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The dual-arm cutting robot addresses the low efficiency of traditional single-arm roadheaders when cutting large cross-sections. However, its dynamic interaction with coal-rock affects control performance. In current studies, both arms of the dual-arm cutting robot interact with the same object, forming a closed kinematic chain, which fails to meet the control requirements for independent arm movement and the output force of each cutting head. To solve this issue, a force-position hybrid control system based on the robot’s relative dynamics model was designed. The kinematic and dynamics models of the dual-arm cutting robot were established, with the relative dynamics model derived using the robot’s relative Jacobian matrix and principles of virtual displacement and virtual work. This model used a single variable to describe the motion states of both arms, integrating their independent dynamics models into a unified one. Based on this relative dynamics model, a force-position hybrid control system was developed for the robot’s dual arms, with system stability and feasibility verified via the Lyapunov function. Simulation results indicated that the dual-arm cutting process had a larger workspace compared to single-arm cutting, allowing for efficient large cross-section cutting. The force-position hybrid control system enabled synchronized tracking of expected relative position and force, with the absolute error in tracking the target cutter position kept within 0.3132 m and a root mean square error of 0.1447 m.

The shield type temporary support robot is an important component of the intelligent excavation robot system for large section roadways that adapts to the coexistence of dirt and debris. Its main function is to provide a safe and reliable workspace for achieving 'parallel excavation and support' operations. In order to enhance the safe and stable support of the shield type temporary support robot for surrounding rock during its pushing and driving process, based on the structure, working environment, and operational requirements of the shield type temporary support robot, a mathematical model of its pushing amount and support force during pressurized driving, as well as a dynamic model of pressurized driving, are established. A hydraulic control system for the shield type temporary support robot driving under pressure is designed. The system mainly consists of a support hydraulic system and a driving hydraulic system. During static support, the support hydraulic system needs to constantly output a support force greater than the weight of the upper shield body itself, and the driving hydraulic system is in standby mode. When driving under pressure, the support hydraulic system and the driving hydraulic system work simultaneously, ensuring that the temporary support robot 'reducing stress without leaving the roof' while steadily moving forward with the roof under pressure at all times. A precise control method for shield type temporary support robot driving under pressure based on fuzzy PID is proposed. The pressure and displacement signals of the temporary support robot are collected in real time by displacement sensors integrated on the displacement cylinder and pressure sensors in the hydraulic circuit. The signals are used to reflect the changes in support force and driving displacement during the temporary support robot's driving under pressure. Based on the error and error rate of the support force and displacement, the fuzzy PID algorithm is used to modify the control parameters of the support force and displacement, achieving reliable control of driving under pressure based on the fuzzy PID algorithm. Both simulation and experimental results show that the effect of fuzzy PID control is superior to traditional PID control. Under fuzzy PID control, the relative error of support force during the pushing and driving process of the shield type temporary support robot is less than 1%, and the driving displacement error is less than 2 mm. Moreover, the response speed of support force and pushing amount control is fast, ensuring the safe and stable support of the surrounding rock during the pushing and driving process.

In the process of path planning for tracked directional drilling rigs in coal mines, there are constraints on the body volume and the demand for driving efficiency in actual scenarios. However, the commonly used A* algorithm has slow search speed, multiple redundant nodes, and the planned path is close to obstacles and has poor smoothness. This study proposes a path planning algorithm for coal mine tracked directional drilling rigs, which uses the improved A* algorithm to plan global paths and integrates the dynamic window approach (DWA) to plan local paths. Considering the influence of directional drilling rig size, a safety extension strategy is introduced in the traditional A* algorithm. The safety distance constraints are added between the directional drilling rig, roadway walls, and obstacles to improve the safety of the planned path. Adaptive weighting is applied to the heuristic function of the traditional A* algorithm, while incorporating the influence of the parent node into the heuristic function to improve the efficiency of global path search. The principle of obstacle detection is used to eliminate redundant nodes in the path planning of the improved A* algorithm. The segmented cubic Hermite interpolation is used for quadratic smoothing to obtain the global optimal path. The improved A* algorithm is integrated with DWA for path planning of directional drilling rigs in coal mines. Matlab is used to simulate and do comparative analysis of directional drilling rig path planning algorithms under different working conditions.The results show that compared with Dijkstra algorithm and traditional A* algorithm, the improved A* algorithm accelerates the search speed while ensuring a safe distance. It reduces search time by 88.5% and 63.2% respectively, and to some extent shortens the length of the planned path, making the path smoother. The improved A* algorithm and DWA fusion algorithm can effectively avoid unknown obstacles on the path planned by the improved A* algorithm. The path length is reduced by 5.5% and 2.9% compared to the paths planned by the PRM algorithm and RRT * algorithm, respectively.

