A multi-level hybrid obstacle avoidance control method adaptive to environment complexity

Abstract

The application discloses a kind of multi-level hybrid obstacle avoidance control methods of self-adapting environmental complexity, it is related to robot control technical field, including: obtaining the global map information of inspection environment, map is divided into grid unit and is marked static obstacle, global path of inspection robot is calculated from start position to target position using A* algorithm;The position and speed information of dynamic obstacle are acquired in real time by laser radar, and the model of attractive field and repulsive field is established, wherein, attractive field is generated by target point, and repulsive field is generated by detected dynamic obstacle.The application establishes obstacle classification mechanism based on distance threshold, realizes the quantitative evaluation of threat degree, and avoids the problem of low control efficiency caused by unified processing strategy for all obstacles in traditional method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, in particular to a multi-level hybrid obstacle avoidance control method adaptive to environment complexity. BACKGROUND

[0002] Obstacle avoidance control of mobile robots in complex dynamic environments has always been a focus and difficulty of research. Traditional obstacle avoidance methods mainly include artificial potential field method, dynamic window method and velocity obstacle method. The artificial potential field method realizes obstacle avoidance by constructing attractive field and repulsive field, has the advantages of simple calculation and strong real-time performance, but is easy to fall into local minimum. The dynamic window method plans trajectory under kinematic constraints of the robot, which can ensure the executability of control instructions, but its obstacle avoidance performance is significantly affected by the velocity sampling resolution. The velocity obstacle method plans trajectory based on velocity-time space, which can effectively handle dynamic obstacles, but has high computational complexity, which is not conducive to real-time control. When dealing with multiple obstacle scenarios, these methods often use a unified obstacle avoidance strategy, which is difficult to differentiate control according to the threat level of obstacles.

[0003] The existing obstacle avoidance control methods have the following shortcomings: first, the threat level of obstacles is not quantitatively evaluated, resulting in overly conservative or aggressive obstacle avoidance control; second, the obstacle avoidance control strategy is relatively fixed, making it difficult to take differentiated control for obstacles with different threat levels; third, the collision risk warning mechanism is not perfect, which cannot identify potential collision risks in time and respond. These problems limit the obstacle avoidance performance of robots in complex dynamic environments and affect their actual application effect. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the present application provides a multi-level hybrid obstacle avoidance control method adaptive to environment complexity, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a multi-level hybrid obstacle avoidance control method adaptive to environment complexity, comprising: acquiring global map information of a patrol environment, dividing the map into grid cells and marking static obstacles, and calculating a global path of a patrol robot from a starting position to a target position using an A* algorithm;

[0007] Real-time acquisition of position and velocity information of dynamic obstacles by laser radar, establishment of attractive field and repulsive field models, wherein the attractive field is generated by a target point, and the repulsive field is generated by detected dynamic obstacles;

[0008] The movement direction and speed of the inspection robot are calculated according to the synthetic force field of the gravitational field and the repulsive field. When a dynamic obstacle is detected, the local path is updated and the inspection robot is controlled to move along the updated path.

[0009] As a preferred solution of the multi-level hybrid obstacle avoidance control method for adaptive environment complexity described in the present invention, in which: in the gravitational field and repulsive field model, a dynamic gravitational field function is constructed by the obstacle motion trend term:

[0010]

[0011] Where α is the velocity direction factor, v obs is the obstacle velocity vector, is the unit vector direction from the robot to the target point; k att is the gravitational field gain coefficient; p is the current position coordinate of the robot; p goal is the target position coordinate;

[0012] The obstacle motion trend item is adjusted by determining the angle θ between the obstacle motion direction and the line connecting the robot to the target point:

[0013] If θ<π / 2, then α=α0cosθ; if θ≥π / 2, then α=0;

[0014] Where α0 is the benchmark impact factor;

[0015] The adaptive repulsive field function is dynamically adjusted according to the motion characteristics of the obstacle:

[0016]

[0017] Where γ is the weight coefficient of motion trend, and the relative speed of the obstacle Calculated; k rep is the repulsive field gain coefficient, d is the distance between the robot and the obstacle, d0 is the influence range of the repulsive field, p obs is the obstacle position coordinate.

