Sweeper path planning method

Abstract

The invention discloses a sweeping vehicle path planning method, and relates to the technical field of sweeping vehicle path planning. Comprehensive two-dimensional scanning recording is performed on a sweeping area through a sensing scanning module of a sweeping vehicle, and an initial position and a target position of a sweeping path are input through a path planning module based on the sensing scanning module; according to the direction from the initial position to the target position, evaluating and outputting a target direction vector for path planning of the sweeper, in the process of sensing, scanning and recognizing the obstacle position in the target direction vector in the path sweeping process of the sweeper, and according to the obstacle position, outputting adjustment processing on path sweeping data under the influence of the obstacle; on the basis of adjustment processing of the path cleaning data and in combination with two-dimensional scanned path cleaning data output by the sweeper during previous path planning, planning and outputting a cleaning path, optimizing a new vector after smoothing, and storing the two-dimensional scanned path cleaning data related to the cleaning path to a server terminal of the sweeper.

Description

Technical Field

[0001] The invention relates to the technical field of path planning for a sweeper, and in particular to a path planning method for a sweeper. Background Art

[0002] Sweeper path planning is an important field in autonomous driving and robotics. It aims to enable sweepers to autonomously plan and perform cleaning tasks through algorithms and sensor technology. This process usually includes multiple links such as environmental perception, path generation, path adjustment and optimization. Specifically, the sweeper uses multiple sensors such as cameras, lidar, and infrared sensors to perceive the surrounding environment in real time, including road conditions and obstacle locations. Subsequently, based on the perceived environmental information, a preliminary cleaning path is generated. During the cleaning process, the path is dynamically adjusted and optimized according to the real-time perceived environmental changes to ensure efficient completion of the cleaning task.

[0003] At present, some existing path planning technologies for street sweepers are unable to accurately perceive and respond to all obstacles in complex and changing environments, such as crowded city streets or complex industrial parks, resulting in inaccurate path planning and poor environmental adaptability. In a dynamically changing environment, street sweepers need to be able to respond quickly to environmental changes and adjust their paths, but some existing technologies are insufficient in real time, which affects cleaning efficiency. In addition, some generated cleaning paths have sharp turns or frequent changes of direction, which not only increases the energy consumption and wear of the street sweeper, but may also affect the cleaning effect. Summary of the invention

[0004] The purpose of the present invention is to provide a path planning method for a sweeper, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, and the path planning method specifically includes the following steps:

[0006] Data collection: The sensor scanning module of the sweeper conducts a comprehensive two-dimensional scanning and recording of the cleaning area;

[0007] Data input and processing: Based on the sensor scanning module, the initial position and target position of the cleaning path are input through the path planning module, and the target direction vector of the path planning of the sweeper is evaluated and output according to the direction of sweeping from the initial position to the target position. The sensor scanning of the sweeper identifies the position of obstacles within the target direction vector during the cleaning path, and outputs the adjustment processing of the path cleaning data under the influence of the obstacle according to the position of the obstacle;

[0008] Path optimization: Based on the adjustment and processing of the path cleaning data, combined with the two-dimensional scanning path cleaning data output during the previous path planning of the sweeper, the cleaning path is planned and output, and the new direction after smoothing is optimized;

[0009] Data upload: The path cleaning data related to the cleaning path scanned in two dimensions is stored in the server terminal of the cleaning vehicle;

[0010] The equipment used in the sensor scanning module includes laser radar, camera, ultrasonic sensor, and the equipment used in the path planning module includes vehicle-mounted computer and control system.

[0011] Optionally, the evaluation process of the target direction quantity evaluation unit is as follows:

[0012] F=(f x , f y );

[0013]

[0014] in:

[0015] F is the direction vector index;

[0016] M x is the x-axis index of the target position, M y Target position y-axis index, (M x , M y ) is the two-dimensional target position;

[0017] C x is the x-axis index of the initial position, C y is the y-axis index of the initial position, (C x , C y ) is the two-dimensional initial position;

[0018] f x is the x-axis change index of the direction quantity, f y Direction quantity y-axis change index;

[0019] Based on the sensor scanning module and the path planning module, within the two-dimensional scanning environment of the cleaning area, the two-dimensional initial position (C x , C y ) and the two-dimensional target position (M x , M y ), using two-dimensional coordinates, through the direction vector F = (f x , f y ) in the two components f x and f y , the evaluation reflects the change from the initial position (C x , C y ) to the target position (M x , M y ) and then output the direction vector index F.

