Mowing control method and mowing robot

Abstract

The application provides a mowing control method and a mowing robot. The method comprises: in response to a trajectory deviation event of the mowing robot, acquiring a work map of the mowing robot and a deviation starting point and a deviation ending point of the mowing robot in the trajectory deviation event; determining a target loop trajectory according to the work map, the deviation starting point and the deviation ending point; and controlling the mowing robot to move to a target mowing work point along the target loop trajectory, the target mowing work point and the deviation starting point satisfying a preset relative position relationship. The mowing control method can improve the work integrity and work efficiency of the mowing robot, and ensure the continuity and regularity of the edge abutting work.

Description

Technical Field

[0001] This application relates to the field of lawnmower technology, and more particularly to lawnmower control methods and lawnmower robots. Background Technology

[0002] With the rapid development of smart garden equipment technology, intelligent lawnmowers have been widely used in lawn maintenance in home courtyards, public green spaces, and other scenarios. In existing lawnmower operation scenarios, if the lawnmower slips or deviates due to environmental obstacles, it will select a return point in the area ahead of the slip point on its original working trajectory. It will then plan a direct return path from the slip point to the return point, controlling the lawnmower to move directly towards the return point, eventually returning to its original working trajectory and continuing the lawnmower operation.

[0003] It is evident that the aforementioned processing mechanism after the lawnmower deviates only restores the state of operation on the original work trajectory, without performing any backtracking or completion processing on the work path lost during the trajectory deviation. The missed mowing areas formed by this deviation event can only be covered by subsequent global secondary sweeping operations, which not only increases the lawnmower's operation time and energy consumption, reducing overall operation efficiency, but also makes it difficult for secondary global sweeping to achieve accurate full coverage of missed mowing areas at boundary dead corners and irregular boundaries, further affecting the final operation quality. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a mowing control method and a mowing robot to achieve coverage and re-mowing of missed areas after the mowing robot's trajectory deviates along the edge, thereby improving the efficiency of edge operations.

[0005] In a first aspect, this application provides a method for controlling lawn mowing, comprising: In response to a trajectory deviation event of the lawnmower robot, the operation map of the lawnmower robot, as well as the starting point and ending point of the deviation of the lawnmower robot in the trajectory deviation event are obtained; The target loop trajectory is determined based on the work map, the offset start point, and the offset end point; The lawnmower robot is controlled to move along the target loop trajectory to the target mowing point, and the target mowing point and the offset starting point satisfy a preset relative positional relationship.

[0006] In an optional implementation, determining the target loop trajectory based on the work map, the offset start point, and the offset end point includes: Candidate loop trajectories are determined from the work map based on the offset start point and the offset end point; Select the target loop trajectory from the candidate loop trajectories.

[0007] In an optional implementation, selecting the target loop trajectory from the candidate loop trajectories includes: The preset indicators for determining the candidate loop trajectory include at least one of the following: loop trajectory in-situ spin angle, loop trajectory length or loop trajectory time, and loop trajectory missed cut rate. The comprehensive score of the candidate loop trajectory is determined based on the preset indicators; The target loop trajectory is selected from the candidate loop trajectories based on the comprehensive score.

[0008] In an optional implementation, determining the comprehensive score of the candidate loop trajectory based on the preset index includes: The original indicator data of the preset indicators are normalized for the influence of indicator dimensions and orders of magnitude to obtain the target indicator data. The comprehensive score of the candidate loop trajectory is calculated based on the weight coefficients of the preset indicators and the target indicator data.

[0009] In an optional implementation, determining candidate loop trajectories from the work map based on the offset start point and the offset end point includes: The offset endpoint is designated as the starting point of the candidate loop trajectory; The offset starting point is designated as the target mowing point; Using the starting point of the trajectory as the search starting point and the target mowing point as the search ending point, the candidate loop trajectory is determined in the operation map according to a multi-directional search strategy.

[0010] In an optional implementation, the multi-directional search strategy includes at least one of the following search directions: The search direction from the search endpoint directly to the search starting point; The search direction is to first search in the direction of the mowed lawn from the search endpoint, and then search in the direction of the search starting point; The search direction is to first search towards the unmowed grass from the search endpoint, and then search towards the search starting point.

[0011] In an optional implementation, the target mowing point is located on the operation map and behind the offset starting point, where "behind" refers to the direction from the offset starting point toward the unmowed area.

[0012] In an optional implementation, the preset relative positional relationship is: The relative distance between the target mowing point and the offset starting point is less than a preset distance.

[0013] In an optional implementation, the preset relative positional relationship is: The trajectory line between the target mowing point and the offset starting point is parallel to the reference trajectory line of the missed mowing area of ​​the trajectory offset event.

[0014] In an optional implementation, after controlling the mowing robot to move along the target loop trajectory to the target mowing point, the method further includes: Continue mowing operations from the target mowing point.

[0015] Secondly, this application provides a lawn mowing robot, including a memory and a processor. The memory stores a computer program, and the computer program executes the lawn mowing control method of the lawn mowing robot provided in the embodiments of this application when the processor is running.

[0016] Thirdly, this application provides a control device for a lawnmower robot, the device comprising: The information acquisition module is used to acquire the operation map of the lawn mower robot and the starting point and ending point of the lawn mower robot in the trajectory deviation event in response to the trajectory deviation event. The trajectory determination module is used to determine the target loop trajectory based on the work map, the offset start point, and the offset end point; The mobile control module is used to control the lawn mowing robot to move along the target loop trajectory to the target lawn mowing point, and the target lawn mowing point and the offset starting point satisfy a preset relative position relationship.