[0018] As a preferred solution of the multi-level hybrid obstacle avoidance control method for adaptive environment complexity of the present invention, wherein: in the calculation of the synthetic force field, the environment in which the robot is located is judged;

[0019] If it is located in a single obstacle environment, the resultant force is calculated directly:

[0020] F total =F att +F rep

[0021] If the environment is full of obstacles, the weight is calculated based on the obstacle threat level:

[0022]

[0023] Where λ is the distance attenuation coefficient, β is the speed influence coefficient, and n is the number of obstacles; is the relative velocity vector of the robot relative to the i-th obstacle;

[0024] Calculate the weighted resultant force:

[0025]

[0026] Determine the robot's movement speed based on the direction and magnitude of the resultant force:

[0027]

[0028] where k v is the speed adjustment coefficient, v max is the maximum permissible speed; k v is the speed adjustment coefficient, v max is the maximum permissible speed.

[0029] As a preferred solution of the multi-level hybrid obstacle avoidance control method for adaptive environment complexity of the present invention, the local path update process includes constructing an obstacle motion prediction model. Specifically, if the obstacle motion direction change rate is less than a threshold value ω th , then the uniform linear motion model is used for prediction; if the obstacle movement direction change rate is greater than or equal to the threshold ω th , then the curve fitting prediction model is used;

[0030] Perform path conflict detection. Specifically, if the minimum distance between the predicted trajectory and the current path is less than the safety threshold d safe , and the time reserved is less than t safe , then the path update is triggered; otherwise, the current path remains unchanged;

[0031] A spline curve is generated in the obstacle avoidance area. The curve parameters are determined by the current and target poses of the robot. The optimal path is selected by minimizing the weighted sum of the curvature integral and the path length.

[0032] As a preferred solution of the multi-level hybrid obstacle avoidance control method of the present invention that is adaptive to environmental complexity, the fusion strategy of the global path and local obstacle avoidance is realized by judging the environmental complexity.

[0033] In the path planning process, if the environmental complexity index C e satisfy:

[0034]

[0035] where vi is the speed of the i-th obstacle, d i is the distance to the i-th obstacle, n is the number of obstacles within the field of view; C th is the environment complexity threshold, then switch to local obstacle avoidance mode:

[0036]

[0037] where t p is the prediction time domain, v robot is the robot velocity calculated from the synthetic force field; P current is the current position of the robot;

[0038] If C e ≤C th , then keep the global path following mode:

[0039]

[0040] Where h(p) is the heuristic distance from the path point to the target point; P global is a set of global path points.

[0041] As a preferred solution of the multi-level hybrid obstacle avoidance control method of the present invention that is adaptive to environmental complexity, the environmental perception layer classifies obstacles by judging the distance. Specifically,

[0042] If the obstacle distance is less than d min , classifying it as a high-threat obstacle;

[0043] If the obstacle distance is between d min with d max between them, classifying them as potential threatening obstacles;

[0044] The motion control layer constrains the robot speed according to the obstacle threat level.

[0045]

[0046] where v cmd is the expected speed, k s is the safety factor; d is the current obstacle distance.

[0047] As a preferred solution of the multi-level hybrid obstacle avoidance control method of the present invention that is adaptive to the complexity of the environment, it also includes a collision warning mechanism for judging based on the time margin:

[0048]

[0049] When the collisionWhen the time is less than the safety time threshold, the deceleration obstacle avoidance control is triggered; is the unit vector from the robot to the obstacle.

[0050] To further solve the above technical problems, the present invention provides the following technical solutions: A system for calibrating the angle of a circular coal yard stacker and reclaimer based on microwave recognition, comprising: an environmental perception module for classifying obstacles as high-threat obstacles or potential-threat obstacles based on a distance threshold;

[0051] Speed ​​control module, used to constrain the robot speed according to the obstacle threat level;

[0052] The early warning monitoring module is used to determine whether to trigger deceleration and obstacle avoidance control based on the time margin.

[0053] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the multi-level hybrid obstacle avoidance control method that adapts to the complexity of the environment as described above.

[0054] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the multi-level hybrid obstacle avoidance control method that is adaptive to the complexity of the environment as described above are implemented.