[0020] Optionally, the adjustment process of the path adjustment unit under the influence of the obstacle is as follows:

[0021]

[0022] in:

[0023] F new It is the new direction volume index after adjustment;

[0024] δ is the obstacle adjustment factor, which reflects the intensity adjustment degree of avoiding obstacles;

[0025] Z x is the x-axis index of the obstacle position, Z y is the y-axis index of the obstacle position, (Z x , Z y ) is the two-dimensional obstacle position;

[0026] The following are the numerical indices in the target direction evaluation unit cited in this unit algorithm: F is the direction vector index, C x is the x-axis index of the initial position, C y is the y-axis index of the initial position;

[0027] Through the sensor scanning module, it is assumed that there is a two-dimensional obstacle position (Z x , Z y ), based on the target direction quantity evaluation unit evaluating the output F = (f x , f y ), and the path planning module adjusts the obstacle adjustment factor δ, and the two-dimensional coordinate axis of the new direction position after the adjustment is output as F new =(f xnew , f ynew ), where f xnew is the x-axis index of the new direction after adjustment, f ynew is the y-axis index of the adjusted new direction quantity, that is, the adjusted new direction quantity index F new .

[0028] Optionally, the optimization process of the path smoothing optimization unit is as follows:

[0029]

[0030] in:

[0031] It is the new direction index after optimization;

[0032] Q x is the x-axis index of the direction vector at the previous moment, Q y is the y-axis index of the direction vector at the previous moment, (Q x, Q y ) is the two-dimensional direction coordinate axis of the previous path planning of the sweeper;

[0033] τ is the direction change adjustment factor, which reflects the new and The weight distribution of control between them;

[0034] Based on F new =(f xnew , f ynew ), and the two-dimensional direction vector (Q x , Q y ), using the direction change adjustment factor τ, the two-dimensional coordinate axis of the new direction quantity after optimization is That is, the new direction index after optimization

[0035] Optionally, the obstacle adjustment factor δ is used in the path planning module to adjust the degree of obstacle avoidance behavior of the sweeper when encountering an obstacle. The degree of control adjustment of the obstacle adjustment factor δ when encountering an obstacle is as follows:

[0036] S1. The closer the obstacle is to the sweeper, the more it is necessary to avoid collision between the obstacle and the sweeper, so the degree of adjustment of the obstacle adjustment factor δ increases;

[0037] S2. The larger the obstacle, the larger the space the sweeper needs to avoid, so the degree of adjustment of the obstacle adjustment factor δ increases;

[0038] S3. The faster the sweeper is, the greater the reaction time and adjustment range required for obstacle avoidance. Therefore, the degree of adjustment change of obstacle adjustment factor δ increases;

[0039] S4. Different sweepers have different steering capabilities. For sweepers with poor steering capabilities, the degree of change of the adjustment factor δ is reduced to avoid oversteering.

[0040] Optionally, the direction change adjustment factor τ is used to balance the change in the sweeping direction of the sweeper path, and the factors for adjusting the change of the direction change adjustment factor τ include the size, mass, steering ability of the sweeper, the distribution of obstacles, the width and curvature of the road, and the sweeping accuracy, speed, and energy consumption of the sweeper;

[0041] In a narrow cleaning space, if the sweeper needs to avoid obstacles more flexibly, a smaller direction change adjustment factor τ value needs to be set; in an open area, if the sweeper needs to maintain stability and straight-line driving ability, a larger direction change adjustment factor τ value is set.

[0042] Optionally, the optimized new direction quantity index output by the path smoothing optimization unit is Right now It is possible to iterate the target direction quantity evaluation unit (M) in the next path planning of the sweeper x , M y ) and use it as input data to continuously optimize the path planning of the sweeper.

[0043] Optionally, the obstacle adjustment factor δ in the path adjustment unit under the obstacle influence can be dynamically adjusted according to the inducement of the direction change adjustment factor τ output by the path smoothing optimization unit to form a closed-loop optimization.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. Through precise environmental perception and real-time path adjustment, the present invention enables the sweeper to complete the cleaning task more efficiently, reduce the phenomenon of repeated cleaning and ineffective movement caused by improper path planning, and improve the cleaning efficiency of the sweeper.