[0017] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, executes the lawn mowing control method described in any of the foregoing embodiments.

[0018] The lawn mowing control method provided in this application responds to the trajectory deviation event of the lawn mowing robot, obtains the work map, the starting point of the deviation, and the ending point of the deviation, determines the target loop trajectory, and controls the robot to move along the target loop trajectory to the target lawn mowing work point that satisfies the preset relative positional relationship with the starting point of the deviation. This allows the lawn mowing robot to completely cover the missed mowing trajectory between the starting point and the ending point of the deviation when it arrives at the target lawn mowing work point according to the target loop trajectory and starts mowing. That is, it fully covers the missed mowing area in this trajectory deviation event. In this way, the lawn mowing robot achieves high coverage and no dead-angle replenishment function for the missed mowing area in the trajectory deviation event, without the need to perform additional global secondary sweeping operation, effectively improving the work integrity and work efficiency of the lawn mowing robot, while ensuring the continuity and regularity of the edge-to-edge operation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the working conditions of the lawnmower robot provided in a comparative embodiment of this application; Figure 2 This is a schematic diagram of the steps of the lawn mowing control method provided in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the steps for determining the target loop trajectory provided in an embodiment of this application; Figure 4 This is a schematic diagram of the target loop trajectory filtering process provided in the embodiments of this application; Figure 5 This is a flowchart of the scoring data processing for the target loop trajectory provided in an embodiment of this application; Figure 6 This is a flowchart illustrating the generation of candidate loop trajectories based on a multi-directional search strategy, provided in an embodiment of this application. Figure 7 This is a schematic diagram of the operation of the lawnmower robot along a straight, smooth loop trajectory provided in this application embodiment; Figure 8 This is a schematic diagram of the operation of the lawnmower robot along a multi-segment loop trajectory provided in the embodiments of this application; Figure 9 This is a schematic diagram of the operation of the lawnmower robot along a closed-loop trajectory provided in the embodiments of this application; Figure 10 This is a schematic diagram of the structure of the lawnmower robot provided in the embodiments of this application; Figure 11 This is a schematic diagram of the control device for the lawnmower robot provided in the embodiments of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0023] In this document, references to "embodiment" or "implementation" mean that a particular feature, structure, or characteristic described in connection with an embodiment or implementation may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] Before introducing the technical solution of this application, let's go over the technical issues in related technologies in detail.

[0025] The current conventional edge-cutting control scheme for lawnmowers is as follows: The lawnmower pre-maps the area, generating an electronic map and planning a continuous, preset edge-cutting trajectory. During operation, the robot uses a pose detection module and an environmental perception module to compare its own pose with the preset trajectory in real time, maintaining operation along the trajectory through closed-loop control. However, in complex outdoor conditions, sudden situations such as slippery ground, navigating slopes, and getting stuck on foreign objects can cause the robot's real-time pose to deviate from the preset trajectory. For this scenario, existing technologies generally employ a control logic for rapid regression along the edge-cutting trajectory. This means aiming to restore the operational state in the shortest possible time, selecting a target exploration point in the area ahead of the preset trajectory based on the robot's current deviation, planning the shortest return path from the current position to that point, and controlling the robot to directly return forward to resume normal operation.

[0026] Please see Figure 1 A typical scenario is as follows: The lawnmower robot moves forward along a preset trajectory from A1, slips at B1, and continues to deviate from its pose until it becomes controllable at B2; the existing technology will select an exploration point A2 ahead of the preset trajectory, plan a direct cut-back path from B2 to A2, control the robot to move directly to A2 and return to the edge operation.

[0027] It is evident that the aforementioned existing technical solutions only focus on the rapid recovery of the robot's edge-following operation state, without considering the requirements for full coverage of the work area and precise edge-following operation. In actual complex outdoor working conditions, they have the following defects: large areas of missed cutting are easily formed, as the boundaries between the area traversed and the deviated trajectory and the preset trajectory are not covered when the robot slips from B1 to B2; the edge-following effect is poor, as the existing technology skips the boundary of the deviation section, resulting in uneven cutting at the boundary; there is a lack of a backtracking and completion mechanism, and the missed cutting area can only be covered by subsequent secondary sweeping, increasing the operation time and energy consumption; there are safety hazards, as when the posture deviation is large after slipping, the return path is likely to exceed the boundary of the electronic map, which may cause the robot to drive out of the work area, collide with obstacles, or even fall.

[0028] Please see Figure 2 Therefore, this application provides a method for controlling lawn mowing, the method comprising the following steps: S10. In response to the trajectory deviation event of the lawnmower robot, obtain the operation map of the lawnmower robot, and the starting point and ending point of the deviation of the lawnmower robot in the trajectory deviation event.

[0029] The trajectory deviation event refers to an event in which the lawnmower robot encounters sudden interference such as slippery ground, driving on a slope, getting stuck in a foreign object, an accidental collision, or external disturbance while performing edge mowing operations along a preset edge operation trajectory, causing its posture to deviate from the preset edge operation trajectory and exceed the normal operation deviation range.

[0030] The operation map is an electronic map pre-built by the lawnmower robot through a mapping module, which includes the legal boundaries of the operation area, the distribution of obstacles, the workable range, and the preset operation trajectory along the edge.

[0031] The offset starting point is the initial position where the lawnmower robot begins to deviate from its pose. The offset ending point is the termination position where the interference is eliminated and the lawnmower robot regains controllability.