[0055] The present invention has the following beneficial effects: By establishing an obstacle classification mechanism based on distance thresholds, a quantitative assessment of threat levels is achieved, avoiding the low control efficiency caused by the traditional method of using a unified processing strategy for all obstacles. By introducing key parameters such as obstacle distance and robot speed into the dynamic speed constraint formula, a quantitative relationship between threat level and control strategy is established, providing a more precise speed regulation mechanism compared to the simple linear speed constraint in the existing technology. The introduction of a collision warning criterion based on time margin takes into account the relative motion characteristics of the obstacle and the robot, overcomes the lag that may be caused by relying solely on distance threshold judgment, and improves the accuracy and timeliness of warnings. This improves control efficiency while ensuring safety, enabling the robot to better adapt to complex dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 is a flow chart of the method of the present invention;

[0058] Figure 2 This is a diagram of the computer equipment in the present invention. DETAILED DESCRIPTION

[0059] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Example 1, with reference to Figure 1 , as an embodiment of the present invention, provides a multi-level hybrid obstacle avoidance control method that is adaptive to environmental complexity.

[0062] Figure 1 The overall flow chart of a multi-level hybrid obstacle avoidance control method that is adaptive to environmental complexity is shown, including: S1: obtaining global map information of the inspection environment, dividing the map into grid cells and marking static obstacles, and using the A* algorithm to calculate the global path of the inspection robot from the starting position to the target position;

[0063] S2: Use LiDAR to obtain the position and velocity information of dynamic obstacles in real time and establish a gravitational field and repulsive field model. The gravitational field is generated by the target point, and the repulsive field is generated by the detected dynamic obstacles.

[0064] S3: Calculate the movement direction and speed of the inspection robot according to the synthetic force field of the gravitational field and the repulsive field, update the local path when a dynamic obstacle is detected, and control the inspection robot to move along the updated path.

[0065] It should be noted that this method, through the organic combination of global path planning and local obstacle avoidance, focuses on solving technical problems of traditional methods such as low path planning efficiency, single obstacle avoidance strategy, and poor real-time performance in complex dynamic environments. The specific implementation steps are as follows:

[0066] In step S1, the global map information of the inspection environment is first acquired. The environment is divided into grid units with a size of 0.1 m x 0.1 m using a gridding method. The size is determined based on the size of the inspection robot (0.6 m x 0.8 m) and the minimum turning radius (0.8 m). When labeling static obstacles, a consideration is given to an obstacle inflation coefficient of 1.2 to ensure that the planned path maintains a sufficient safety distance from the static obstacles. On this basis, an improved A* algorithm is used to calculate the global path. The improvement mainly lies in the design of the heuristic function, which takes into account both the shortest distance and the avoidance of obstacle-dense areas.

[0067] In step S2, dynamic obstacle information is acquired in real time using a laser radar. The scanning frequency is set to 10 Hz, the ranging range is 0.1-30 m, and the angular resolution is 0.25°. Based on the detected obstacle information, an improved potential field model is established. The characteristics of this model are that both the attractive field and the repulsive field take into account the motion characteristics of the obstacles. By introducing a velocity coupling term and a motion trend weight, the predictability and adaptability of obstacle avoidance are improved.

[0068] In step S3, the system calculates the motion instructions and updates the local path based on the synthesized force field. A hierarchical processing strategy is adopted: collision detection and emergency obstacle avoidance are performed at a frequency of 5 Hz to ensure safety; trajectory smoothing and local optimization are performed at a frequency of 2 Hz to ensure the continuity of the motion trajectory. The local path is generated using a cubic spline curve, which ensures C2-level continuity of the curvature and significantly improves the smoothness of the robot motion.

[0069] Through the implementation of the above steps, the present scheme has significant advantages in complex dynamic environments: the global path planning time is reduced by 44% compared to the traditional A* algorithm; the dynamic obstacle avoidance success rate is improved by 11.4 percentage points; and the trajectory smoothness is improved by 40%. These improvements make the present scheme particularly suitable for inspection tasks in complex environments with multiple dynamic obstacles, such as industrial parks and warehouse logistics.

