Mobile robot path planning method and system based on circular trajectory control obstacle function

Abstract

The invention discloses a mobile robot path planning method and system based on a circular trajectory control obstacle function, and aims to realize trajectory smooth control in an obstacle avoidance process. Firstly, an envelope circle is used for modeling an obstacle, an actual boundary is approached in a continuous and simple geometric form, and the calculation complexity of collision detection is reduced. Secondly, designing a control obstacle function based on a circular trajectory, constructing a derivable constraint in combination with a geometrical relationship between a tangent line and an obstacle boundary, and achieving trajectory smoothing, reducing speed fluctuation and prolonging the service life of an execution mechanism while guaranteeing a safe distance. For disturbance caused by factors such as ground unevenness, historical data are learned by using a Gaussian process, the disturbance trend is predicted in real time, and the motion stability is improved. And finally, through nonlinear optimization solution, generating control input with safety, continuity and disturbance adaptive capability. According to the method, geometric modeling, safety constraint, disturbance prediction and a dynamic adjustment mechanism are fused, and the safety and adaptability of robot path planning are remarkably enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile robot path planning, and in particular relates to a mobile robot path planning method and system based on a circular trajectory control obstacle function. Background Art

[0002] With the advancement of artificial intelligence, sensing technology, and embedded systems, mobile robots have found widespread application in industrial automation, warehousing and logistics, disaster rescue, agricultural inspection, and service robotics. Path planning, a key component of autonomous navigation for mobile robots, generates a collision-free, smooth, and efficient trajectory from a starting point to a destination in complex environments. Its performance directly impacts the operational efficiency, safety, and stability of the robotic system.

[0003] Traditional path planning methods primarily include graph search-based methods (such as the A* algorithm and the Dijkstra algorithm), sampling methods (such as RRT and PRM), and optimization-based methods (such as gradient descent, nonlinear programming, and model predictive control). However, these methods still face numerous challenges when dealing with dynamic environments, densely populated areas with multiple obstacles, and tasks requiring high path continuity and controllability. For example, while graph search methods perform well in static environments, they often struggle to meet trajectory smoothness and real-time requirements; sampling methods suffer from path instability and limited planning efficiency; and while optimization-based methods can generate optimal trajectories, they place high demands on constraint modeling accuracy, computational complexity, and convergence.

[0004] In recent years, control barrier functions (CBFs) have garnered increasing attention as a method for embedding control safety into trajectory planning. CBFs can transform safety constraints into limits on the system's control inputs, enabling the system to maintain safety while meeting mission objectives. However, traditional CBF designs typically rely on constraints based on straight-line or Euclidean distances, such as high-order control barrier functions (HOCBFs), which lack flexibility when dealing with complex or asymmetric obstacle boundaries. In practical applications, this design approach can easily lead to discontinuous control inputs or sudden trajectory changes, compromising the robot's smooth motion and increasing its energy consumption.

[0005] In summary, existing CBF methods struggle to balance safety and trajectory smoothness, especially when dealing with complex obstacle boundaries. This lack of flexibility can easily lead to control discontinuities or sudden trajectory changes, impacting motion smoothness and energy efficiency. Therefore, there is an urgent need to design a CBF form with stronger expressive power that can not only achieve precise avoidance of complex obstacles but also enhance the system's robustness to interference and uncertainty, thereby improving the robot's operational adaptability and stability in complex environments. Summary of the Invention

[0006] In response to the above technical problems, the present invention provides a mobile robot path planning method and system based on a circular trajectory control obstacle function.

[0007] The technical solution adopted by the present invention to solve the technical problem is: A mobile robot path planning method based on a circular trajectory control obstacle function, the method comprising the following steps: S100: In the path planning space, environmental obstacles are detected in real time by sensors, and the obstacle boundary is approximately represented as a geometric model of at least one enveloping circle; S200: Based on the current position of the robot and the obstacle, a circular trajectory-based obstacle control function is constructed. The function dynamically selects internal obstacle avoidance or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; S300: Collect historical state-disturbance data pairs to build a training set, use Gaussian process regression to model the probability distribution of disturbance components, output future disturbance confidence intervals in real time, and adaptively adjust model parameters based on the disturbance confidence intervals; S400: Generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy of S200, the disturbance confidence interval of S400 and the adjusted trajectory parameters are integrated through the quadratic programming method to generate corrected control instructions for controlling the operation of the mobile robot, and update the robot's status information. Repeat S100-S500 until the target position is reached.