[0046] Second, the present invention utilizes a smooth cleaning path, reduces sharp turns and frequent changes of direction of the cleaning vehicle, thereby reducing energy consumption and mechanical wear and extending the service life of the cleaning vehicle.

[0047] 3. The present invention adopts a more intelligent and efficient path planning method for the sweeper, which can bring better user experience and improve user satisfaction and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a method flow chart of the path planning method for the road sweeper;

[0049] Figure 2 This is a schematic diagram of the units involved in the sensor scanning module in the path planning method for the sweeper;

[0050] Figure 3 This is a schematic diagram of the units involved in the path planning module in this road sweeper path planning method. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The path planning method for this sweeper is different from the existing path planning method for sweepers. The existing path planning method for sweepers is difficult to accurately perceive and respond to all obstacles, resulting in inaccurate path planning and poor environmental adaptability. In a dynamically changing environment, the sweeper needs to be able to quickly respond to environmental changes and adjust the path. In addition, some generated cleaning paths have sharp turns or frequent changes of direction. The algorithm unit accurately calculates the target direction vector, so that the sweeper can more accurately determine the direction of the cleaning path, reduce path planning errors caused by direction deviations, consider the impact of obstacles in real time, dynamically adjust the path to avoid obstacles, ensure the smooth progress of the cleaning task, and smooth the generated cleaning path to reduce sharp turns and frequent changes of direction, thereby reducing the energy consumption and wear of the sweeper.

[0053] For examples, see Figures 1 to 3 This embodiment provides a path planning method for a sweeper, and the path planning method specifically includes the following steps:

[0054] Data collection: The sensor scanning module of the sweeper conducts a comprehensive two-dimensional scanning and recording of the cleaning area;

[0055] Data input and processing: Based on the sensor scanning module, the initial position and target position of the cleaning path are input through the path planning module, and the target direction vector of the path planning of the sweeper is evaluated and output according to the direction of sweeping from the initial position to the target position. The sensor scanning of the sweeper identifies the position of obstacles within the target direction vector during the cleaning path, and outputs the adjustment processing of the path cleaning data under the influence of the obstacle according to the position of the obstacle;

[0056] Path optimization: Based on the adjustment and processing of the path cleaning data, combined with the two-dimensional scanning path cleaning data output during the previous path planning of the sweeper, the cleaning path is planned and output, and the new direction after smoothing is optimized;

[0057] Data upload: The path cleaning data related to the cleaning path scanned in two dimensions is stored in the server terminal of the cleaning vehicle;

[0058] The equipment used in the sensor scanning module includes laser radar, camera, ultrasonic sensor, and the equipment used in the path planning module includes vehicle-mounted computer and control system.

[0059] The lidar, camera, and ultrasonic sensor used in the sensor scanning module can be used to sense and scan obstacles in the cleaning area and obtain the current position, target position, and obstacle shape information of the sweeper. The on-board computer is used to execute path planning algorithms and calculations. The control system is responsible for receiving the planned path information and controlling the sweeper to travel along the planned path. Due to the combination of multiple sensors such as lidar, camera, and ultrasonic sensor, a more comprehensive environmental perception is achieved, thereby improving the sweeper's ability to adapt to complex environments.

[0060] In this embodiment, the system combines the three algorithm units to form a path planning method based on the existing road sweeper. new and The three calculation results can achieve significant progress, not only solving the problems of poor environmental adaptability, insufficient real-time performance and poor path smoothness, but also bringing a more efficient, smarter and more environmentally friendly cleaning experience. F is the direction vector index. By accurately calculating the target direction vector, the sweeper can more accurately determine the direction of the cleaning path and reduce path planning errors caused by direction deviation. new In order to adjust the new direction index, during the path planning process, the impact of obstacles is considered in real time, and the path is dynamically adjusted to avoid obstacles to ensure the smooth progress of the cleaning task. In order to optimize the new direction index, the generated cleaning path is smoothed to reduce sharp turns and frequent changes of direction, reduce the energy consumption and wear of the sweeper, and F can be used according to Dynamically adjust the path. The calculation results can also affect the calculation of the next iteration of the sweeper path planning F, making the three algorithms of this system have a high correlation and entanglement, so that the overall algorithm system can perform automatic feedback and optimization according to the actual situation to be closer to reality.