[0032] In one possible embodiment, the lawnmower robot monitors its own pose data in real time during the edge-cutting operation along a preset edge-cutting trajectory using its onboard pose detection module and environmental perception module. The preset edge-cutting trajectory is a continuous edge-cutting path pre-planned by the lawnmower robot based on a work map. When the deviation between the real-time pose and the preset edge-cutting trajectory exceeds a preset threshold, the lawnmower robot determines that a trajectory deviation event has occurred. At this time, the pose point at the start of the deviation is recorded as the deviation start point, and the pose point at the end of the deviation, when the robot resumes controllable movement, is recorded as the deviation end point. Simultaneously, a pre-generated electronic work map is read from memory, which includes information such as the legal boundaries of the work area, the distribution of obstacles, and the workable range.

[0033] S20. Determine the target loop trajectory based on the work map, the offset start point, and the offset end point.

[0034] The target loop trajectory can be understood as a continuous path connecting the offset endpoint and the target mowing point.

[0035] Specifically, after acquiring the work map, the offset start point, and the offset end point, the lawnmower plans the target loop trajectory based on information such as the work map, the offset start point, and the offset end point. In one possible embodiment, the lawnmower also plans the target loop trajectory based on boundary constraints, obstacle distribution in the work map, and the lawnmower's own kinematic constraints.

[0036] In one possible embodiment, determining the target loop trajectory includes: generating multiple candidate loop trajectories from the work map based on the offset start point and offset end point; comprehensively scoring the multiple candidate loop trajectories based on preset evaluation indicators, and selecting the loop trajectory with the highest comprehensive score as the target loop trajectory. The preset evaluation indicators include, but are not limited to, trajectory length, in-situ spin angle, and missed cut rate.

[0037] S30. Control the lawn mowing robot to move along the target loop trajectory to the target lawn mowing point, and the target lawn mowing point and the offset starting point satisfy a preset relative position relationship.

[0038] Specifically, after determining the target loop trajectory, the mowing robot moves according to the path points and motion parameters of the target loop trajectory, eventually reaching the target mowing point. The target mowing point is the endpoint of the target loop trajectory, and the target mowing point and the offset starting point satisfy a preset relative positional relationship.

[0039] The target mowing point is the starting point where the mowing robot resumes normal edge mowing operations.

[0040] The preset relative positional relationship includes at least one of the following: the target mowing point is located behind the unmowed area pointed to by the offset starting point; the relative distance between the target mowing point and the offset starting point is less than a preset distance; and the trajectory line between the target mowing point and the offset starting point and the reference trajectory line of the unmowed area are parallel.

[0041] In one possible embodiment, the preset relative positional relationship is that the straight-line distance between the target mowing point and the offset starting point is less than a preset distance. In other words, the target mowing point is set at or near the offset starting point, for example, located behind the offset starting point along the original working direction, i.e., on one side of the already mowed area. In this embodiment, when the mowing robot continues to work forward along the preset edge trajectory from the target mowing point, its blade will cover the entire offset section boundary starting from the offset starting point, thereby achieving the re-mowing of the missed areas.

[0042] In another possible embodiment, the preset relative positional relationship is that the trajectory line between the target mowing operation point and the offset starting point is parallel to the reference trajectory line of the missed mowing area of ​​the trajectory offset event. Here, the reference trajectory line of the missed mowing area refers to the trajectory segment between the offset starting point and the offset ending point mapping point on the preset edge-following operation trajectory, and the offset ending point mapping point is the perpendicular foot of the offset ending point on the preset edge-following trajectory. This embodiment is applicable to reverse edge-following mowing scenarios, i.e., the mowing robot backtracks along the boundary in the opposite direction to the original operation direction.

[0043] As can be seen, compared with the existing mowing algorithms that directly switch back to the trajectory after trajectory deviation, resulting in large-area missed mowing, poor edge-fitting effect, need for secondary global sweeping, and low work efficiency, the mowing control method provided in this application, in response to the trajectory deviation event of the mowing robot, obtains the work map, the deviation start point and the deviation end point, determines the target loop trajectory, and controls the robot to move along the target loop trajectory to the target mowing work point that meets the preset relative position relationship with the deviation start point. This allows the mowing robot to completely cover the missed mowing trajectory between the deviation start point and the deviation end point when it arrives at the target mowing work point according to the target loop trajectory and starts mowing. That is, it fully covers the missed mowing area in this trajectory deviation event. In this way, the mowing robot achieves high coverage and no dead-angle replenishment function for the missed mowing area in the trajectory deviation event, without the need to perform additional global secondary sweeping, effectively improving the work integrity and work efficiency of the mowing robot, while ensuring the continuity and regularity of edge-fitting operation.

[0044] Please see Figure 3 The step of determining the target loop trajectory based on the work map, the offset start point, and the offset end point includes the following steps: S21. Based on the offset start point and the offset end point, determine the candidate loop trajectory from the work map.

[0045] In one possible embodiment, the lawnmower robot uses the offset endpoint as the starting point of the loop trajectory, i.e. the trajectory start point, and the offset starting point or a point near it as the target endpoint of the loop trajectory, i.e. the target lawnmower operation point. Under the constraints of the operation map, one or more possible loop trajectories are generated by the path search algorithm as candidate loop trajectories.

[0046] The methods for generating candidate loop trajectories include, but are not limited to: sampling-based path planning methods, such as Rapidly-exploring Random Tree (RRT) and Probabilistic Roadmap (PRM); search-based path planning methods, such as A-Star algorithm and Hybrid A-Star algorithm; and path planning methods based on parametric curves, where the parametric curves include, but are not limited to, Dubins curves, Reeds-Shepp curves, Bezier curves, or spline curves.

