A path planning method and device for a sweeper based on multiple control centers

Abstract

The present invention discloses a path planning method and device for a sweeper based on multiple control centers, which relates to the technical field of path planning for sweepers. The following scheme is proposed, which includes configuring a dual-mode control system for the sweeper, including an independent driving control center and a sweeping control center; establishing a driving control coordinate system based on the center of the vehicle's rear axle, and simultaneously establishing a sweeping control coordinate system based on the sweeping brush inertia compensation result, and generating an inertia compensation projection point as a transition coordinate for switching between the dual control modes; sensing the road environment in real time, and dynamically dividing the area type according to the obstacle distance and road width. The present invention adopts an independent control architecture for the driving and sweeping dual coordinate systems. The driving coordinate system is established based on the center of the vehicle's rear axle, and the sweeping coordinate system accurately reflects the operating posture by dynamically compensating for the sweeping brush inertia offset. The two are switched through the inertia compensation projection point, thereby improving the accuracy of the sweeping path and effectively solving the path deviation problem caused by the traditional single coordinate system.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning for cleaning vehicles, and in particular to a path planning method and device for cleaning vehicles based on multiple control centers. Background Art

[0002] Traditional path planning methods for street sweepers generally adopt a rigid body kinematic model based on a single fixed reference point, such as the vehicle's center of mass or rear axle center, and generate a global path through a unified coordinate system. This planning method based on a single fixed reference has high computational efficiency and is relatively simple to implement. It performs well in standard configuration vehicles such as passenger cars, but it is difficult to adapt to the special operational requirements of street sweepers.

[0003] Conventional path planning methods use fixed reference points to establish a single coordinate system, which cannot take into account the dual requirements of driving stability and cleaning accuracy. They also cannot accurately reflect the posture changes during sweeping operations, and produce systematic path deviations under turning or rapid extension conditions. Therefore, we propose a sweeper path planning method and device based on multiple control centers to solve this problem. Summary of the Invention

[0004] In order to solve the above technical problems, a path planning method and device for a sweeper based on multiple control centers are provided. This technical solution solves the problems that the rigid body kinematic modeling using a single coordinate reference proposed in the above background technology cannot adapt to the non-rigid characteristics of the dynamic extension of the sweeper's sweeping brush mechanism, resulting in a deviation between the planned path and the actual cleaning requirements; the planning system based on fixed control points is difficult to track the changes in the sweeping brush position in real time, affecting the path tracking accuracy; and the use of a unified collision detection weight lacks the ability to make adaptive adjustments based on environmental characteristics and sweeping brush status.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a path planning method for a road sweeper based on multiple control centers, the method comprising:

[0006] Step 1: Configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center;

[0007] Step 2: Establish a driving control coordinate system based on the center of the vehicle's rear axle. Simultaneously, establish a sweeping control coordinate system based on the sweeping brush inertia compensation results, and generate inertia compensation projection points as transition coordinates for switching between dual control modes.

[0008] Step 3: Real-time perception of the road environment, dynamic division of area types based on obstacle distance and road width, and differentiated configuration of collision weights and expansion coefficients;

[0009] Step 4: Based on the collaborative work of the dual control centers, the configured collision weights and expansion coefficients are comprehensively applied to generate feasible operation paths for the cleaning vehicle in stages.

[0010] Furthermore, a sweeper path planning device based on multiple control centers is proposed, which is used to implement any of the above-mentioned sweeper path planning methods based on multiple control centers, including:

[0011] Dual-mode control module, used to configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center;

[0012] A multi-source information processing module is used to establish a driving control coordinate system based on the center of the vehicle's rear axle, and a sweeping control coordinate system based on the sweeping brush inertia compensation results, and to generate inertia compensation projection points as transition coordinates for switching between dual control modes;

[0013] The environment adaptation parameter configuration module is used to perceive the road environment in real time, dynamically divide the area type according to the obstacle distance and road width, and differentially configure the collision weight and expansion coefficient;