Traditional belt conveyor abnormal state recognition uses manual inspection or mechanical comprehensive protection system for detection. The manual inspection is labor-intensive, inefficient, and difficult to accurately detect faults. Mechanical comprehensive protection system is prone to misjudgment and poor recognition effect. The above methods can no longer meet the needs of coal industry intelligence. With the development of machine vision, deep learning, and industrial Ethernet technology, video AI technology has become a research hotspot for intelligent recognition of abnormal states of coal mine belt conveyors. This paper analyzes the current research status of using video AI technology to identify abnormal states of coal mine belt conveyors, such as belt deviation, idler failure, personnel invasion, unsafe behavior of personnel, coal stacking, and foreign objects. It is pointed out that there are three main problems in the current video AI recognition technology for abnormal states of coal mine belt conveyors: long construction time-consumption of video image datasets, low precision of abnormal state recognition, and large time delay in video information transmission. To address the issue of long construction time-consumption of video image datasets, a solution is proposed to strengthen the research on video AI recognition algorithms based on semi supervised, unsupervised, and small sample learning, and to expand the dataset based on generative models. To address the issue of low precision of abnormal state recognition, a solution is proposed to strengthen research on data deblurring methods, and to utilize algorithms such as generative adversarial networks to balance positive and negative samples, and improve AI recognition algorithms. To address the issue of large time delay in video information transmission, a solution is proposed to build a 'cloud-edge-end' collaborative video AI recognition system architecture for abnormal states of belt conveyors, and to deploy a high bandwidth and low time delay network communication system. This article looks forward to the development trend of video AI recognition technology for abnormal states of belt conveyors from four aspects: high-performance video AI recognition algorithms, high bandwidth and low time delay video communication technology, 'cloud-edge-end' efficient collaborative video AI recognition system, and sound video AI recognition technology standards.

The advantages and disadvantages of five commonly used mining monorail cranes, namely explosion-proof diesel engine monorail crane, explosion-proof battery monorail crane, pneumatic monorail crane, rope traction monorail crane, and cable dragging monorail crane, are analyzed. It is pointed out that explosion-proof diesel engine monorail crane and explosion-proof battery monorail crane have been widely used for underground equipment and material transportation in coal mines due to their strong transportation capacity. The explosion-proof battery monorail crane has green and clean features. It has gradually become a key research and development equipment for underground auxiliary transportation in coal mines. This paper analyzes the research status of the mining monorail crane system from four aspects of battery drive, positioning, measurement and control, and information transmission. It is pointed out that the current mining monorail crane system has low automation and intelligence levels. To achieve intelligent transportation of the monorail crane, it is necessary to study the key technologies such as lithium battery explosion-proof and battery management of the monorail crane, global and local fusion precise positioning, multi-sensor information fusion intelligent measurement and control, edge computing information transmission, etc. For the explosion-proof and battery management technology of lithium battery, a topology optimization design method of explosion-proof large capacity lithium battery structure based on variable density method, and a balanced management scheme based on improved sparrow search algorithm/genetic algorithm are proposed. For precise positioning technology, a global positioning method based on inertial navigation and odometer fusion, and a local positioning method based on visual+UWB combination are proposed. For intelligent measurement and control technology, an intelligent detection method based on recursive least squares algorithm/second-order approximate extended Kalman filter , the real-time construction method for follow-up electronic fences, and vector control method based on fuzzy rules are proposed. For the information transmission technology, the 'local-short range-ground' communication system architecture based on the fusion of 5G network and the distributed data calculation and transmission method based on edge computing are proposed. The proposal of key technical issues and solutions for intelligent monorail cranes in coal mines provides a new approach to accelerate the development of intelligent monorail cranes in coal mines.