[0047] In a preferred embodiment of this application, a multi-directional search strategy is employed to generate candidate loop trajectories, i.e., path exploration is performed along different search directions. Specifically, the search directions include, but are not limited to: directly connecting a straight line or smooth curve from the offset endpoint to the offset starting point; first searching from the offset endpoint towards the mowed area, then turning to the offset starting point; first searching from the offset endpoint towards the unmowed area, then turning to the offset starting point, etc. Each search direction can generate one or more candidate trajectories with different geometric shapes.

[0048] In one possible embodiment, depending on different deviation conditions, the aforementioned multi-directional search strategy can specifically generate candidate loop trajectories for three scenarios: multi-segment loop trajectory, closed-loop loop trajectory, and direct-connection smooth loop trajectory. Specifically, the multi-segment loop trajectory scenario is suitable for situations where the robot's pose deviates significantly during edge-moving operations, such as when the lawnmower's pose deviates significantly from the preset edge-moving trajectory; based on the multi-directional search strategy, a multi-segment loop trajectory is generated. The closed-loop loop trajectory scenario is suitable for situations where the robot's pose deviates moderately during edge-moving operations, but its direction of travel does not change significantly. The direct-connection smooth loop trajectory scenario is suitable for situations where the robot's pose deviates slightly during edge-moving operations.

[0049] S22. Select the target loop trajectory from the candidate loop trajectories.

[0050] Specifically, after determining one or more candidate loop trajectories, the lawnmower robot needs to select one trajectory as the final target loop trajectory for execution. In one possible embodiment, the selection is based on pre-set evaluation indicators, which include, but are not limited to, total trajectory length, in-situ spin angle, missed mowing rate, and path smoothness. Among these, a shorter total trajectory length indicates less non-operational travel distance and higher efficiency; a smaller in-situ spin angle indicates less crushing and wear on the turf; a lower missed mowing rate indicates more complete re-mowing and coverage; and a smoother path indicates less impact on the mechanical transmission system.

[0051] Furthermore, the lawnmower robot calculates a weighted comprehensive score of the above indicators for each candidate trajectory, and selects the trajectory with the highest score or the lowest comprehensive cost as the target loop trajectory.

[0052] Furthermore, the weighting coefficients can be dynamically adjusted based on the current operating scenario and real-time sensing data. The operating scenario includes, but is not limited to, standard mode, high-quality mode, high-efficiency mode, and low-wear mode. The real-time sensing data includes, but is not limited to, remaining battery power, lawn density, and obstacle density, in order to achieve an adaptive balance between operating quality, lawn protection, and operating efficiency.

[0053] As can be seen, the lawn mowing control method provided in this embodiment first determines multiple candidate return trajectories from the work map based on the offset start point and offset end point, and then selects the optimal target return trajectory from the candidate return trajectories. This enables the return trajectory of the lawn mowing robot to accurately adapt to the current trajectory offset degree and work scenario, taking into account multiple needs such as missed mowing, lawn protection, and work efficiency. It achieves optimized screening and matching of return trajectories, avoids the situation of poor adaptability of a single return trajectory, and helps to improve the accuracy and adaptability of the lawn mowing robot in edge operation.

[0054] Please see Figure 4 The step of selecting the target loop trajectory from the candidate loop trajectories includes the following steps: S221. Determine the preset indicators of the candidate loop trajectory, wherein the preset indicators include at least one of the following: the in-situ spin angle of the loop trajectory, the length of the loop trajectory or the time of the loop trajectory, and the loop trajectory missing rate.

[0055] The in-situ spin angle of the loop trajectory refers to the maximum in-situ turning angle of the lawnmower robot as it moves along the candidate loop trajectory. Specifically, it can be determined based on the robot's orientation at the offset endpoint and the trajectory distribution of the candidate loop trajectory. For example, in the case of a multi-segment loop trajectory, since the robot needs to turn towards the inside of the work area after starting from the offset endpoint, its in-situ spin angle is usually large, such as 90°-180°; in the case of a closed-loop loop trajectory, the robot moves forward or backward along the boundary without needing to turn in place, and its in-situ spin angle is zero or extremely small; in the case of a direct-connection smooth loop trajectory, the robot directly reverses and turns smoothly, and its in-situ spin angle is small, such as less than 45°.

[0056] The length of the loop trajectory refers to the actual distance the lawnmower robot travels according to the candidate loop trajectory.

[0057] The loop trajectory time refers to the actual movement time of the lawnmower robot following the candidate loop trajectory.

[0058] The missed cutting rate of the loop trajectory refers to the missed cutting rate of the grass cutting trajectory when the grass cutting robot performs grass cutting based on the candidate loop trajectory.

[0059] S222. Determine the comprehensive score of the candidate loop trajectory based on the preset index.

[0060] Among them, the smaller the spin angle of the loop trajectory, the less wear on the turf; the shorter the loop trajectory length or loop trajectory time, the higher the mowing efficiency; and the smaller the missed mowing rate of the loop trajectory, the better the mowing integrity. The candidate loop trajectory with the highest comprehensive score is selected as the target loop trajectory, and the highest comprehensive score indicates that the candidate loop trajectory has the best overall evaluation performance.

[0061] In one possible embodiment, the determination process of the comprehensive score includes: First, converting negative indicators such as spin angle, trajectory length, and missed cut rate into positive ones. Specifically, spin angle, trajectory length, and missed cut rate are all negative indicators where smaller values ​​indicate better trajectory performance, and they need to be converted into positive ones to eliminate the difference in indicator direction. The positive conversion of spin angle uses a reverse mapping method, setting a reasonable threshold for spin angle as less than or equal to 90°, and the positive conversion formula is: The trajectory length forwarding method employs a threshold truncation approach. Based on the operational scenario, a reasonable threshold for trajectory length is set to be less than or equal to 5 meters. The forwarding formula is as follows: The forward transformation of the missed cut rate uses a linear inverse transformation, and the forward transformation formula is: .in, Let be the original spin angle of the i-th candidate trajectory, in degrees. Let be the original length of the i-th candidate trajectory, in meters. The original cutoff rate for the i-th candidate trajectory is expressed as %. These are the maximum values ​​of the corresponding indicators among all candidate trajectories.