[0014] The multimodal path generation module is used to generate feasible operation paths for cleaning vehicles in stages based on the collaborative work of the dual control centers and the comprehensive application of configured collision weights and expansion coefficients.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention adopts an independent control architecture for driving and cleaning dual coordinate systems. The driving coordinate system is established based on the center of the vehicle's rear axle to ensure driving stability. The cleaning coordinate system accurately reflects the operating posture by dynamically compensating for the inertial offset of the sweeping brush. The two are switched through the inertia compensation projection point to improve the cleaning path accuracy and effectively solve the path deviation problem caused by the traditional single coordinate system. By real-time perception of the road environment, dynamic adjustment of the collision weight and expansion coefficient, and combined with the garbage coverage rate trigger mode switching, the optimal path planning for the entire scene is achieved, the success rate of edge cleaning is improved, and it automatically returns to its position after avoiding obstacles, which significantly improves operating efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a path planning method for a road sweeper based on multiple control centers proposed by the present invention;

[0018] Figure 2 A flow chart of the method for dynamically configuring control parameters in the present invention;

[0019] Figure 3 This is a structural block diagram of a sweeper path planning device based on multiple control centers proposed by the present invention. DETAILED DESCRIPTION

[0020] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0021] Reference Figure 1-3 As shown, a path planning method for a road sweeper based on multiple control centers includes:

[0022] Step 1: Configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center;

[0023] Step 2: Establish a driving control coordinate system based on the center of the vehicle's rear axle. Simultaneously, establish a sweeping control coordinate system based on the sweeping brush inertia compensation results, and generate inertia compensation projection points as transition coordinates for switching between dual control modes.

[0024] Step 3: Real-time perception of the road environment, dynamic division of area types based on obstacle distance and road width, and differentiated configuration of collision weights and expansion coefficients;

[0025] Step 4: Based on the collaborative work of the dual control centers, the configured collision weights and expansion coefficients are comprehensively applied to generate feasible operation paths for the cleaning vehicle in stages.

[0026] In an embodiment of the present invention, the driving control coordinate system is established based on the center of the vehicle's rear axle, the sweeping control coordinate system is established based on the sweeping inertia compensation result, and the inertia compensation projection point is generated as the transition coordinate for switching between the dual control modes. Specifically, the process includes:

[0027] Collect vehicle chassis parameters and real-time position information of the sweeping device;

[0028] According to the wheelbase parameters, the coordinates of the midpoint of the line connecting the center points of the two wheel hubs are calculated and defined as the rear axle center reference point. A right-handed coordinate system is constructed with this reference point as the origin as the driving control center coordinate system.

[0029] Based on the brush posture information, the real-time geometric center point of each brush is extracted, the inertia offset vector of the brush during extension is calculated through the kinematic model, and the inertia compensation of the brush geometric center point is performed;

[0030] The geometric center coordinates of the compensated sweeping brushes are calculated by arithmetic averaging to solve the overall geometric center coordinates of the multiple sweeping brushes. The right-handed coordinate system is established based on the overall geometric center coordinates as the sweeping control center coordinate system.

[0031] According to the change of the sweeping brush posture, the inertia compensation projection point of the rear axle center is generated, and the projection point is used as the transition reference point for switching from the driving control reference system to the sweeping control reference system.

[0032] In an embodiment of the present invention, obtaining the inertial offset vector during the extension of the sweep brush through kinematic calculation and compensating the geometric center point of the sweep brush specifically includes:

[0033] Get the current position of each brush , sweep brush fully extended position and maximum inertial deviation angle ;

[0034] Get the brush extension speed , maximum extension speed and the sweep direction vector, and calculate the normalized direction based on the sweep direction vector ;

[0035] Specifically, the brush extension speed is obtained through the built-in encoder of the brush drive mechanism, and the fixed offset of the brush mounting base relative to the rear axle center is obtained. The current angle is obtained through the joint encoder of the brush. The coordinates of the end in the local coordinate system of the brush are calculated in combination with the length of the brush arm. The coordinates are transformed into the travel control center coordinate system through a homogeneous transformation matrix to obtain the current position of the brush, and the direction vector of the brush is calculated. The brush is controlled to perform full-stroke extension movement with maximum driving force, and the shortest completion time is recorded using the built-in encoder as the minimum time for full extension. The maximum extension speed is calculated in combination with the length of the brush arm. The joint encoder records the current angle, and the fully extended position of the brush is obtained in the same way as the current position of the brush. At the same time, an inertial measurement unit is installed at the end of the brush, and the maximum inertial offset angle is obtained by integrating the angular velocity data.