[0070] In the attractive field and repulsive field models, a dynamic attractive field function is constructed through an obstacle motion trend term:

[0071]

[0072] where α is a velocity direction influence factor, v obs is the obstacle velocity vector, is the unit vector direction from the robot to the target point; k att is the attractive field gain coefficient; p is the current position coordinate of the robot; p goal is the target position coordinate;

[0073] The obstacle motion trend term is adjusted by judging the angle θ between the motion direction of the obstacle and the line connecting the robot to the target point:

[0074] If θ<π / 2, then α=α0cosθ; if θ≥π / 2, then α=0;

[0075] Where α0 is the benchmark impact factor;

[0076] The adaptive repulsive field function is dynamically adjusted according to the motion characteristics of the obstacle:

[0077]

[0078] Where γ is the weight coefficient of motion trend, and the relative speed of the obstacle Calculated; k rep is the repulsive field gain coefficient, d is the distance between the robot and the obstacle, d0 is the influence range of the repulsive field, p obs is the obstacle position coordinate.

[0079] In the calculation of the synthetic force field, determine the robot's environment;

[0080] If it is located in a single obstacle environment, the resultant force is calculated directly:

[0081] F total =F att +F rep

[0082] If the environment is full of obstacles, the weight is calculated based on the obstacle threat level:

[0083]

[0084] Where λ is the distance attenuation coefficient, β is the speed influence coefficient, and n is the number of obstacles; is the relative velocity vector of the robot relative to the i-th obstacle;

[0085] Calculate the weighted resultant force:

[0086]

[0087] Determine the robot's movement speed based on the direction and magnitude of the resultant force:

[0088]

[0089] where k v is the speed adjustment coefficient, v max is the maximum permissible speed; k v is the speed adjustment coefficient, v max is the maximum permissible speed.

[0090] It should be noted that the above steps address the technical challenges of robot obstacle avoidance control in dynamic environments. First, addressing the motion characteristics of dynamic obstacles, the system introduces an obstacle motion trend term, Fatt(d), into the gravitational field. The velocity direction influence factor α is dynamically adjusted based on the angle θ. When the obstacle is moving toward the target (0 < π / 2), α varies with the cosine of the angle. When the obstacle is moving away from the target (), α is reset to zero. This design enables the system to proactively perceive and respond to the obstacle's motion trend, avoiding the obstacle avoidance lag caused by traditional methods that rely solely on current position information. Secondly, a relative velocity term is introduced into the repulsive field function Frep(d). Its influence is adjusted by the motion trend weight coefficient γ, enabling adaptive adjustment of the repulsive field strength with the relative motion state, ensuring obstacle avoidance safety. For multi-obstacle environments, a threat-based weighting mechanism is proposed. This comprehensively considers the distance and motion characteristics of the obstacle using the distance attenuation coefficient λ and the velocity influence coefficient β. The threat weight wi of each obstacle is calculated, resulting in a weighted composite force Ftotal. This mechanism avoids the local minimum problem caused by simply superimposing multiple repulsive forces in traditional methods, and improves the obstacle avoidance efficiency in complex environments. Finally, according to the direction and magnitude of the composite force, combined with the speed adjustment coefficient k v Dynamically determine the robot's movement speed v robot , ensuring the smoothness of the motion trajectory. The core innovation of this method lies in organically integrating the motion characteristics of obstacles into the artificial potential field model, achieving precise perception and rapid response to dynamic environments. Compared with existing technologies, this method significantly improves the timeliness, safety, and smoothness of obstacle avoidance. Experimental results show that in typical dynamic obstacle avoidance scenarios, this method can increase the obstacle avoidance success rate by approximately 15%, shorten the average obstacle avoidance time by approximately 20%, and ensure the continuity of the trajectory curvature.

[0091] In the process of implementing this solution, the optimal values ​​of key parameters were determined through a large number of experimental verifications: the baseline influence factor α0 = 0.5, which makes the system moderately sensitive to the movement trend of obstacles; the movement trend weight coefficient γ = 0.3, which reasonably balances the influence of relative speed on the repulsive field; the distance attenuation coefficient λ = 0.8, which achieves a reasonable attenuation of threat with distance; the speed influence coefficient β = 0.4, which ensures the accuracy of the relative motion threat assessment; the speed adjustment coefficient k v = 0.6, maintaining smooth trajectory while ensuring motion responsiveness. These parameter settings demonstrate good adaptability in various typical dynamic obstacle avoidance scenarios and have no special requirements for the robot itself, making them highly practical.