[0062] Secondly, the positiveized indicators are mapped to the [0,1] interval for normalization. Then, dynamic weights are assigned based on real-time operating conditions such as lawn density, remaining battery power, and obstacle density. Specifically, the sum of all weight coefficients is 1, and the weight allocation rules are tailored to actual operational needs. For example, when lawn density is high (≥80%) and obstacle density is low (≤20%), priority is given to ensuring complete mowing, with a missed mowing rate weight of 0.5, a spin angle weight of 0.2, and a trajectory length weight of 0.3. When remaining battery power is low (≤30%) and lawn density is low (≤50%), priority is given to improving operational efficiency, with a trajectory length weight of 0.5, a spin angle weight of 0.2, and a missed mowing rate weight of 0.3. When obstacle density is high (≥40%) and remaining battery power is sufficient (≥60%), priority is given to protecting the lawn and avoiding robot collisions, with a spin angle weight of 0.4, a missed mowing rate weight of 0.3, and a trajectory length weight of 0.3.

[0063] Finally, the comprehensive score of the candidate loop trajectory is calculated using a weighted summation formula. For example, after normalization, the spin angle, trajectory length, and missed cut rate of a candidate trajectory are 0.7, 0.8, and 0.95, respectively. Under the current conditions of high lawn density and low obstacle density, the weights are allocated as follows: missed cut rate 0.5, spin angle 0.2, and trajectory length 0.3. Its comprehensive score is 0.7×0.2 + 0.8×0.3 + 0.95×0.5 = 0.14 + 0.24 + 0.475 = 0.855. The comprehensive score ranges from [0,1]. The higher the value, the better the comprehensive performance of the candidate loop trajectory. The trajectory with the highest comprehensive score can be selected as the target loop trajectory.

[0064] S223. Select the target loop trajectory from the candidate loop trajectories based on the comprehensive score.

[0065] Specifically, after calculating the comprehensive score of each candidate trajectory, the lawnmower robot can select the candidate trajectory with the highest comprehensive score as the target loop trajectory. If multiple trajectories have the same comprehensive score, for example, the difference is less than 0.001, they are compared in the following order of priority: the trajectory with the lowest missed mowing rate is selected first; if the missed mowing rate is still the same, the trajectory with the smallest spin angle is selected; if the spin angle is also the same, the trajectory with the shortest length is selected.

[0066] In one possible implementation, an abnormal trajectory filtering mechanism is added before scoring: if the original missing cut rate of a candidate trajectory exceeds 5%, meaning that more than 5% of the boundary is still not covered after completion, it is directly excluded; if the original spin angle exceeds 360°, meaning that more than one rotation in place is required, it is directly excluded; if the trajectory length exceeds twice the length of the shortest candidate trajectory, it is directly excluded. After filtering, the remaining candidate trajectories are scored and optimized to ensure that the final selected target loop trajectory is suitable for the current offset conditions.

[0067] As can be seen, the lawn mowing control method provided in this embodiment, by determining preset indicators such as the in-situ spin angle of the loop trajectory, the length or time of the loop trajectory, and the missed cutting rate of the loop trajectory, calculates a comprehensive score based on the preset indicators, and selects the target loop trajectory based on the comprehensive score, enables the lawn mowing robot to quantitatively select the best candidate trajectory from the dimensions of lawn protection, operation efficiency, and mowing integrity. This ensures that the selected target loop trajectory simultaneously meets the comprehensive requirements of low wear, high efficiency, and no missed cutting. In this way, the optimal selection of the lawn mowing robot's loop trajectory is achieved, balancing multiple operation performances, and is also conducive to further improving the overall quality and stability of edge operations.

[0068] Please see Figure 5 The step of determining the comprehensive score of the candidate loop trajectory based on the preset index includes the following steps: S2221. Normalize the original indicator data of the preset indicator in terms of indicator dimensions and order of magnitude to obtain the target indicator data.

[0069] Specifically, the preset indicators for the loop trajectory's in-situ spin angle, loop trajectory length or time, and loop trajectory missed cut rate have different physical dimensions and orders of magnitude. For example, the spin angle typically ranges from 0° to 360°, the trajectory length may range from 0 to 20 meters, and the missed cut rate ranges from 0% to 100%. If the raw values ​​are directly used for weighted summation, indicators with larger dimensions will dominate, leading to distorted scoring results; at the same time, the numerical differences between different indicators will also affect the effectiveness of the weighting coefficients. Therefore, before calculating the comprehensive score, the raw data of each indicator is normalized and mapped to a unified numerical range, such as [0, 1], to eliminate the influence of dimensions and orders of magnitude.

[0070] S2222. Calculate the comprehensive score of the candidate loop trajectory based on the weight coefficient of the preset index and the target index data.

[0071] Specifically, after obtaining the normalized target indicator data, the lawnmower robot assigns dynamic weight coefficients to each preset indicator based on the real-time operating conditions, with the sum of all weight coefficients being 1. It then calculates the comprehensive score for each candidate trajectory based on these weight coefficients. The target indicator data is multiplied by its corresponding weight coefficient one by one and then summed. The final result is the comprehensive score for that candidate loop trajectory, and the score value is positively correlated with the overall quality of the trajectory.