[0061] See also Figures 1 to 3 , the evaluation process of the target direction evaluation unit is as follows:

[0062] F=(f x , f y );

[0063]

[0064]

[0065] in:

[0066] F is the direction vector index;

[0067] M x is the x-axis index of the target position, M yTarget position y-axis index, (M x , M y ) is the two-dimensional target position;

[0068] C x is the x-axis index of the initial position, C y is the y-axis index of the initial position, (C x , C y ) is the two-dimensional initial position;

[0069] f x is the x-axis change index of the direction quantity, f y Direction quantity y-axis change index;

[0070] Based on the sensor scanning module and the path planning module, the two-dimensional initial position (C x , C y ) and the two-dimensional target position (M x , M y ), using two-dimensional coordinates, through the direction vector F = (f x , f y ) in the two components f x and f y , the evaluation reflects the change from the initial position (C x , C y ) to the target position (M x , M y ) and then output the direction vector index F.

[0071] In this embodiment: First, in this algorithm unit, the x-axis index M of the target position can be accurately obtained through the sensor scanning module and the path planning module. x 、Target position y-axis index M y , Initial position x-axis index C x , Initial position y-axis index C y , which are the two necessary conditions for path planning of the sweeper: the two-dimensional target position (M x , M y ) and the two-dimensional initial position (C x , C y ), and then the target direction evaluation unit inputs the evaluation and outputs the direction vector index F;

[0072] In this algorithm unit, the direction vector index F is used to calculate the basic direction vector of the sweeper from the current position to the target position. It is also the basis for the path planning of the sweeper and provides a direction reference for subsequent path adjustment and optimization. By calculating the vector difference between two points and normalizing it, the unit direction vector is obtained, ensuring the accuracy and consistency of the direction.

[0073] See also Figures 1 to 3 ,The adjustment process of the path adjustment unit under the influence of obstacles is as follows:

[0074]

[0075] in:

[0076] F new It is the new direction volume index after adjustment;

[0077] δ is the obstacle adjustment factor, which reflects the intensity adjustment degree of avoiding obstacles;

[0078] Z x is the x-axis index of the obstacle position, Z y is the y-axis index of the obstacle position, (Z x , Z y ) is the two-dimensional obstacle position;

[0079] The following are the numerical indices in the target direction evaluation unit cited in this unit algorithm: F is the direction vector index, C x is the x-axis index of the initial position, C y is the y-axis index of the initial position;

[0080] Through the sensor scanning module, it is assumed that there is a two-dimensional obstacle position (Z x , Z y ), based on the target direction quantity evaluation unit evaluating the output F = (f x , f y ), and the path planning module adjusts the obstacle adjustment factor δ, and the two-dimensional coordinate axis of the new direction position after the adjustment is output as F new =(f xnew , f ynew ), where f xnew is the x-axis index of the new direction after adjustment, f ynew is the y-axis index of the adjusted new direction quantity, that is, the adjusted new direction quantity index F new ;

[0081] The obstacle adjustment factor δ is used in the path planning module to adjust the degree of obstacle avoidance behavior of the sweeper when encountering obstacles. The obstacle adjustment factor δ is used to adjust the degree of control when encountering obstacles as follows:

[0082] S1. The closer the obstacle is to the sweeper, the more it is necessary to avoid collision between the obstacle and the sweeper, so the degree of adjustment of the obstacle adjustment factor δ increases;

[0083] S2. The larger the obstacle, the larger the space the sweeper needs to avoid, so the degree of adjustment of the obstacle adjustment factor δ increases;

[0084] S3. The faster the sweeper is, the greater the reaction time and adjustment range required for obstacle avoidance. Therefore, the degree of adjustment change of obstacle adjustment factor δ increases;

[0085] S4. Different sweepers have different steering capabilities. For sweepers with poor steering capabilities, the degree of change of the adjustment factor δ is reduced to avoid oversteering.