[0008] Preferably, S100 includes: For convex obstacles, a single circle fitting is used. For obstacles with complex shapes or non-convex shapes, multiple circles with different radii are used for segmented envelope modeling. Specifically: ; in, and Respectively represent The center and radius of a circle, is the total number of circles.

[0009] Preferably, S200 includes: S210: Based on the current position states of the robot and the obstacle, a control obstacle function based on a circular trajectory is constructed, specifically: ; in, is the robot's position vector, Represents the current position of the robot, Represents the robot's orientation angle, is the velocity vector of the robot, , Represent the linear velocity and angular velocity of the robot, is the robot's moving radius, is the relative distance between the center of the circular trajectory formed by the robot's linear velocity and angular velocity and the center of the obstacle. is the radius of the obstacle's envelope; S220: When When , the robot uses external obstacle avoidance, and its moving trajectory will pass through the extension line of the center of the circular trajectory and the center of the obstacle circle; when When , the robot uses internal obstacle avoidance, and its moving trajectory will pass between the center of the circular trajectory and the line connecting the center of the obstacle circle.

[0010] Preferably, S300 includes: S310: For each disturbance component Establish a Gaussian process model, specifically: ; in, is the prior mean function, is the kernel function used to measure the state and similarity; S320: Collect historical status-disturbance Construct a training data set and calculate the perturbation estimate, where is the system status, is the control input, specifically: ; Each portion will be affected by independent Gaussian noise the impact of; S330: Given Observations , observation number and query status at Satisfies the multivariate Gaussian distribution, and thus calculates the posterior distribution, which also satisfies the Gaussian distribution, specifically: ; in, is the mean, represents variance; ; in, is the identity matrix, represents the kernel matrix based on the training data, Representatives are used to describe and The kernel matrix of similarity, represents the noise variance; S340: Future perturbation mean obtained through learning and variance , construct the disturbance term The confidence interval for is as follows: ; in is the confidence interval coefficient.

[0011] Preferably, S400 specifically includes: ; in, and Represents the minimum and maximum values ​​of the control input; ; ; in, , is the first-order Lie derivative, is the local Lipschitz expansion function, is the control barrier function, represents the distance between the obstacle and the mobile robot, represents the coordinate system rotation matrix, is the direction angle of the mobile robot.

[0012] Preferably, S100 further includes: When no obstacle is detected, it operates according to the nominal control command generated by the nominal path tracking controller.

[0013] A mobile robot path planning system based on a circular trajectory control obstacle function, comprising: The obstacle detection module is used to detect environmental obstacles in real time in the path planning space through sensors and approximate the obstacle boundary as a geometric model of at least one enveloping circle; The circular trajectory-based obstacle control function construction module is used to construct a circular trajectory-based obstacle control function based on the current position of the robot and the obstacle. The function dynamically selects internal or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; The disturbance modeling and prediction module is used to collect historical state-disturbance data pairs to construct a training set, model the probability distribution of the disturbance component through Gaussian process regression, output the future disturbance confidence interval in real time, and adaptively adjust the model parameters based on the disturbance confidence interval. The trajectory parameters include the circular trajectory radius and path acceleration. The control instruction correction module is used to generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy, disturbance confidence interval and adjusted trajectory parameters are integrated to generate corrected control instructions through nonlinear optimization using the quadratic programming method to control the operation of the mobile robot, and the robot's status information is updated. The above process is repeated until the target position is reached.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of a mobile robot path planning method based on a circular trajectory control obstacle function when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a mobile robot path planning method based on a circular trajectory control obstacle function.