[0092] The local path update process includes building an obstacle motion prediction model. Specifically, if the obstacle motion direction change rate is less than the threshold ω th, then the uniform linear motion model is used for prediction; if the obstacle movement direction change rate is greater than or equal to the threshold ω th , then the curve fitting prediction model is used;

[0093] Perform path conflict detection. Specifically, if the minimum distance between the predicted trajectory and the current path is less than the safety threshold d safe , and the time reserved is less than t safe , then the path update is triggered; otherwise, the current path remains unchanged;

[0094] A spline curve is generated in the obstacle avoidance area. The curve parameters are determined by the current and target poses of the robot. The optimal path is selected by minimizing the weighted sum of the curvature integral and the path length.

[0095] The fusion strategy of global path and local obstacle avoidance is realized by judging the complexity of the environment.

[0096] In the path planning process, if the environmental complexity index C e satisfy:

[0097]

[0098] where v i is the speed of the i-th obstacle, d i is the distance to the i-th obstacle, n is the number of obstacles within the field of view; C th is the environment complexity threshold, then switch to local obstacle avoidance mode:

[0099]

[0100] where t p is the prediction time domain, v robot is the robot velocity calculated from the synthetic force field; P current is the current position of the robot;

[0101] If C e ≤C th , then keep the global path following mode:

[0102]

[0103] Where h(p) is the heuristic distance from the path point to the target point; P global is a set of global path points.

[0104] It should be noted that the above steps solve the dynamic obstacle avoidance problem in different complexity scenarios. First, for a single dynamic obstacle, the system uses the environmental complexity index C e Dynamically evaluate the current obstacle avoidance difficulty. e Exceeding threshold C thWhen the system switches to the local obstacle avoidance mode, it predicts the time domain t p The velocity integral within determines the local path P local , to achieve a quick response to the movement trend of obstacles. e ≤C th When the system maintains the global path following mode, it selects the optimal target point P by comprehensively considering the distance from the current position to the path point and the heuristic function h(p). target , ensuring the global optimality of path planning. In a multi-obstacle environment, the system is based on the same evaluation mechanism, but calculates the speed-distance ratio of each obstacle cumulatively. To reflect the overall complexity of the environment, this method accurately captures potential risks in multi-obstacle scenarios. Experiments show that this method achieves an obstacle avoidance success rate of over 95% in single-obstacle scenarios and maintains a success rate of over 85% in complex environments with 3-5 dynamic obstacles, while increasing average obstacle avoidance time by only approximately 30%.

[0105] In the implementation process, the selection of environment complexity evaluation parameters is crucial: the environment complexity threshold C th Set to 2.5, this value is determined by comparing and analyzing the obstacle avoidance performance in different scenarios, which can achieve a good balance between ensuring safety and path planning efficiency; the prediction time domain t p A duration of 1.5 seconds was chosen, which is sufficient to cover the characteristic motion cycle of most dynamic obstacles while maintaining an acceptable computational load. In scenarios with multiple obstacles, the system automatically adjusts the impact weight of each obstacle, prioritizing closer and faster obstacles. This mechanism ensures that the system prioritizes the most threatening obstacles. This solution demonstrates excellent environmental adaptability and obstacle avoidance reliability in a variety of practical application scenarios, making it particularly suitable for use in dynamic and complex indoor and outdoor environments.

[0106] The environmental perception layer classifies obstacles by judging the distance. Specifically,

[0107] If the obstacle distance is less than d min , classifying it as a high-threat obstacle;

[0108] If the obstacle distance is between d min with d max between them, classifying them as potential threatening obstacles;

[0109] The motion control layer constrains the robot's speed according to the obstacle threat level.

[0110]

[0111] where v cmdDesired speed, k s Safety factor; d is the current obstacle distance.

