnormal operating status for the unmanned vehicle is effectively realized based on the dynamic wind field information. The experiment results verify the rationality and feasibility of perception method of key operating status for underground unmanned vehicle based on dynamic wind field in roadway, which can make up for the limitations of the existing roadway environment perception technology, and help to improve the operating safety of underground unmanned vehicles.

The current research on underground handling robots is still in its infancy, and there are still obvious deficiencies in the basic fields such as innovative design of handling mechanisms, trajectory planning of transport space, and autonomous walking dynamic obstacle avoidance. Aiming at the practical problems of many transport links, difficult transport operation and slow manual transport in coal mine, a new multi-functional handling robot and its supporting standardized loading container and delivery platform are designed, and the trajectory planning method of roadway transport space is carried out through simulation and experiment. Firstly, the standardized loading container and delivery platform suitable for robot loading and unloading are designed, and the autonomous transport strategy of handling robot is formulated. Secondly, the multi-functional handling mechanism of underground heavy loading handling robot is designed, and its mechanical properties are simulated and analyzed. Thirdly, the improved D-H modeling method and the forward and inverse kinematics modeling method are used to improve the reliability of the trajectory planning of the transport robotic arm. In addition, an obstacle evaluation subfunction is introduced to improve the DWA algorithm by measuring safety and efficiency. Finally, the model prototype of the handling robot is built, and transport roadway scene in the coal mine is constructed according to the ratio of 1:10. The simulation experiment of space trajectory planning is carried out in the laboratory. The test results show that the repeated positioning error of the robotic arm does not exceed 0.0375 mm. The average improvement of path length and walking time are 31.27% and 42.33%, respectively, and the walking speed of the robot is increased by 19.18% on average. Real time and effective dynamic obstacle avoidance is achieved during the walking process, and good smoothness and efficiency are demonstrated during the handling process.

In order to realize the lane-changing maneuver of Connected Autonomous Vehicles (CAV), a lateral controller based on model predictive control is developed with the three degrees of freedom vehicle dynamic model. Then the controller is synthesized to track the reference trajectory fitted by the quintic Bézier curve. The controller is validated by MATLAB/CarSim under different road adhesion conditions and driving speeds. Results show that for different road adhesion conditions and driving speeds, the controller does not need to adjust the control parameters and can continuously correct the deviation from the expected trajectory. During the tracking process, the front wheel angle, front wheel angle increment, centroid side deflection angle, and front wheel side deflection angle are kept within the limited constraint range. The established control algorithm has good control robustness and tracking driving stability. The research can provide a theoretical basis and technical support for lane-changing safety and control of CAV.

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

With the technical support of an intelligent networking environment, autonomous driving technology is facing a new stage of development, and the decision-making behavior of autonomous vehicles is changing fundamentally, so it is urgent to explore the lane-changing decision-making behavior mechanism of autonomous driving. Firstly, through the analysis of system similarity, the similarity between autonomous vehicles and moving molecules is sought, and the attraction and repulsion between molecules are applied to the lane-changing process of vehicles to effectively recognize the traffic scene of lane-changing vehicles. Secondly, the molecular interaction potential is introduced to unify the attraction and repulsion, and explore the dynamic influencing factors of lane-changing behavior for vehicles. Moreover, we systematically analyze the interaction relationship in the lane-changing process of Connected and Automated Vehicles, and establish the molecular interaction potential lane-changing model to explore the lane-changing decision-making behavior mechanism. Furthermore, we study the impact of micro lane-changing behavior on macro traffic flow. Finally, the SL2015 lane-changing model and the molecular interaction potential lane-changing model are compared and analyzed by using the SUMO platform. The results show that the speed fluctuation of Connected and Automated Vehicles based on the molecular interaction potential lane-changing model is reduced by 15.5%, and the number of passed vehicles is increased by 3.26% on average, which has better safety, stability, and efficiency. The molecular interaction potential modeling of lane-changing decision-making behavior for Connected and Automated Vehicles comprehensively considers the interaction relationship of dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of Connected and Automated Vehicles.

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety warning system to enable pedestrians to cross the street intelligently. The model developed to consider human–vehicle characteristics improves the parking sight distance and pedestrian crossing safety psychological distance models by adding consideration of the effect of vehicle size and type on pedestrian psychology. The established model considering human–vehicle characteristics was improved for the stopping sight distance and pedestrian crossing safety psychological distance models. The effects of vehicle size and type on pedestrian psychology were taken into account. The designed warning system can be divided into a detection module, control module, warning module, and wireless communication module. The system detects the position and speed of pedestrians and vehicles and discriminates the conflict situation, executing the corresponding warning plan for three different types of situations. The system provides warning to pedestrians and vehicles through the different color displays of the intelligent crosswalk. The results show that the proposed model, which synergistically couples vehicle speed, driver reaction time, road characteristic correlation coefficients, and the psychological impact of vehicle size and type on pedestrians, is safe and effective. The designed system solves the problem of pedestrian crossing safety from both theoretical and technical aspects.

Autonomous driving vehicles have some advantages, such as alleviating tasks of drivers and reducing carbon emissions. With the advancement of intelligent network connection technology, autonomous driving vehicles are showing a trend of practicality and popularization, so it is crucial to study the decision-making mechanism of autonomous driving vehicles. This paper focuses on the lane-changing decision-making behavior of autonomous driving vehicles. Firstly, the objective quantification of lane-changing intention is carried out to reasonably show the lane-changing intention of autonomous driving vehicles as a prerequisite for lane-changing decision-making. Besides, the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced, and explore the dynamic influencing factors of lane-changing process for autonomous driving vehicles. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous driving vehicles is established. Analyze the decision-making behavior mechanism of lane-changing, so that the autonomous driving vehicle can change lanes safely and reasonably. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that under the decision-making behavior model of the lane-changing game, the average speed of vehicles increases by 3.6%, and the average number of passed vehicles increases by 10.3%, which has higher stability, safety, speed gains, and lane utilization. The modeling of lane-changing game strategy for autonomous driving vehicles comprehensively considers the dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of autonomous driving vehicles.