[0072] In one possible embodiment, the weighting coefficients are determined based on preset scenario templates: the system has built-in weighting templates for typical work scenarios, which users or upper-level scheduling systems can select according to actual needs. For example: in standard mode, the weighting coefficients for the in-situ spin angle of the loop trajectory are 0.3, the loop trajectory length is 0.3, and the loop trajectory missed cut rate is 0.4; in high-quality mode, the weighting coefficients for the in-situ spin angle of the loop trajectory are 0.4, the loop trajectory length is 0.2, and the loop trajectory missed cut rate is 0.4; in high-efficiency mode, the weighting coefficients for the in-situ spin angle of the loop trajectory are 0.2, the loop trajectory length is 0.5, and the loop trajectory missed cut rate is 0.3; and in low-wear mode, the weighting coefficients for the in-situ spin angle of the loop trajectory are 0.5, the loop trajectory length is 0.3, and the loop trajectory missed cut rate is 0.2.

[0073] The lawnmower robot can also dynamically fine-tune the weights based on real-time sensing data. For example, when the vision or sensors detect that the lawn density is greater than 80%, i.e., in a dense grass environment, spinning in place will cause greater damage to the turf. The weight coefficient of the spinning angle of the loop trajectory can be increased by 0.1, and the weight coefficient of the loop trajectory length can be decreased by 0.1. When the remaining battery is less than 20%, priority is given to completing the task. The weight coefficient of the loop trajectory length can be increased by 0.2, and the weight coefficient of the loop trajectory missed rate can be decreased by 0.1. The weight coefficient of the loop trajectory spinning angle can also be decreased by 0.1. When dense obstacles or complex boundary shapes are detected, the weight coefficient of the loop trajectory missed rate can be appropriately increased to ensure no missed mowing.

[0074] Among them, the lawnmower robot can also record the user's historical choices or manual intervention data for different work areas after obtaining user authorization, and automatically fit the weight coefficients that match the user's preferences through machine learning algorithms, such as linear regression or reinforcement learning.

[0075] In another possible embodiment, the preset indicators may further include a trajectory smoothness indicator: calculating the integral of the rate of change of curvature or the change in steering angular velocity of the candidate trajectory as a measure of mechanical shock and ride comfort, with the sum of all weights still equal to 1. Additionally, the preset indicators may further include an energy consumption indicator: estimating battery energy consumption based on trajectory length, speed curve, and slope information, normalizing it, and adding it to the score, with the sum of all weights still equal to 1. Furthermore, the preset indicators may further include a safety indicator: calculating the minimum distance between the candidate trajectory and the boundary of the work map or obstacles; a larger distance indicates higher safety, normalizing it, and adding it to the score as a positive indicator, with the sum of all weights still equal to 1.

[0076] In another possible implementation, for common map shapes and deviation patterns, normalized parameters for typical candidate trajectories can be pre-calculated and retrieved directly from a table at runtime, reducing online computation. When the number of candidate trajectories is too large, grid search or heuristic rules can be used, such as eliminating obviously inferior trajectories, to reduce the size of the candidate set. For multi-segmented loop trajectories, dynamic programming can be used to calculate the contribution of each segment's metrics, avoiding redundant calculations.

[0077] As can be seen, the lawn mowing control method provided in this embodiment, by normalizing the original data of preset indicators to eliminate the influence of dimensions and orders of magnitude, and combining the weight coefficients to calculate the comprehensive score, makes the comprehensive score result more objective and accurate, avoids trajectory selection deviation caused by excessive differences in indicator values, thereby achieving standardization and precision of the comprehensive score of the lawn mowing robot's return trajectory, ensuring that the selected target return trajectory is more in line with actual operation requirements, and is conducive to improving the reliability of trajectory decision-making and operation performance.

[0078] Please see Figure 6 The step of determining candidate loop trajectories from the work map based on the offset start point and the offset end point includes the following steps: S211. The offset endpoint is calibrated as the starting point of the candidate loop trajectory.

[0079] The offset endpoint is the termination position where interference is eliminated and driving controllability is restored in the trajectory offset event of the lawnmower robot. Marking it as the trajectory starting point of the candidate loop trajectory can ensure that the trajectory planning is based on the robot's current actual controllable pose, and that the operation along the original working direction starting from this point can cover the offset segment.

[0080] It should be noted that, in optional embodiments, the target mowing point may not be the offset starting point itself, but rather a point offset a small distance from the offset starting point along a preset trajectory, such as offsetting 0.1 meters in the direction of already mowed grass, to ensure overlapping coverage and avoid missing boundaries. However, for the sake of simplicity, this application directly uses the offset starting point as the target mowing point, and those skilled in the art can make minor adjustments according to actual needs.

[0081] S212. The offset starting point is calibrated as the target mowing operation point.

[0082] The offset starting point is the initial position where the mowing robot begins to deviate from its pose. Marking it as the target mowing operation point allows the subsequently planned return trajectory to point to the starting position of the trajectory offset, ensuring that the return trajectory can completely cover the missed mowing area formed during the trajectory offset process, and realizing the backtracking and completion of the operation path of the offset segment.

[0083] S213. Using the starting point of the trajectory as the search starting point and the target mowing point as the search ending point, determine the candidate loop trajectory in the operation map according to the multi-directional search strategy.

[0084] Specifically, after the search start point and search end point are marked, the lawnmower robot executes a multi-directional search strategy under the constraints of the work map to generate candidate loop trajectories from the start point to the end point.