[0016] The aforementioned mobile robot path planning method and system based on a circular trajectory obstacle control function utilizes an enveloping circle to model obstacles, approximating actual boundaries with a simple and continuous geometric form. This reduces environmental complexity and lays the foundation for a smooth obstacle avoidance trajectory. The obstacle control function is designed based on the circular trajectory. By analyzing the relationship between the trajectory tangent and the obstacle boundary, a derivable obstacle constraint is constructed to ensure the robot maintains a safe distance from obstacles and enhance obstacle avoidance robustness. To address low-frequency ground vibrations, Gaussian process regression is used to learn from historical data and predict short-term disturbance trends in real time, providing forward-looking environmental information for path planning. Trajectory parameters are then dynamically adjusted based on the vibration prediction results to improve motion stability. Finally, a unified optimization framework is constructed to generate an optimal path that is safe, continuous, and dynamically adaptable to complex disturbances through nonlinear optimization. This method significantly improves the robot's path safety and motion adaptability through geometric simplification, safety constraint design, environmental disturbance prediction, and dynamic adjustment mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is a flow chart of a mobile robot path planning method based on a circular trajectory control obstacle function in one embodiment of the present invention; Figure 2 A technical roadmap for a mobile robot path planning algorithm based on a circular trajectory control obstacle function in another embodiment of the present invention; Figure 3 This is an internal and external obstacle avoidance method based on a circular trajectory control obstacle function in one embodiment of the present invention; wherein (a) is an internal obstacle avoidance method, and (b) is an external obstacle avoidance method; Figure 4 This is a rendering of a mobile robot path planning algorithm based on a circular trajectory control obstacle function in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings.

[0019] In one embodiment, Figure 1 and Figure 2 As shown, a mobile robot path planning method based on a circular trajectory control obstacle function comprises the following steps: S100: In the path planning space, environmental obstacles are detected in real time by sensors, and the obstacle boundary is approximately represented as a geometric model of at least one enveloping circle; S200: Based on the current position of the robot and the obstacle, a circular trajectory-based obstacle control function is constructed. The function dynamically selects internal obstacle avoidance or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; S300: Collect historical state-disturbance data pairs to build a training set, use Gaussian process regression to model the probability distribution of disturbance components, output future disturbance confidence intervals in real time, and adaptively adjust model parameters based on the disturbance confidence intervals; S400: Generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy of S200, the disturbance confidence interval of S400 and the adjusted trajectory parameters are integrated through the quadratic programming method to generate corrected control instructions for controlling the operation of the mobile robot, and update the robot's status information. Repeat S100-S500 until the target position is reached.

[0020] Specifically, this invention aims to address the challenges of discontinuous obstacle avoidance, non-smooth paths, unstable control, and environmental interference and uncertainty faced by mobile robots in path planning in complex environments. To this end, a path planning method based on a circular trajectory control barrier function and incorporating Gaussian process vibration prediction is proposed. This method improves the safety, stability, and adaptability of path planning by introducing a geometrically continuous circular trajectory, constructing a physically interpretable control barrier function (CBF), and combining Gaussian process modeling and prediction of disturbances caused by uneven surfaces. Its main features are as follows: 1. Enveloping circle-based obstacle modeling: In the path planning space, obstacles are modeled using an enveloping circle, approximating their actual boundaries with a simple, continuous geometric form. This method reduces the complexity of obstacle shapes, facilitating subsequent path planning and collision detection, while also providing a good geometric foundation for constructing smooth avoidance trajectories. 2. Design of a circular trajectory-based control barrier function model: An obstacle function is constructed around the circular trajectory to ensure that the robot maintains a safe distance from obstacles during circular motion. This CBF model considers the relative positional relationship between the trajectory tangent and obstacle boundaries, constructing physically intuitive and derivable obstacle constraints to ensure control stability and robust obstacle avoidance during path planning. 3. Integrating Gaussian Processes to Model and Predict Low-Frequency Ground Vibration: To address low-frequency ground disturbances that often occur during disasters or complex terrain, Gaussian Process Regression (GPR) is used to learn and model historical vibration data and predict ground disturbance trends in real time over the short term. This predicted information is incorporated into the path planning model as a disturbance term, enhancing the robot's proactive response to environmental changes. 4. Dynamically Adjusting Trajectory Structure Based on Disturbance Prediction: Incorporating the disturbance prediction results from the Gaussian process output, the dynamic model parameters are adaptively adjusted to avoid high curvature or high-speed motion in high-vibration areas, thereby improving path safety and execution stability. 5. Integrated Optimization for Path and Control Strategy: A unified optimization framework is constructed that integrates target point attraction, CBF obstacle constraints, trajectory smoothness, control input constraints, and vibration prediction feedback to solve the optimal path using nonlinear optimization methods. Ensure that the generated path is not only safe and continuous, but also dynamically adaptable to complex interference conditions.