[0086] In this embodiment, firstly, based on the direction vector index F output by the target direction quantity evaluation unit, the obstacle phenomenon in the cleaning process of the sweeper is taken into account, and the two-dimensional obstacle position (Z x , Z y ), the sensor scanning module can sense obstacles in the surrounding environment in real time and provide the location information of the obstacles. If multiple obstacles are detected, the path adjustment unit under the influence of obstacles needs to calculate the adjustment vector for each obstacle separately and comprehensively consider all the adjustment vectors to determine the final path planning direction. This requires the use of more complex path planning algorithms, such as the A algorithm, RRT algorithm and dynamic window method (DWA), which can handle multiple constraints and find the optimal or feasible path;

[0087] The obstacle adjustment factor δ is used to adjust the obstacle avoidance adjustment degree of the sweeper when encountering an obstacle in path planning. Specifically, the obstacle adjustment factor δ determines the degree to which the sweeper deviates from the original direction when facing an obstacle, that is, the degree of the direction vector index F. A larger obstacle adjustment factor δ value will cause the sweeper to deviate more strongly from the original direction to avoid the obstacle; while a smaller obstacle adjustment factor δ value means that the sweeper will drive closer to the original direction while making only slight adjustments. The obstacle adjustment factor δ depends on the distance, size, speed and steering ability of the obstacle. It directly affects the path selection of the sweeper, thereby ensuring that the sweeper can safely avoid obstacles and continue to move towards the target. In practical applications, the specific value of the obstacle adjustment factor δ is dynamically calculated through empirical formulas, table lookup methods, and machine learning algorithms. For example, a basic obstacle adjustment factor δ value is set based on the distance between the obstacle and the sweeper and the speed of the sweeper, and then fine-tuned according to other factors.

[0088] See also Figures 1 to 3 , the optimization process of the path smoothing optimization unit is as follows:

[0089]

[0090] in:

[0091] It is the new direction index after optimization;

[0092] Qx is the x-axis index of the direction vector at the previous moment, Q y is the y-axis index of the direction vector at the previous moment, (Q x , Q y ) is the two-dimensional direction coordinate axis of the previous path planning of the sweeper;

[0093] τ is the direction change adjustment factor, which reflects the new and The weight distribution of control between them;

[0094] Based on F new =(f xnew , f ynew ), and the two-dimensional direction vector (Q x , Q y ), using the direction change adjustment factor τ, the two-dimensional coordinate axis of the new direction quantity after optimization is That is, the new direction index after optimization

[0095] The direction change adjustment factor τ is used to balance the change in the sweeping direction of the sweeper path. The factors that influence the direction change adjustment factor τ include the size, mass, steering ability of the sweeper, the distribution of obstacles, the width and curvature of the road, the sweeper's cleaning accuracy, speed, and energy consumption.

[0096] In a narrow cleaning space, if the sweeper needs to avoid obstacles more flexibly, a smaller direction change adjustment factor τ value needs to be set; in an open area, if the sweeper needs to maintain stability and straight-line driving ability, a larger direction change adjustment factor τ value is set.

[0097] In this embodiment, the algorithm unit combines the two-dimensional direction vector (Q x , Q y ), and F new =(f xnew , f ynew ), through the weighted renormalization optimization adjustment of the direction change adjustment factor τ, ensuring the smooth processing of the path smoothness optimization unit can help reduce the sharp turns in the path and make the movement of the sweeper smoother;

[0098] In this algorithm unit, regarding the factors that induce the adjustment of the direction change adjustment factor τ, it is worth further explaining that the size, mass, and steering ability of the sweeper will affect the response speed and stability of the sweeper when changing direction. The distribution of obstacles, the width and curvature of the road will affect the complexity of the sweeper's obstacle avoidance and the frequency of direction changes. The sweeper's cleaning accuracy, speed, and energy consumption will determine the factors that the sweeper needs to give priority to when planning a path. In actual applications, experiments and simulations are used to test the impact of different values ​​of the direction change adjustment factor τ on the behavior of the sweeper, and the appropriate value of the direction change adjustment factor τ is selected based on the test results. In addition, some advanced path planning algorithms will dynamically adjust the value of the direction change adjustment factor τ based on real-time environmental information and the status of the sweeper to achieve more intelligent and flexible path planning.