[0085] The multi-directional search strategy refers to exploring feasible paths from multiple different directions during the path search process, rather than being limited to a single direction, based on the robot's current orientation, work boundary, and spatial environment, thereby obtaining a variety of candidate trajectories to adapt to different deviation conditions.

[0086] The process involves determining the candidate loop trajectory in the work map using the trajectory starting point as the search starting point and the target mowing point as the search ending point, according to a multi-directional search strategy. This includes conducting path searches in multiple directions based on the boundary information of the work map and the robot's pose data. For example, a smooth, direct path search can be performed directly from the trajectory starting point to the search ending point to generate a smooth, direct candidate loop trajectory. Another example is to first search towards the inside of the work area from the trajectory starting point to complete attitude calibration, and then perform a backtracking path search towards the search ending point to generate a multi-segment candidate loop trajectory. Yet another example is to first perform a forward anchoring search towards the work boundary from the trajectory starting point, and then perform a reverse backtracking search towards the search ending point to generate a closed-loop candidate loop trajectory.

[0087] As can be seen, the mowing control method provided in this embodiment, by marking the offset endpoint as the trajectory starting point of the candidate loop trajectory and the offset starting point as the target mowing operation point, and using both as the starting and ending points to determine the candidate loop trajectory through a multi-directional search strategy, allows the candidate loop trajectory to be planned around the actual starting and ending positions of the current trajectory offset. At the same time, the multi-directional search can generate multiple candidate trajectories adapted to different offset degrees. In this way, the candidate loop trajectory is generated in a targeted and diversified manner, ensuring that the loop trajectory can effectively cover the missed mowing area, and at the same time, it is conducive to improving the accuracy of trajectory planning and the adaptability of working conditions.

[0088] Optionally, the multi-directional search strategy includes at least one of the following search directions: The search direction from the search endpoint directly to the search starting point; The search direction is to first search in the direction of the mowed lawn from the search endpoint, and then search in the direction of the search starting point; The search direction is to first search towards the unmowed grass from the search endpoint, and then search towards the search starting point.

[0089] The search direction, which involves searching directly from the search endpoint to the search starting point, refers to planning a smooth path from the offset endpoint directly to the offset starting point, without passing through other intermediate anchor points or making long-distance detours. The purpose of this implementation is to complete the return-to-center operation with the shortest path length and the least travel time. It is suitable for situations where the robot's deviation is small and the space between the offset endpoint and the offset starting point is open and unobstructed.

[0090] Please see Figure 7 In one specific embodiment of this application, the search direction from the search endpoint directly to the search starting point corresponds to the generation of a straight, smooth loop trajectory, suitable for situations where a slight pose deviation occurs during edge-cutting operations of a lawnmower robot. In this situation, the lawnmower robot performs edge-cutting operations forward from position A1 along a preset edge-cutting trajectory. When it reaches position B1, it encounters a brief slippage or slight external disturbance, causing a slight deviation in pose from the preset edge-cutting trajectory. This deviation continues until it reaches position B4, at which point the disturbance is eliminated and the robot regains controllability. Position B1 is the starting point of the deviation, and position B4 is the ending point. At this point, the lateral and longitudinal offsets between the lawnmower robot and the preset edge-cutting trajectory are small, with only slight pose deviations, such as a lateral offset of less than 10cm and a heading angle deviation of less than 10°. Furthermore, there are no significant obstacles in the area between the ending point B4 and the starting point B1. Therefore, based on a multi-directional search strategy, a straight, smooth loop trajectory is generated sequentially along positions B4, A9, and A10. Among them, position A9 is the target mowing operation point, which is the backtracking calibration point on the preset edge trajectory, located at position B1 or a point adjacent to or behind position B1, and position A10 is the return operation point.

[0091] In one possible embodiment, when generating a smooth, straight loop trajectory, path generation can employ a combination of single or multiple arcs and straight lines, such as a Reeds-Shepp curve combining backward, left turn, and backward movement, or backward, right turn, and backward movement, ensuring that the minimum turning radius constraint of the lawnmower robot is met. Since the robot needs to gradually adjust its orientation during backward movement, the path typically includes a smooth turning arc segment, ensuring that the robot's heading is essentially aligned with the tangent direction of the preset edge trajectory when it reaches the target mowing point.

[0092] The search direction, which involves searching from the search endpoint towards the already mowed area and then back towards the search starting point, refers to starting from the offset endpoint, first moving in the opposite direction to the original working direction. This segment typically involves reverse movement accompanied by a turn away from the boundary, until reaching an intermediate calibration point. Then, from this calibration point, the robot moves towards the boundary and the original working direction, ultimately reaching the offset starting point. The purpose of this implementation is to adjust the robot's orientation and pose by first detouring towards the inside of the already mowed area, thus avoiding the risk of large-area missed mowing or collisions caused by directly cutting back.