The problem of low detection accuracy of driving displacement exists in the driving process of anchor digging robots. In order to solve the above problem, taking the supporting bolt as the positioning benchmark, by analyzing the distance relationship between the anchor digging robot and the supporting bolt, the positioning model of 'anchor digging robot-supported anchor' is established. This paper proposes a binocular vision-based displacement detection method for anchor digging robots. Due to the complexity of the underground coal mine environment, the disparity map obtained by using the traditional Census transform algorithm has limitations. By analyzing the binocular vision ranging principle, an improved Census transform algorithm is proposed to obtain the disparity map of the anchor and the depth information of the anchor image. This paper presents a method of anchor feature recognition and positioning, and uses edge detection algorithm to extract the anchor contour in disparity map. The minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm are used to frame the anchor outline and extract the pixel coordinates of anchor feature points. By analyzing coordinate conversion relationships, the pixel coordinates of feature points are converted to world coordinates. By using the least square method, the spatial coordinates of feature points are fitted into a straight line. The plane parallel to the roadway section is established through the straight line. The distance between the binocular camera and the plane is calculated, and then the distance between the anchor digging robot and the plane is obtained. A mobile robot platform is set up to carry out the displacement detection experiment of the anchor digging robot. The results show the following points. The improved Census transform algorithm reduces the mismatch rate from 19.85% to 11.52%, which is 41.96% lower than the traditional Census transform algorithm. The method of anchor feature point recognition and positioning can effectively extract the spatial coordinates of anchor feature points. The distance between the camera and the three parallel sections is 3 010.428, 2 215.910, 1 415.127 mm respectively through straight line fitting. In the robot positioning experiment, the real calculated displacement is compared with the theoretical displacement. The results show that the real calculated displacement curve coincides with the theoretical displacement curve basically. The error between the theoretical displacement and the calculated displacement is less than 20 mm. The autonomous, accurate and real-time displacement detection of the anchor digging robot can be realized.

Machine vision has a certain theoretical basis in target detection and recognition for sorting robot of coal mine belt conveyor. But current target recognition of sorting robot of coal mine belt conveyor is mainly aimed at coal-gangue recognition. There are few kinds of research on the recognition of foreign object targets causing conveyor belt penetration and tearing, and also few kinds of research on the precise positioning of target foreign object. In order to solve the above problems, a foreign object recognition and positioning system based on machine vision for sorting robots of coal mine belt conveyor is designed. The system can recognize and position different types and shapes of foreign objects on the conveyor belt. The image information of the foreign objects on the conveyor belt in real-time is obtained by adopting binocular vision, and the image is preprocessed. Image information is enhanced based on the Canny operator. The gray stretching method is used to improve image edge information to highlight the edge features of foreign objects on coal mine belt conveyor. The morphological method is used to extract foreign object shape features, and establish foreign object image feature sample library. The image feature matching method is used to solve the existing area of foreign objects to realize the detection, classification and recognition of foreign objects. On the basis of the successful recognition of foreign object type, the region of interest (ROI) of the target foreign object is established based on the edge feature value of the target foreign object. The coordinate conversion relationship is built between the camera, conveyor belt and target foreign object. The fast multi-target centroid calculation method is used to obtain the centroid coordinate of the target foreign object, so as to realize the positioning of the target foreign object. The experimental result of the system prototype shows that the foreign object recognition rate of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor is not affected by the size, material, color and other factors, the system can realize the image acquisition, process, feature extraction, recognition and positioning of the target foreign object of coal mine conveyor belt. The recognition rate is above 92.5%, and the average error of the target foreign object positioning is about 3%.

The coal flow of the belt conveyor will be mixed with anchor rod, angle iron, wood, gangue, and lump coal. This will easily lead to the tearing of the conveyor belt, the blockage of the transition and even the breakage of the belt. It is difficult for the inspection robot of the belt conveyor to efficiently and accurately recognize foreign objects in the environment of uneven lighting and high-speed running of the belt conveyor. The model deployment is inconvenient. The YOLOv7 model has a high capability to extract target features, but its recognition speed is slow. In order to solve the above problems, a foreign object recognition method of belt conveyor in coal mine based on improved YOLOv7 is proposed. The method of adaptive histogram equalization with limited contrast is used to enhance the collected monitoring image of the belt conveyor to improve the clarity of object contour in the image. The YOLOv7 model is improved by introducing a simple and parameter-free attention module into the backbone extraction network. The improved model can improve the model's anti-interference capability against the complex background of the image and the capability to extract foreign object features. The depthwise separable convolution is introduced to replace the ordinary convolution in the backbone feature extraction network to improve the speed of foreign object recognition. TensorRT engine is used to convert the improved YOLOv7 model after training and deploy it on NVIDIA Jetson Xavier NX, realizing the acceleration of the model. The video of the belt conveyor with the resolution of 1 920 × 1 080 in the underground coal mine is recognized. The experimental results show that the recognition effect of improved YOLOv7 is better than YOLOv5L and YOLOv7. The recognition accuracy rate is 92.8%, and the recognition speed is 25.64 frames/s, meeting the requirements of accurate and efficient recognition of foreign objects in the belt conveyor.