[0036] Construct the convex hull of the current position and the fully extended position of the brush, and extract the convex hull vertex set , according to the formula Calculation frontier points ;

[0037] By the formula Calculate the inertia compensation vector of each brush, where is the inertia compensation vector, is the geometric offset, is the speed weight;

[0038] By the formula Calculate the geometric center point of the sweep brush after inertia compensation, where: The current geometric center point of the brush.

[0039] Reference Figure 2 As shown, the inertia compensation projection point of the rear axle center is generated based on the sweeping posture information, specifically including:

[0040] By calculating the ratio of the distance between the inertia compensation center point and the rear axle center to the wheelbase, the positional relationship between the two is quantified as a coupling factor;

[0041] Get the current extension length of the sweeping brush, calculate the ratio of the current extension length to the maximum extension length of the sweeping brush, and calculate the overall extension ratio using the arithmetic average method , adjust the coupling factor based on the linear mapping of the overall stretch ratio to generate a dynamic coupling factor ;

[0042] Get the vehicle's real-time steering angle and the center of the vehicle's rear axle r, combined with the dynamic coupling factor, is given by the formula Calculate the projection center point as the inertia compensation projection point, where is the projection center point, is the vehicle wheelbase.

[0043] In an embodiment of the present invention, the control parameters are dynamically configured according to the real-time road environment, and the control parameters include collision weight and expansion coefficient, specifically including:

[0044] Get vehicle width , Current distance to the nearest obstacle and road width ;

[0045] When the distance to the nearest obstacle is greater than half the road width, the current road environment is determined to be a square area. When the distance to the nearest obstacle is less than or equal to half the road width, the current road environment is determined to be a narrow road area. Based on the narrow road area, if the distance to the nearest obstacle is less than 0.3 meters and it is a fixed obstacle, the road environment on that side is determined to be an edge area.

[0046] According to the calculation model Analyze the basic collision weight of the cleaning vehicle in different road environments, among which, is the base collision weight, is the basic collision weight in the open area, is the safety standard collision weight;

[0047] For reference, according to the industrial vehicle safety standard, the open area basic collision weight is 0.2, and the safety standard collision weight is 0.7;

[0048] It should be noted that Indicates that the obstacle is outside the road boundary and belongs to a completely open square area. The basic collision weight value is directly used. Indicates that the obstacle is within the road boundary and the weight needs to be dynamically adjusted based on the distance.

[0049] When it is determined to be a side-touching area, the collision weight of the cleaning vehicle close to the curb is set to , the collision weight on the other side is set to ;

[0050] The vehicle size in the vehicle algorithm is set according to the road environment, and the ratio of the vehicle size in the vehicle algorithm to the vehicle width is recorded as the expansion coefficient.

[0051] In an embodiment of the present invention, generating a feasible operation path for the cleaning vehicle based on the control center in combination with the collision weight and the expansion coefficient specifically includes:

[0052] At the start-up stage of the cleaning vehicle, the driving control center is activated, and an environmental perception system based on multi-sensor fusion is established to collect road topology information in real time. Based on the road topology information and vehicle parameters, the expansion coefficient is dynamically adjusted. ;

[0053] By the formula Calculating the coefficient of expansion ,in The maximum expansion coefficient is 1.5, The minimum expansion coefficient is 1.1;

[0054] Based on the driving control center coordinate system and taking the road centerline as the reference trajectory, the path is offset to the side away from the roadside based on the minimum mandatory interval distance, and the offset path is smoothed and optimized to generate the driving path; according to the formula Analyze the minimum mandatory separation distance between the cleaning vehicle and the curb, where is the minimum mandatory separation distance, is the safety margin constant;