[0112] Also includes a collision warning mechanism for judging according to time margin:

[0113]

[0114] When t collision is less than the safety time threshold, trigger deceleration obstacle avoidance control; The unit vector of the robot to the obstacle.

[0115] It should be noted that the safety control problem in the process of robot motion is solved. The system designs a multi-level safety monitoring mechanism. First, at the environmental perception level, the obstacles are dynamically classified based on distance threshold: when the obstacle distance is less than the safety distance threshold d min , it is identified as a high threat obstacle and immediate obstacle avoidance measures need to be taken; when the obstacle distance is between d min and d max , it is marked as a potential threat obstacle, and the system enters an alert state and prepares for obstacle avoidance. At the motion control level, the adaptive adjustment of the robot speed is realized through the speed constraint formula v out , in which the safety factor k s is used to adjust the sensitivity of speed attenuation. At the same time, the system introduces a collision warning mechanism based on time margin, which triggers deceleration obstacle avoidance control when the collision time t collision is less than the preset safety time threshold. This multi-level safety monitoring mechanism not only ensures the safety of obstacle avoidance, but also realizes the smoothness of the motion trajectory through dynamic speed planning. Experimental data shows that this method can reduce the collision risk by more than 90%, while maintaining an average motion speed reduction of only about 20%.

[0116] In specific implementation, the selection of safety monitoring parameters is based on a large number of experimental verification: the safety distance threshold d min is set to 0.5m, considering the robot size and normal braking distance; the maximum alert distance d max is set to 2.0m, within which the system can fully estimate and respond to potential threats; the safety factor k s is set to 1.5, which achieves a good balance between safety margin and motion efficiency; the collision warning time threshold is set to 2.0s, which can reserve sufficient obstacle avoidance decision and execution time for the system. This set of parameter configuration performs stably in various application scenarios, especially suitable for use in human-robot coexistence environments. This scheme combines safety monitoring mechanism with motion control strategy, not only ensuring the reliability of obstacle avoidance, but also realizing the efficiency and smoothness of the motion process.

[0117] In summary, this method establishes a distance-based threat level assessment mechanism to quantitatively determine the threat level of obstacles. It also employs a dynamic velocity constraint strategy to achieve differentiated control for obstacles of varying threat levels. Furthermore, it introduces a time-margin-based collision warning mechanism to enhance the safety and reliability of obstacle avoidance control. The beneficial effects of this invention include improving the robot's obstacle avoidance performance in complex dynamic environments and enhancing the adaptability and robustness of obstacle avoidance control.

[0118] Example 2, an embodiment of the present invention, provides a circular coal yard stacker-reclaimer angle calibration system based on microwave recognition, comprising:

[0119] Environmental perception module, used to classify obstacles as high threat obstacles or potential threat obstacles based on distance threshold judgment;

[0120] Speed ​​control module, used to constrain the robot speed according to the obstacle threat level;

[0121] The early warning monitoring module is used to determine whether to trigger deceleration and obstacle avoidance control based on the time margin.

[0122] Example 3, reference Figure 2 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0123] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0124] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0125] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0126] Example 4 is an embodiment of the present invention, which provides a multi-level hybrid obstacle avoidance control method that is adaptive to environmental complexity. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0127] To validate the effectiveness of this method, a series of comparative experiments were designed, testing it in static environments, single-dynamic obstacle environments, and multiple-dynamic obstacle environments. The experimental platform employed a Pioneer 3-AT mobile robot equipped with a lidar and depth camera for environmental perception, and the experimental site was an 8m×8m indoor area. Under identical starting and ending points and obstacle distribution conditions, this method was compared with the traditional artificial potential field method (APF), the dynamic window method (DWA), and the improved velocity obstacle method (VO). During the experiments, static obstacles were randomly distributed within the experimental site, while dynamic obstacles moved at speeds ranging from 0.2 to 0.8m / s, with their directions randomly generated within the range [-π,π]. Each set of experiments was repeated 20 times, and key metrics such as obstacle avoidance success rate, average avoidance time, path smoothness, computational efficiency, and safety margin were recorded. To evaluate the system's performance under extreme conditions, several challenging scenarios were designed, including typical conditions such as high-speed intersections, dense obstacles, and narrow passages.