Multi-rotor aircraft has a good application prospect in coal mine production inspection because of its advantages of simple structure, hovering and multi-directional flight. However, multi-rotor aircraft moves at a high speed, and the aircraft is easily influenced by various external factors during flying. It is difficult to establish a precise mathematical model. The design of a flight control algorithm is complicated. The existing synchronous positioning and map construction method based on laser radar is difficult to meet the real-time requirement of rapid flying of the multi-rotor aircraft. In view of the above problems, an obstacle avoidance control method of multi-rotor aircraft in coal mine by using remote virtual control technology is studied. The virtual remote control system of the multi-rotor aircraft in coal mine underground roadway is constructed. The virtual roadway model and the global navigation map are established in the virtual remote control system according to the initial information of the coal mine roadway. The known static obstacle information in the moving process of the aircraft is obtained, and the known static environment model is established. The task quantity of environment perception modeling in the moving process of the multi-rotor aircraft is reduced, and the operation efficiency of virtual remote control can be improved. In the inspection process, the multi-rotor aircraft detects dynamic obstacle information in the moving direction through sensing equipment carried by the multi-rotor aircraft. The remote control system reconstructs the dynamic obstacle information in an initial virtual roadway model in real-time according to obstacle data. The virtual environment state is updated in real-time to provide a reliable environment basis for local obstacle avoidance control of the aircraft. The remote control system uses the compound virtual force field (CVFF) obstacle avoidance control algorithm to plan the obstacle avoidance path by reading the positioning data and moving speed information of obstacles and aircraft. If the obstacle in front is detected to pose a great threat to the movement of the aircraft, the remote controller can implement remote intervention on the aircraft according to the planned obstacle avoidance path. The system realizes autonomous obstacle avoidance flight and human remote intervention control. In order to improve the perception efficiency and accuracy of aircraft to dynamic obstacles, a CVFF obstacle avoidance control algorithm is studied based on virtual force field (VFF) algorithm by introducing the influence of relative velocity between aircraft and obstacles and target points. The CVFF obstacle avoidance control algorithm is verified by simulation from two aspects of static and dynamic obstacle avoidance paths. The results show that under static conditions, compared with the VFF algorithm, the CVFF algorithm reduces the number of iterations and also shortens the trajectory length of the aircraft. Under dynamic conditions, the aircraft successfully avoids the two dynamic obstacles set in advance and successfully reaches the set target point. The effectiveness of the obstacle avoidance control method of multi-rotor aircraft using the CVFF algorithm is verified.

The precision and computational efficiency of coal quantity measurement for belt conveyors based on linear laser stripes are low. There is a trailing phenomenon during belt operation, as well as data misalignment caused by deviation and drifting. In order to solve the above problems, a coal flow volume measurement method based on linear model partitioning is proposed. Firstly, the method uses a high-speed line laser camera to collect coal flow data. Secondly, a point cloud registration algorithm based on linear model partitioning is used to fuse the point cloud data at the bottom of the belt with the surface data of the coal flow, forming a complete three-dimensional coal flow data. Finally, the coal flow volume measurement is achieved through a coal flow volume measurement model. The experimental results show that the coal flow volume measurement method based on linear model partitioning has a precision of over 95% when measuring rough surface iron blocks, smooth surface iron blocks, and physical objects (coal and gangue) in high dust environment, coal flow surface watering environment, dim environment, and normal lighting environment. Moreover, the measurement precision of smooth-surface iron blocks is higher than that of rough-surface iron blocks in four simulated environments. It indicates that the better the flatness of the object surface, the higher the measurement precision. The environment has little impact on measurement precision. The actual test results show that the coal flow volume measurement method based on linear model partitioning has a measurement precision of over 97%. The corresponding average time is within 80 ms. Compared with the measurement method based on the KD tree, the overall precision has been improved by more than 6% and the processing timeliness has been doubled.