[0055] Obtain garbage distribution data within a predetermined range in front of the cleaning vehicle and calculate the real-time garbage coverage rate. If the garbage coverage rate of the road surface detected for a continuous preset length exceeds the set threshold, the cleaning vehicle switches to cleaning mode and the expansion coefficient returns to the initial value;

[0056] Specifically, the laser radar scans the area 10 meters in front of the vehicle to obtain three-dimensional point cloud data and segment the ground valid point cloud. At the same time, the visual sensor collects images of the same area, uses the target detection algorithm to identify garbage targets in the image and outputs the confidence level. The valid point cloud and the visual detection results are spatially aligned and mapped to a preset 20×20 ground grid. For each grid cell, a weighted calculation is performed combining the point cloud density and the confidence level to obtain the garbage density value of the grid cell. The number of grid cells with garbage density values greater than 0.4g / cm² is calculated, and the ratio of this number to the total number of grid cells is the garbage coverage rate. When the garbage coverage rate is greater than 0.7 for three consecutive detection cycles, the vehicle operation mode is switched to cleaning mode. The geometric characteristic parameters of the curb are obtained through the environmental perception system, and combined with the coordinate system of the cleaning control center, a cleaning path parallel to the curb is generated.

[0057] Based on the road environment and collision weight, combined with road topology information, an obstacle avoidance path is generated. When the cleaning vehicle passes the obstacle, it automatically switches back to the cleaning path.

[0058] Specifically, obstacles are modeled in three dimensions based on the environmental perception system, and their safety envelope range is accurately calculated. The safety expansion radius is adjusted based on the collision weight and vehicle speed, and a multidimensional constraint space is established that includes collision cost, curvature limit and dynamic constraint. The RRT path search algorithm is used to solve the optimal obstacle avoidance path within the constraint space.

[0059] Reference Figure 3 As shown, this solution proposes a sweeper path planning device based on multiple control centers, which is used to implement the above-mentioned sweeper path planning method based on multiple control centers, including:

[0060] Dual-mode control module, used to configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center;

[0061] A multi-source information processing module is used to establish a driving control coordinate system based on the center of the vehicle's rear axle, and a sweeping control coordinate system based on the sweeping brush inertia compensation results, and to generate inertia compensation projection points as transition coordinates for switching between dual control modes;

[0062] The environment adaptation parameter configuration module is used to perceive the road environment in real time, dynamically divide the area type according to the obstacle distance and road width, and differentially configure the collision weight and expansion coefficient;

[0063] The multimodal path generation module is used to generate feasible operation paths for cleaning vehicles in stages based on the collaborative work of the dual control centers and the comprehensive application of configured collision weights and expansion coefficients.

[0064] The multi-source information processing module specifically includes:

[0065] Vehicle chassis information acquisition unit, used to obtain clean wheel speed, steering angle and IMU data;

[0066] The sweeping brush posture detection unit is used to collect the angle and extension status of each sweeping brush joint.

[0067] The environment adaptation parameter configuration module specifically includes:

[0068] The ring road feature recognition unit is used to monitor the ratio of obstacle distance to road width in real time, and divide the road environment into three categories: square area, narrow road area and edge area;

[0069] The collision weight calculation unit is used to set differentiated collision weights for different road environments.

[0070] The multimodal path generation module specifically includes:

[0071] A driving path generation unit is used to calculate the safety offset by using the minimum mandatory separation distance, and to generate a driving path by optimizing the trajectory using cubic spline interpolation based on the road centerline;

[0072] The cleaning path decision unit is used to detect the density of road debris in real time. When the coverage rate of consecutive road sections exceeds the set threshold, it triggers a mode switch, resets the expansion coefficient to the initial value, and generates an operation path that is parallel to the road edge.

[0073] The obstacle avoidance path generation unit is used to dynamically assess the obstacle collision risk, generate a temporary obstacle avoidance path within the constrained space, and automatically restore to the original operating path after passing the obstacle.