[0128] The dynamic adaptability test focused on verifying the response characteristics of the present invention under conditions of drastic changes in environmental complexity. By dynamically changing the number and motion characteristics of obstacles during the experiment, the system's mode switching and parameter adaptation capabilities were tested. At the same time, curvature continuity and acceleration fluctuation were used as evaluation indicators for velocity planning and trajectory smoothness. The robot's actual motion trajectory was recorded using a high-precision motion capture system and quantitatively analyzed. In the safety test, the minimum obstacle avoidance distance and average obstacle avoidance margin in different scenarios were statistically calculated to verify the effectiveness of the safety monitoring mechanism. In particular, the introduction of random perturbations and sensor noise was used to evaluate the system's robustness and anti-interference capabilities.

[0129] Table 1 Performance comparison test data of different obstacle avoidance methods

[0130] Evaluation Metrics This method Traditional APF DWA method VO method Static environment obstacle avoidance success rate (%) 98.5 92.3 94.1 93.8 Single dynamic obstacle avoidance success rate (%) 95.2 85.6 88.4 89.2 Multiple dynamic obstacle avoidance success rate (%) 92.1 78.3 82.5 83.1 Average calculation time (ms) 12.5 8.2 15.8 14.3 Path smoothness index 0.92 0.75 0.83 0.85 Minimum obstacle avoidance distance (m) 0.48 0.32 0.41 0.39 Average obstacle avoidance time (s) 15.3 19.8 17.2 16.8 <![CDATA[速度波动度(m / s 2 )]]> 0.15 0.28 0.21 0.19 Environmental adaptability index 0.88 0.65 0.72 0.75 Computing resource utilization (%) 25.3 18.5 32.1 29.8

[0131] Through the analysis of experimental data, it can be concluded that the present invention is significantly superior to existing methods in multiple key performance indicators. In terms of obstacle avoidance success rate, the present invention reaches 98.5%, 95.2% and 92.1% in static environment, single dynamic obstacle and multiple dynamic obstacle scenarios respectively, which is 5-15 percentage points higher than the traditional method. This is mainly due to the newly proposed environment complexity assessment mechanism and adaptive control strategy. In terms of motion performance, the path smoothness index of the present invention reaches 0.92, which is 22.7% higher than the traditional APF method, and the speed fluctuation is reduced to 0.15m / s 2, the improvement reaches 46.4%, which shows that the present invention can significantly improve the quality of the motion trajectory while ensuring the safety of obstacle avoidance. Although the average calculation time (12.5ms) of the present invention is slightly higher than that of the traditional APF method (8.2ms), it is still significantly better than the DWA method (15.8ms) and the VO method (14.3ms), and considering the significantly improved performance, this moderate computational overhead is completely acceptable. It is particularly noteworthy that the present invention achieves 0.48m in the minimum obstacle avoidance distance index, which is much higher than other methods, fully verifying the effectiveness of the multi-level safety monitoring mechanism. The environmental adaptability index reaches 0.88, which is 17-35% higher than other methods, indicating that the present invention can better cope with complex and changing environments. These data fully demonstrate the comprehensive advantages of the present invention in obstacle avoidance performance, motion quality and environmental adaptability.

[0132] It is important to note that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-level hybrid obstacle avoidance control method that is adaptive to environmental complexity, characterized in that: include: Obtain global map information of the inspection environment, divide the map into grid cells and mark static obstacles, and use the A* algorithm to calculate the global path of the inspection robot from the starting position to the target position; Using laser radar to obtain the position and velocity information of dynamic obstacles in real time, and establish a gravitational field and repulsive field model, wherein the gravitational field is generated by the target point and the repulsive field is generated by the detected dynamic obstacles; The inspection robot's movement direction and speed are calculated based on the composite force field of the gravitational field and the repulsive field. When a dynamic obstacle is detected, the local path is updated and the inspection robot is controlled to move along the updated path. The fusion strategy of global path and local obstacle avoidance is realized by judging the complexity of the environment. In the path planning process, if the environmental complexity index C e satisfy: where v i is the speed of the i-th obstacle, d i is the distance to the i-th obstacle, n is the number of obstacles within the field of view; C th is the environment complexity threshold, then switch to local obstacle avoidance mode: where t p is the prediction time domain, v robot is the robot velocity calculated from the synthetic force field; P current is the current position of the robot; If C e ≤C th , then keep the global path following mode: Where h(p) is the heuristic distance from the path point to the target point; P global is the global path point set; It also includes a collision warning mechanism to determine based on the time margin: When the collision When the time is less than the safety time threshold, the deceleration obstacle avoidance control is triggered; is the unit vector from the robot to the obstacle.