In order to solve the problem of autonomous deviation control of roadheader in complex roadway environment, the paper analyzes the deviation reasons of roadheader, defines the functional requirements of deviation correction control of roadheader, proposes a deviation correction control system of coal mine roadheader based on digital twin, and introduces the system composition. Taking the roadheader central position control as an example, the system deviation correction control mechanism is analyzed, and a deviation correction control method of the roadheader based on binocular vision image information is proposed. Taking the roadway image detected by binocular vision as the basic data, by extracting the characteristics of the roadway image and analyzing the relationship between the roadway coordinate system and the roadheader coordinate system, the position and attitude parameters of the roadheader relative to the roadway space are calculated, and the deviation correction control of the roadheader is carried out according to the solution results. The digital model and the positioning and orientation parameter database of the roadheader and the roadway are constructed, and the virtual remote deviation correction control of the roadheader is realized through the virtual-real mapping relationship. The experimental results show that the deviation correction control system based on digital twin can compensate the yaw angle and offset distance of the roadheader under different working conditions effectively. The deviation correction process can be displayed on the monitoring interface in real time, and the simulation results of deviation correction path planning are consistent with the actual working conditions.

In view of problem that flight path and trajectory of four-rotor flying robot in coal mine roadway are changeable, which leads to great redundancy of environment information data collected by the robot in dynamic space, an environmental information data compression algorithm for coal mine four-rotor flying robot is proposed. The default threshold noise reduction method of wavelet analysis is used to reduce the noise of original data to improve signal-to-noise ratio; taking methane monitoring as an example to introduce data compression algorithm, the roadway space is split into several sections along the roadway direction, and the effective value of methane concentration of each split section is selected and used to reconstruct the detection data of methane concentration. The experimental results show that the methane concentration signal compressed and reconstructed by the algorithm has little error with the manual detection result, the data compression algorithm can extract the effective value of methane concentration data and remove the redundant environmental information collected by the robot, thereby improving the effectiveness and real-time performance of data transmission.

The imbalance between excavation and mining is significant as it restricts the efficient development of coal resources. Slow tunneling speed is primarily due to the inability to concurrently conduct excavation and permanent support operations, and temporary support is considered a key solution to this problem. However, the mechanism by which temporary support affects the surrounding rock in unsupported are as remains unclear, hindering the assurance of stability in these areas and the determination of a reasonable unsupported span. To address this issue, this work proposed a stress distribution model as temporary support, elucidating the distribution law of support forces within the surrounding rock. By analyzing the stress differences between areas with and without temporary support, the stress field distribution characteristics of temporary support were determined. Subsequently, the evolution of stress and strain in the surrounding rock within unsupported areas was analyzed concerning changes in temporary support length, support force, and unsupported distance. The results indicated that, although temporary support does not directly act on unsupported areas, it still generates a supportive stress field within them. The maximum unsupported distance should not exceed 3 m, and there is a strong linear relationship between the optimal temporary support force and the unsupported span. Furthermore, the length of temporary support should not exceed 17 m from the tunnel face. The successful application of the shield tunneling robot system verifies that temporary support can ensure the stability of the surrounding rock in unsupported areas, confirming the validity of the temporary support stress distribution model. This research can be used to design and optimize cutting parameters and temporary support parameters, arrange equipment, and design and optimize tunnel excavation processes to achieve safe and efficient tunneling. [ABSTRACT FROM AUTHOR]