[0074] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A path planning method for a road sweeper based on multiple control centers, characterized in that: The method comprises: Step 1: Configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center; Step 2: Establish a driving control coordinate system based on the center of the vehicle's rear axle. Simultaneously, establish a sweeping control coordinate system based on the sweeping brush inertia compensation results, and generate inertia compensation projection points as transition coordinates for switching between dual control modes. Step 3: Real-time perception of the road environment, dynamic classification of area types based on obstacle distance and road width, and differentiated configuration of collision weights and expansion coefficients. The vehicle size in the vehicle algorithm is set based on the road environment, and the ratio of the vehicle size in the vehicle algorithm to the vehicle width is recorded as the expansion coefficient; Step 4: Based on the collaborative work of the two control centers, the configured collision weights and expansion coefficients are comprehensively applied to generate feasible operation paths for the cleaning vehicle in stages; The method of establishing a driving control coordinate system based on the center of the vehicle's rear axle and a sweeping control coordinate system based on the sweeping inertia compensation result, and generating an inertia compensation projection point as a transition coordinate for switching between the dual control modes, specifically includes: Collect vehicle chassis parameters and real-time position information of the sweeping device; According to the wheelbase parameters, the coordinates of the midpoint of the line connecting the center points of the two wheel hubs are calculated and defined as the rear axle center reference point. A right-handed coordinate system is constructed with this reference point as the origin as the driving control center coordinate system. Based on the brush posture information, the real-time geometric center point of each brush is extracted, the inertia offset vector of the brush during extension is calculated through the kinematic model, and the inertia compensation of the brush geometric center point is performed; The geometric center coordinates of the compensated sweeping brushes are calculated by arithmetic averaging to solve the overall geometric center coordinates of the multiple sweeping brushes. The right-handed coordinate system is established based on the overall geometric center coordinates as the sweeping control center coordinate system. According to the change of the sweeping brush posture, the inertia compensation projection point of the rear axle center is generated, and the projection point is used as the transition reference point for switching from the driving control reference system to the sweeping control reference system.

2. The method according to claim 1, characterized in that The calculation of the inertia offset vector during the sweeping brush extension process by using a kinematic model and the inertia compensation of the sweeping brush geometric center point specifically include: Get the current position of each brush , sweep brush fully extended position and maximum inertial deviation angle ; Get the brush extension speed , maximum extension speed and the sweep direction vector, and calculate the normalized direction based on the sweep direction vector ; Construct the convex hull of the current position and the fully extended position of the brush, and extract the convex hull vertex set , according to the formula Calculation frontier points ; By the formula Calculate the inertia compensation vector of each brush, where is the inertia compensation vector, is the geometric offset, is the speed weight; By the formula Analyze the geometric center point of the sweep brush after inertia compensation, where: The current geometric center point of the brush.

3. The method according to claim 1, characterized in that Generating the inertia compensation projection point of the rear axle center according to the change in the sweeping brush posture specifically includes: By calculating the ratio of the distance between the inertia compensation center point and the rear axle center to the wheelbase, the positional relationship between the two is quantified as a coupling factor; Get the current extension length of the sweeping brush, calculate the ratio of the current extension length to the maximum extension length of the sweeping brush, and use the weighted average method to calculate the overall extension ratio , adjust the coupling factor based on the linear mapping of the overall stretch ratio to generate a dynamic coupling factor ; Get the vehicle's real-time steering angle and the center of the vehicle's rear axle r, combined with the dynamic coupling factor, is given by the formula Calculate the projection center point as the inertia compensation projection point, where is the projection center point, is the vehicle wheelbase.