[0099] The specific role of the direction change adjustment factor τ is to balance the direction change of the sweeper. In path planning, the sweeper needs to smoothly transition from one direction to another to avoid sharp turns, which may cause instability or unnecessary energy consumption. The direction change adjustment factor τ is calculated by adjusting the new direction index F new And the optimized new direction index To achieve this, the direction change adjustment factor τ mainly improves the smoothness of the path and the stability of the sweeper. By adjusting the value of the direction change adjustment factor τ, the behavior of the sweeper in avoiding and adjusting the direction can be controlled to make it more in line with the actual operation needs. For example, when the value of the direction change adjustment factor τ is large, the sweeper will be more inclined to maintain the two-dimensional direction coordinate axis (Q x , Q y ); When the value of the direction change adjustment factor τ is small, the sweeper will be more inclined to the two-dimensional coordinate axis of the optimized new direction output by the path smoothing optimization unit

[0100] See also Figures 1 to 3 , the optimized new direction index output by the path smoothing optimization unit Right now It can iterate the target direction evaluation unit in the next path planning of the sweeper (M x , M y ) and use it as input data to continuously optimize the path planning of the sweeper;

[0101] The obstacle adjustment factor δ in the path adjustment unit under the influence of the obstacle can be dynamically adjusted according to the inducement of the direction change adjustment factor τ output by the path smoothing optimization unit to form a closed-loop optimization.

[0102] In this embodiment, the algorithm unit is based on the optimized new direction index F output by the path smoothing optimization unit. n → ew , which can be used as the target direction of the target direction evaluation unit in the next iteration. Especially when the target position needs to be reached in steps, the obstacle adjustment factor δ in the path adjustment unit under the influence of obstacles can be dynamically adjusted according to the path quality output by the path smoothing optimization unit, including path length and number of turns, to form a closed-loop optimization.

[0103] To sum up, in the specific implementation process, iterating the target direction quantity evaluation unit usually means repeatedly using the target direction quantity evaluation unit to update the direction vector index F in the continuous steps of path planning. During the iteration process, these values ​​can be dynamically adjusted according to the current position, target position and possible obstacle positions of the sweeper. The purpose of iteration is to gradually approach the target position while avoiding obstacles to ensure that the sweeper can complete the task safely and efficiently.

[0104] In each iteration, the new direction index is optimized using the results of the path smoothing optimization unit As the input of the "target direction" of the target direction quantity evaluation unit in the next iteration, the sweeper can continuously optimize its driving path according to the current environmental information and previous driving experience. The significance of iteration is to gradually approach the optimal solution and feasible solution through continuous small adjustments, so as to realize the autonomous navigation and path planning of the sweeper. The target direction quantity evaluation unit, the path adjustment unit under the influence of obstacles and the path smoothing optimization unit, utilize the continuous control and adjustment of the two-dimensional position, namely the x and y coordinates, to jointly constitute the core part of the sweeper path planning. The interdependence and cyclic influence between them ensure that the sweeper can drive autonomously and safely in a complex environment.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A path planning method for a sweeper, characterized in that: The path planning method specifically includes the following steps: Data collection: The sensor scanning module of the sweeper conducts a comprehensive two-dimensional scanning and recording of the cleaning area; Data input and processing: Based on the sensor scanning module, the initial position and target position of the cleaning path are input through the path planning module, and the target direction vector of the path planning of the sweeper is evaluated and output according to the direction of sweeping from the initial position to the target position. The sensor scanning of the sweeper identifies the position of obstacles within the target direction vector during the cleaning path, and outputs the adjustment processing of the path cleaning data under the influence of the obstacle according to the position of the obstacle; Path optimization: Based on the adjustment and processing of the path cleaning data, combined with the two-dimensional scanning path cleaning data output during the previous path planning of the sweeper, the cleaning path is planned and output, and the new direction after smoothing is optimized; Data upload: The path cleaning data related to the cleaning path scanned in two dimensions is stored in the server terminal of the cleaning vehicle.

2. The path planning method for a sweeper according to claim 1, characterized in that: The equipment used in the sensor scanning module includes laser radar, camera, ultrasonic sensor, and the equipment used in the path planning module includes vehicle-mounted computer and control system.

3. The path planning method for a sweeper according to claim 2, characterized in that: The evaluation process of the target direction evaluation unit is as follows: F=(f x ,f y ); in: F is the direction vector index; M x is the x-axis index of the target position, M y Target position y-axis index, (M x , M y ) is the two-dimensional target position; C x is the x-axis index of the initial position, C y is the y-axis index of the initial position, (C x , C y ) is the two-dimensional initial position; f x is the x-axis change index of the direction quantity, f y Direction quantity y-axis change index; Based on the sensor scanning module and the path planning module, within the two-dimensional scanning environment of the cleaning area, the two-dimensional initial position (C x , C y ) and the two-dimensional target position (M x , M y ), using two-dimensional coordinates, through the direction vector F = (f x , f y ) in the two components f x and f y , the evaluation reflects the change from the initial position (C x , C y ) to the target position (M x , M y ) and then output the direction vector index F.