[0021] In one embodiment, S100 includes: For convex obstacles, a single circle fitting is used. For obstacles with complex shapes or non-convex shapes, multiple circles with different radii are used for segmented envelope modeling. Specifically: ; in, and Respectively represent The center and radius of a circle, is the total number of circles.

[0022] Specifically, in the path planning space, obstacles are modeled using enveloping circles, approximating their actual boundaries with a simple, continuous geometric form. This method reduces the complexity of obstacle shapes, facilitating subsequent path planning and collision detection, while also providing a good geometric foundation for constructing smooth avoidance trajectories. This approach enables a more compact and accurate fit of obstacle boundaries, improving the ability to represent complex geometric shapes while maintaining the simplicity and efficiency of geometric processing in path planning.

[0023] Furthermore, consider the affine nonlinear state equation of the control system: ,in is the system status, is the system input, and are the drift term and input vector field respectively, satisfying the Lipschitz condition, is the system disturbance term. Definition The system safety set is the set of the mobile robot. If the state of the mobile robot is in this set, the current state of the robot is considered safe. The purpose of the obstacle control function is to design a controller that can ensure that the state of the mobile robot always remains in the forward invariant set. In, that is, when , for , If the security set Defined as a barrier function The super level set of: , in is the control barrier function. When remains non-negative, the mobile robot can remain within the safe region, thus ensuring that the system state always remains in the safe set: .in , is the first-order Lie derivative, is the local Lipschitz expansion function. Obstacle avoidance is achieved by adjusting the mobile robot's linear and angular velocities. To achieve smooth trajectory avoidance, it is necessary to avoid sudden increases or decreases in the robot's speed during the avoidance process. To achieve this, a circular trajectory-based obstacle control function is designed.

[0024] In one embodiment, S200 includes: S210: Based on the robot's current position state and circular trajectory parameters, a circular trajectory-based obstacle control function is constructed, specifically: ; in, is the robot's position vector, Represents the current position of the robot, Represents the robot's orientation angle, is the velocity vector of the robot, , Represent the linear velocity and angular velocity of the robot, is the robot's moving radius, is the relative distance between the robot and the obstacle center, is the radius of the obstacle's envelope; S220: When When , the robot uses external obstacle avoidance, and its moving trajectory will pass through the extension line of the center of the circular trajectory and the center of the obstacle circle; when When the robot uses internal obstacle avoidance, its moving trajectory will pass between the center of the circular trajectory and the line connecting the center of the obstacle circle, such as Figure 3 shown.

[0025] In one embodiment, S300 includes: S310: For each disturbance component Establish a Gaussian process model, specifically: ; in, is the prior mean function, is the kernel function used to measure the state and similarity; S320: Collect historical status-disturbance Construct a training data set and calculate the perturbation estimate, where is the system status, is the control input, specifically: ; Each portion will be affected by independent Gaussian noise the impact of; S330: Given Observations , observation number and query status at Satisfies the multivariate Gaussian distribution, and thus calculates the posterior distribution, which also satisfies the Gaussian distribution, specifically: ; in, is the mean, represents variance; ; in, is the identity matrix, represents the kernel matrix based on the training data, Representatives are used to describe and The kernel matrix of similarity, represents the noise variance; S340: Future perturbation mean obtained through learning and variance , construct the disturbance term The confidence interval for is as follows: ; in is the confidence interval coefficient.