4. The method according to claim 1, wherein The step 3 specifically includes: Get vehicle width , Current distance to the nearest obstacle and road width ; When the distance to the nearest obstacle is greater than half the road width, the current road environment is determined to be a square area. When the distance to the nearest obstacle is less than or equal to half the road width, the current road environment is determined to be a narrow road area. Based on the narrow road area, if the distance to the nearest obstacle is less than 0.3 meters and it is a fixed obstacle, the road environment on that side is determined to be an edge area. According to the calculation model Analyze the basic collision weight of the cleaning vehicle in different road environments, among which, is the base collision weight, is the basic collision weight of the open area, is the safety standard collision weight; When it is determined to be a side-touching area, the collision weight of the cleaning vehicle close to the curb is set to , the collision weight on the other side is set to .

5. The method according to claim 1, characterized in that The step 4 specifically includes: At the start-up stage of the cleaning vehicle, the driving control center is activated, and an environmental perception system based on multi-sensor fusion is established to collect road topology information in real time. Based on the road topology information and vehicle parameters, the expansion coefficient is dynamically adjusted. ; Based on the driving control center coordinate system and taking the road centerline as the reference trajectory, the path is offset to the side away from the roadside based on the minimum mandatory interval distance, and the offset path is smoothed and optimized to generate the driving path; according to the formula Analyze the minimum mandatory separation distance between the cleaning vehicle and the curb, where is the minimum mandatory separation distance, is the safety margin constant; Obtain garbage distribution data within a predetermined range in front of the cleaning vehicle and calculate the real-time garbage coverage rate. If the garbage coverage rate of the road surface detected for a continuous preset length exceeds the set threshold, the cleaning vehicle switches to cleaning mode and the expansion coefficient returns to the initial value; The geometric characteristic parameters of the curb are obtained through the environmental perception system, and combined with the coordinate system of the cleaning control center, a cleaning path parallel to the curb is generated; Based on the road environment and collision weight, combined with road topology information, an obstacle avoidance path is generated. When the cleaning vehicle passes the obstacle, it automatically switches back to the cleaning path.

6. A sweeper path planning device based on multiple control centers, characterized in that: A method for implementing a path planning method for a road sweeper based on multiple control centers as described in any one of claims 1 to 5, comprising: Dual-mode control module, used to configure a dual-mode control system for the cleaning vehicle, including independent driving control center and cleaning control center; A multi-source information processing module is used to establish a driving control coordinate system based on the center of the vehicle's rear axle, and a sweeping control coordinate system based on the sweeping brush inertia compensation results, and to generate inertia compensation projection points as transition coordinates for switching between dual control modes; The environment adaptation parameter configuration module is used to perceive the road environment in real time, dynamically divide the area type according to the obstacle distance and road width, and differentially configure the collision weight and expansion coefficient; The multimodal path generation module is used to generate feasible operation paths for cleaning vehicles in stages based on the collaborative work of the dual control centers and the comprehensive application of configured collision weights and expansion coefficients.

7. The path planning device for a road sweeper based on multiple control centers according to claim 6, characterized in that: The multi-source information processing module specifically includes: Vehicle chassis information acquisition unit, used to obtain clean wheel speed, steering angle and IMU data; The sweeping brush posture detection unit is used to collect the angle and extension status of each sweeping brush joint.

8. The path planning device for a road sweeper based on multiple control centers according to claim 6, characterized in that: The environment adaptation parameter configuration module specifically includes: The ring road feature recognition unit is used to monitor the ratio of obstacle distance to road width in real time, and divide the road environment into three categories: square area, narrow road area and edge area; The collision weight calculation unit is used to set differentiated collision weights for different road environments.

9. The path planning device for a road sweeper based on multiple control centers according to claim 6, characterized in that: The multimodal path generation module specifically includes: A driving path generation unit is used to calculate the safety offset by using the minimum mandatory separation distance, and to generate a driving path by optimizing the trajectory using cubic spline interpolation based on the road centerline; The cleaning path decision unit is used to detect the density of road debris in real time. When the coverage rate of consecutive road sections exceeds the set threshold, it triggers a mode switch, resets the expansion coefficient to the initial value, and generates an operation path that is parallel to the road edge. The obstacle avoidance path generation unit is used to dynamically assess the obstacle collision risk, generate a temporary obstacle avoidance path within the constrained space, and automatically restore to the original operating path after passing the obstacle.

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