4. The path planning method for a sweeper according to claim 3, characterized in that: The adjustment process of the path adjustment unit under the influence of the obstacle is as follows: in: F new It is the new direction volume index after adjustment; δ is the obstacle adjustment factor, which reflects the intensity adjustment degree of avoiding obstacles; Z x is the x-axis index of the obstacle position, Z y is the y-axis index of the obstacle position, (Z x , Z y ) is the two-dimensional obstacle position; The following are the numerical indices in the target direction evaluation unit cited in this unit algorithm: F is the direction vector index, C x is the x-axis index of the initial position, C y is the initial position y-axis index; Through the sensor scanning module, it is assumed that there is a two-dimensional obstacle position (Z x , Z y ), based on the target direction quantity evaluation unit evaluating the output F = (f x , f y ), and the path planning module adjusts the obstacle adjustment factor δ, and the two-dimensional coordinate axis of the new direction position after the adjustment is output as F new =(f xnew , f ynew ), where f xnew is the x-axis index of the new direction after adjustment, f ynew is the y-axis index of the adjusted new direction quantity, that is, the adjusted new direction quantity index F new .

5. The path planning method for a sweeper according to claim 4, characterized in that: The optimization process of the path smoothing optimization unit is as follows: in: It is the new direction index after optimization; Q x is the x-axis index of the direction vector at the previous moment, Q y is the y-axis index of the direction vector at the previous moment, (Q x , Q y ) is the two-dimensional direction coordinate axis of the previous path planning of the sweeper; τ is the direction change adjustment factor, which reflects the new and The weight distribution of control between them; Based on F new =(f xnew , f ynew ), and the two-dimensional direction vector (Q x , Q y ), using the direction change adjustment factor τ, the two-dimensional coordinate axis of the new direction quantity after optimization is That is, the new direction index after optimization 6. The path planning method for a sweeper according to claim 5, characterized in that: The obstacle adjustment factor δ is used in the path planning module to adjust the degree of obstacle avoidance behavior of the sweeper when encountering obstacles. The control adjustment degree of the obstacle adjustment factor δ when encountering obstacles is as follows: S1. The closer the obstacle is to the sweeper, the more it is necessary to avoid collision between the obstacle and the sweeper, so the degree of adjustment of the obstacle adjustment factor δ increases; S2. The larger the obstacle, the larger the space the sweeper needs to avoid, so the degree of adjustment of the obstacle adjustment factor δ increases; S3. The faster the sweeper is, the greater the reaction time and adjustment range required for obstacle avoidance. Therefore, the degree of adjustment change of obstacle adjustment factor δ increases; S4. Different sweepers have different steering capabilities. For sweepers with poor steering capabilities, the degree of change of the adjustment factor δ is reduced to avoid oversteering.

7. The path planning method for a sweeper according to claim 6, characterized in that: The direction change adjustment factor τ is used to balance the change in the sweeping direction of the sweeper path. The factors that influence the change in the direction change adjustment factor τ include the size, mass, and steering ability of the sweeper, the distribution of obstacles, the width and curvature of the road, and the sweeping accuracy, speed, and energy consumption of the sweeper. In a narrow cleaning space, if the sweeper needs to avoid obstacles more flexibly, a smaller direction change adjustment factor τ value needs to be set; in an open area, if the sweeper needs to maintain stability and straight-line driving ability, a larger direction change adjustment factor τ value is set.

8. The path planning method for a sweeper according to claim 7, characterized in that: The optimized new direction index output by the path smoothing optimization unit Right now It is possible to iterate the target direction quantity evaluation unit (M) in the next path planning of the sweeper x , M y ) and use it as input data to continuously optimize the path planning of the sweeper.

9. The path planning method for a sweeper according to claim 8, characterized in that: The obstacle adjustment factor δ in the path adjustment unit under the obstacle influence can be dynamically adjusted according to the inducement of the direction change adjustment factor τ output by the path smoothing optimization unit to form a closed-loop optimization.

Images