At present, China's coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberate productivity, this paper further researches the trajectory planning of the drilling and anchoring materials of the robotic arm for the drilling machine "grasping–carrying–loading–unloading" on the basis of the drilling and anchoring robotic system designed by the team in the previous stage. Firstly, the kinematic model of the robotic arm with material was established by improving the D-H parameter method. Then, the working space of the robotic arm with the material was analyzed using the Monte Carlo method. The singular bit-shaped region of the robotic arm was restricted, and obstacles were removed from the working space. The inverse kinematics was utilized to solve the feasible domain of the robotic arm with material. Secondly, in order to avoid blind searching, the guidance of the Bi-RRT algorithm was improved by adding the target guidance factor, and the two-way tree connection strategy for determining the feasible domain was combined with the Bi-RRT algorithm's feasible domain judgment bi-directional tree connection strategy to improve the convergence speed of the Bi-RRT algorithm. Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. Finally, a simulation environment was built in a ROS environment to compare and analyze the planning effect of different algorithms. The simulation results showed that the improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field improved the optimization speed by 69.8% and shortened the trajectory length by 46.6% compared with the traditional RRT algorithm. [ABSTRACT FROM AUTHOR]

In response to the challenges of multiple personnel, heavy support tasks, and high labor intensity in coal mine tunnel drilling and anchoring operations, this study proposes a novel tracked drilling and anchoring robot. The robot is required to maintain alignment with the centerline of the tunnel during operation. However, owing to the effects of skidding and slipping between the track mechanism and the floor, the precise control of a drilling and anchoring robot in tunnel environments is difficult to achieve. Through an analysis of the body and track mechanisms of the drilling and anchoring robot, a kinematic model reflecting the pose, steering radius, steering curvature, and angular velocity of the drive wheel of the drilling and anchoring robot was established. This facilitated the determination of speed control requirements for the track mechanism under varying driving conditions. Mathematical models were developed to describe the relationships between a tracked drilling and anchoring robot and several key factors in tunnel environments, including the minimum steering space required by the robot, the minimum relative steering radius, the steering angle, and the lateral distance to the sidewalls. Based on these models, deviation-correction control strategies were formulated for the robot, and deviation-correction path planning was completed. In addition, a PID motion controller was developed for the robot, and trajectory-tracking control simulation experiments were conducted. The experimental results indicate that the tracked drilling and anchoring robot achieves precise control of trajectory tracking, with a tracking error of less than 0.004 m in the x-direction from the tunnel centerline and less than 0.001 m in the y-direction. Considering the influence of skidding, the deviation correction control performance test experiments of the tracked drilling and anchoring robot at dy = 0.5 m away from the tunnel centerline were completed. In the experiments, the tracked drilling and anchoring robot exhibited a significant difference in speed between the two sides of the tracks with a track skid rate of 0.22. Although the real-time tracking maximum error in the y-direction from the tunnel centerline was 0.13 m, the final error was 0.003 m, meeting the requirements for position deviation control of the drilling and anchoring robot in tunnel environments. These research findings provide a theoretical basis and technical support for the intelligent control of tracked mobile devices in coal mine tunnels, with significant theoretical and engineering implications. [ABSTRACT FROM AUTHOR]

Tracked inspection robots have demonstrated their versatility in a wide range of applications. However, challenges arising from issues such as skidding and slipping have posed obstacles to achieving precise and efficient trajectory control. This paper introduces a method to determine the steering parameters of robot based on the surrounding obstacles and road information. The primary objective is to enhance the steering efficiency of tracked robots. The corresponding relationship between the track speed, driving force and track steering radius of the tracked robot is obtained. Considering the influence of track skid and slip, relationship models about the steering radius and traveling speed of the robot are established. The minimum and maximum steering radii in the obstacle avoidance process are analyzed, and a mathematical model of the relationship between the steering angle of the robot and the distance between the side obstacles is established. The trajectory deviation model of the tracked robot is established, and a principle analysis of the LiDAR ranging is completed. This lays the foundation for a steering measurement and control system for tracked robots. ADAMS(2020) software is used to establish the multi-body dynamics model of the tracked robot, and three different obstacle-avoiding steering control strategies are designed for the robot in a simulated environment with space obstacles. The simulation experiment demonstrates that the robot achieves more efficient obstacle avoidance steering through the use of differential steering, leading to a decrease in both track skid and slip rates. Through the simulation experiment, it can be seen that the robot uses differential steering to complete the obstacle avoidance steering movement more efficiently, and the track skid and slip rates are smaller. The simulation results are used to complete the steering control experiment of the tracked robot on different road surfaces. The results show that by adjusting the track driving parameters, the robot can effectively complete the obstacle-avoiding steering movement by using the differential steering control strategy, which verifies the accuracy of the steering control strategy. [ABSTRACT FROM AUTHOR]