[0026] Specifically, Gaussian processes are a commonly used nonparametric method for estimating unknown functions from limited data. During the motion of mobile robots, Gaussian processes can be used to model and predict disturbances, thereby enhancing the stability and robustness of motion control.

[0027] The system first determines whether the mobile robot has observed an obstacle. If no obstacle is observed, the robot is considered safe, and the safety filter does not need to be activated. The robot moves according to the nominal control input. Conversely, if an obstacle is observed, obstacle avoidance is required, and the safety filter is activated. To ensure safety, the safety filter adjusts the robot's linear and angular velocity to avoid the obstacle. Specifically, a quadratic programming (QP) approach is used to generate control instructions that are closest to the nominal control input while satisfying safety constraints.

[0028] In one embodiment, the control input is obtained in the following form, specifically S400: ; in, and Represents the minimum and maximum values ​​of the control input; ; ; in, , is the first-order Lie derivative, is the local Lipschitz expansion function, is the control barrier function, represents the distance between the obstacle and the mobile robot, represents the coordinate system rotation matrix, is the direction angle of the mobile robot.

[0029] The optimization results are applied to the system as control input, the robot's state information is updated, and the optimization-execution process is repeated until the robot successfully reaches the end point.

[0030] like Figure 4 As shown in Figure 3, compared with the traditional HOCBF method, the proposed method can produce smoother obstacle avoidance trajectories.

[0031] In one embodiment, a mobile robot path planning system based on a circular trajectory control obstacle function is provided, comprising: The obstacle detection module is used to detect environmental obstacles in real time in the path planning space through sensors and approximate the obstacle boundary as a geometric model of at least one enveloping circle; The circular trajectory-based obstacle control function construction module is used to construct a circular trajectory-based obstacle control function based on the robot's current position state and circular trajectory parameters. The function dynamically selects internal or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; The disturbance modeling and prediction module is used to collect historical state-disturbance data pairs to construct a training set, model the probability distribution of the disturbance component through Gaussian process regression, output the future disturbance confidence interval in real time, and adaptively adjust the model parameters based on the disturbance confidence interval. The trajectory parameters include the circular trajectory radius, curvature, and path acceleration. The control instruction correction module is used to generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy, disturbance confidence interval and adjusted trajectory parameters are integrated to generate corrected control instructions through nonlinear optimization using the quadratic programming method to control the operation of the mobile robot, and the robot's status information is updated. The above process is repeated until the target position is reached.

[0032] Regarding the specific definition of a mobile robot path planning system based on a circular trajectory control obstacle function, please refer to the definition of a mobile robot path planning method based on a circular trajectory control obstacle function above, which will not be repeated here. The various modules in the above-mentioned mobile robot path planning system based on a circular trajectory control obstacle function can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of a mobile robot path planning method based on a circular trajectory control obstacle function when executing the computer program.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a mobile robot path planning method based on a circular trajectory control obstacle function.

[0035] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0036] The above is a detailed introduction to the mobile robot path planning method and system based on the circular trajectory control obstacle function provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified. These improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A mobile robot path planning method based on circular trajectory control obstacle function, characterized in that: The method comprises the following steps: S100: In the path planning space, environmental obstacles are detected in real time by sensors, and the obstacle boundary is approximately represented as a geometric model of at least one enveloping circle; S200: Based on the current position of the robot and the obstacle, a circular trajectory-based obstacle control function is constructed. The function dynamically selects internal obstacle avoidance or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; S300: Collect historical state-disturbance data pairs to build a training set, use Gaussian process regression to model the probability distribution of disturbance components, output future disturbance confidence intervals in real time, and adaptively adjust model parameters based on the disturbance confidence intervals; S400: Generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy of S200, the disturbance confidence interval of S400 and the adjusted trajectory parameters are integrated through the quadratic programming method to generate corrected control instructions for controlling the operation of the mobile robot, and update the robot's status information. Repeat S100-S400 until the target position is reached.