2. The multi-level hybrid obstacle avoidance control method adaptive to environmental complexity according to claim 1, characterized in that: In the gravitational field and repulsive field model, the dynamic gravitational field function is constructed by the obstacle motion trend term: Where α is the velocity direction factor, v obs is the obstacle velocity vector, is the unit vector direction from the robot to the target point; k att is the gravitational field gain coefficient; p is the current position coordinate of the robot; p goal is the target position coordinate; The obstacle motion trend item is adjusted by determining the angle θ between the obstacle motion direction and the line connecting the robot to the target point: If θ<π / 2, then α=α0cosθ; if θ≥π / 2, then α=0; Where α0 is the benchmark impact factor; The adaptive repulsive field function is dynamically adjusted according to the motion characteristics of the obstacle: Where γ is the weight coefficient of motion trend, and the relative speed of the obstacle Calculated; k rep is the repulsive field gain coefficient, d is the distance between the robot and the obstacle, d0 is the influence range of the repulsive field, p obs is the obstacle position coordinate.

3. The multi-level hybrid obstacle avoidance control method adaptive to environmental complexity according to claim 2, characterized in that: In the calculation of the synthetic force field, the environment in which the robot is located is determined; If it is located in a single obstacle environment, the resultant force is calculated directly: F total =F att +F rep If the environment is full of obstacles, the weight is calculated based on the obstacle threat level: Where λ is the distance attenuation coefficient, β is the speed influence coefficient, and n is the number of obstacles; is the relative velocity vector of the robot relative to the i-th obstacle; Calculate the weighted resultant force: Determine the robot's movement speed based on the direction and magnitude of the resultant force: where k v is the speed adjustment coefficient, v max is the maximum permissible speed; k v is the speed adjustment coefficient, v max is the maximum permissible speed.

4. The multi-level hybrid obstacle avoidance control method adaptive to environmental complexity according to claim 3, characterized in that: The local path update process includes building an obstacle motion prediction model. Specifically, if the obstacle motion direction change rate is less than a threshold value ω th , then the uniform linear motion model is used for prediction; if the obstacle movement direction change rate is greater than or equal to the threshold ω th , then the curve fitting prediction model is used; Perform path conflict detection. Specifically, if the minimum distance between the predicted trajectory and the current path is less than the safety threshold d safe , and the time reserved is less than t safe , then the path update is triggered; otherwise, the current path remains unchanged; A spline curve is generated in the obstacle avoidance area. The curve parameters are determined by the current and target poses of the robot. The optimal path is selected by minimizing the weighted sum of the curvature integral and the path length.

5. The multi-level hybrid obstacle avoidance control method according to claim 4, wherein: The environmental perception layer classifies obstacles by judging the distance. Specifically, If the obstacle distance is less than d min , classifying it as a high-threat obstacle; If the obstacle distance is between d min with d max between them, classifying them as potential threatening obstacles; The motion control layer constrains the robot speed according to the obstacle threat level. where v cmd is the expected speed, k s is the safety factor; d is the current obstacle distance.

6. A system using the multi-level hybrid obstacle avoidance control method according to any one of claims 1 to 5, characterized in that: include: Environmental perception module, used to classify obstacles as high threat obstacles or potential threat obstacles based on distance threshold judgment; Speed ​​control module, used to constrain the robot speed according to the obstacle threat level; The early warning monitoring module is used to determine whether to trigger deceleration and obstacle avoidance control based on the time margin.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-level hybrid obstacle avoidance control method adapted to environment complexity according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-level hybrid obstacle avoidance control method adaptive to environment complexity according to any one of claims 1 to 5 are implemented.

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