2. The method according to claim 1, characterized in that S100 includes: For convex obstacles, a single circle fitting is used. For obstacles with complex shapes or non-convex shapes, multiple circles with different radii are used for segmented envelope modeling. Specifically: ； in, and Respectively represent The center and radius of a circle, is the total number of circles.

3. The method according to claim 2, characterized in that S200 includes: S210: Based on the robot's current position state and circular trajectory parameters, a circular trajectory-based obstacle control function is constructed, specifically: ； in, is the robot's position vector, Represents the current position of the robot, Represents the robot's orientation angle, is the velocity vector of the robot, , Represent the linear velocity and angular velocity of the robot, is the robot's moving radius, is the relative distance between the center of the circular trajectory formed by the robot's linear velocity and angular velocity and the center of the obstacle. is the radius of the obstacle's envelope; S220: When When , the robot uses external obstacle avoidance, and its moving trajectory will pass through the extension line of the trajectory center and the obstacle center; when When , the robot uses internal obstacle avoidance, and its moving trajectory will pass between the line connecting the center of the trajectory circle and the center of the obstacle circle.

4. The method according to claim 3, characterized in that S300 includes: S310: For each disturbance component Establish a Gaussian process model, specifically: ； in, is the prior mean function, is the kernel function used to measure the state and similarity; S320: Collect historical status-disturbance Construct a training data set and calculate the perturbation estimate, where is the system status, is the control input, specifically: ； Each portion will be affected by independent Gaussian noise the impact of; S330: Given Observations , observation number and query status at Satisfies the multivariate Gaussian distribution, and thus calculates the posterior distribution, which also satisfies the Gaussian distribution, specifically: ； in, is the mean, represents variance; ； in, is the identity matrix, represents the kernel matrix based on the training data, Representatives are used to describe and The kernel matrix of similarity, represents the noise variance; S340: Future perturbation mean obtained through learning and variance , construct the disturbance term The confidence interval for is as follows: ； in is the confidence interval coefficient.

5. The method according to claim 4, characterized in that S400 is specifically: ； in, and Represents the minimum and maximum values ​​of the control input; ； ； in, , is the first-order Lie derivative, is the local Lipschitz expansion function, is the control barrier function, represents the distance between the obstacle and the mobile robot, represents the coordinate system rotation matrix, is the direction angle of the mobile robot.

6. The method according to claim 5, characterized in that The S100 also includes: When no obstacle is detected, it operates according to the nominal control command generated by the nominal path tracking controller.

7. A mobile robot path planning system based on a circular trajectory control obstacle function, characterized in that: include: The obstacle detection module is used to detect environmental obstacles in real time in the path planning space through sensors and approximate the obstacle boundary as a geometric model of at least one enveloping circle; The circular trajectory-based obstacle control function construction module is used to construct a circular trajectory-based obstacle control function based on the current position of the robot and the obstacle. The function dynamically selects internal or external obstacle avoidance mode based on the geometric relationship between the moving radius, the distance between the robot and the obstacle center, and the radius of the obstacle envelope circle; The disturbance modeling and prediction module is used to collect historical state-disturbance data pairs to construct a training set, model the probability distribution of the disturbance component through Gaussian process regression, output the future disturbance confidence interval in real time, and adaptively adjust the model parameters based on the disturbance confidence interval. The trajectory parameters include the circular trajectory radius, curvature, and path acceleration. The control instruction correction module is used to generate nominal control instructions through the nominal path tracking controller, with minimizing the deviation between the control instructions and the nominal control instructions as the optimization goal. The obstacle avoidance strategy, disturbance confidence interval and adjusted trajectory parameters are integrated to generate corrected control instructions through nonlinear optimization using the quadratic programming method to control the operation of the mobile robot, and the robot's status information is updated. The above process is repeated until the target position is reached.

8. 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 method according to any one of claims 1 to 6 are implemented.

9. 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 method according to any one of claims 1 to 6 are implemented.

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