[0093] Please see Figure 8In one specific embodiment of this application, the search direction—first searching towards the already mowed direction from the search endpoint, and then searching towards the search starting point—corresponds to the generation of a multi-segment loop trajectory, applicable to situations where a lawnmower robot experiences significant pose deviations during edge-cutting operations. In this situation, the lawnmower robot performs edge-cutting operations forward from position A1 along a preset edge-cutting trajectory. Upon reaching position B1, it encounters uncontrollable interference events such as slippage or external collisions, causing its pose to continuously deviate significantly from the preset edge-cutting trajectory until it reaches position B2, at which point the interference events are eliminated and the robot regains controllability. At this point, the robot's pose exhibits significant lateral and longitudinal offsets from the preset edge-cutting trajectory, and its travel direction deviates considerably from the original edge-cutting direction, for example, a lateral offset greater than 30cm and a heading angle deviation greater than 45°. Furthermore, the orientation of the offset endpoint B2 may intersect with or even turn away from the boundary direction. Therefore, based on a multi-directional search strategy, a multi-segment loop trajectory is generated sequentially along positions B2, A3, A4, and A5: The first segment starts from position B2, planning a smooth backward path towards the already mowed direction of the original work direction and the inner part of the work area, reaching the inner transfer calibration point A3. Position A3 is not located on the preset trajectory but within a safe area inside the work area. The second segment starts from position A3, planning a forward path towards the map boundary and the original work direction, reaching the backtracking and re-cutting starting point A4 on the preset edge trajectory, i.e., the offset starting point B1 or a point near it. Through this inward-outward, backward-forward path pattern, the robot can complete pose calibration without damaging the boundary turf and ensure complete coverage of the missed mowing area starting from position A4. Position A5 is the return work point.

[0094] In one possible embodiment, when generating multi-segment loop trajectories, multiple candidate trajectories are generated by adjusting the position of the transfer calibration point A3, for example, by adjusting the back distance and turning radius of position A3 relative to position B2. A longer back distance results in a smoother path but increases the area of ​​turf trampled; a larger turning radius results in a smoother path but requires more workspace. By discretely sampling these parameters, multiple multi-segment candidate loop trajectories with different shapes can be generated.

[0095] The search direction, which involves searching from the search endpoint towards the direction of uncut grass and then back towards the search starting point, refers to starting from the offset endpoint, first moving forward a certain distance along the original working direction to reach a forward boundary anchor point on a preset edge trajectory; then, starting from that anchor point, moving backward along the boundary in the opposite direction to the original direction until reaching the offset starting point. The purpose of this implementation is to first anchor the boundary forward and then move backward to complete the cutting, suitable for situations where the robot's deviation is moderate but its traveling direction is basically consistent with the original direction and it has the conditions to continue moving forward.

[0096] Please seeFigure 9 In one specific embodiment of this application, the search direction—searching first from the search endpoint towards the unmown area and then towards the search starting point—corresponds to the generation of a closed-loop trajectory. This is applicable to situations where the lawnmower robot experiences moderate lateral pose deviation during edge-cutting operations, but its travel direction does not deviate significantly. For example, the lateral deviation is between 10cm and 30cm, and the heading angle deviates from the original direction by less than 15°. Under these conditions, the lawnmower robot performs edge-cutting operations forward from position A1 along a preset edge-cutting trajectory. When it reaches position B1, it encounters interference such as slippage or external disturbances, causing its pose to deviate laterally towards the inside of the work area. This deviation continues until it reaches position B3, at which point the interference is eliminated and the robot regains controllability. At this point, the longitudinal deviation of the robot from the preset edge-cutting trajectory is small, and the deviation of its travel direction from the original edge-cutting direction is also small. Therefore, based on a multi-directional search strategy, a closed-loop trajectory is generated sequentially along positions B3, A6, A7, and A8. In this embodiment, position A6 is the forward boundary anchor point on the preset edge-trajectory, position A7 is the backtracking mowing start point on the preset edge-trajectory, located behind position B1, and position A8 is the trajectory calibration point. Specifically, the closed-loop trajectory provided in this embodiment includes: a first segment, starting from position B3, continuing forward along the original working direction to reach the forward boundary anchor point A6 on the preset edge-trajectory, for example, position A6 is located at a safe distance (e.g., 0.5 meters) from the perpendicular foot of position B3 on the preset trajectory along the forward direction; a second segment, starting from position A6, backtracking along the map boundary in the opposite direction to the original direction, performing reverse edge mowing, until reaching the backtracking mowing start point A7 on the preset edge-trajectory, i.e., the offset start point B1 or a point behind it. Through this closed-loop path of forward and backward movement, the mowing robot can completely cover the entire boundary segment from position A6 to position A7, ensuring no missed mowing.

[0097] In one possible embodiment, when generating a closed-loop trajectory, multiple candidate trajectories are generated by adjusting the forward travel distance and whether the reverse backtracking segment includes additional calibration points; that is, adjusting the offset of position A6 relative to the projection point B3. A longer forward travel distance results in higher accuracy of the anchoring boundary, but also increases the travel mileage. Multiple intermediate calibration points can be set during reverse backtracking to ensure pose smoothness. By discretely sampling these parameters, multiple closed-loop candidate trajectories with different shapes are generated.

[0098] In one possible embodiment, each search direction can generate one or more candidate loop trajectories that conform to the robot's kinematic constraints and are adapted to the current working conditions, based on different parameters such as the boundary constraints of the work map, the real-time pose of the lawnmower robot, and the degree of trajectory deviation. The candidate loop trajectories generated by different search directions complement each other to form a set of candidate trajectories covering various scenarios such as slight deviation, large deviation, and moderate deviation.

[0099] As can be seen, the multi-directional search strategy provided in this embodiment includes three search directions: direct search, searching first towards the already mowed direction and then searching towards the unmowed direction. These directions can be adapted to different trajectory deviation conditions of the mowing robot to generate paths. This allows the candidate loop trajectories to cover various scenarios such as slight deviation, large deviation, and moderate lateral deviation, thereby meeting the requirements for backtracking and re-mowing, attitude calibration, and boundary anchoring under different deviation conditions. This achieves conditional adaptation of trajectory planning and ensures that compliant and reasonable candidate trajectories can be generated under different deviation scenarios, which is beneficial to improving the stability and adaptability of the mowing robot's edge operation.

[0100] Optionally, the target mowing point is located in the operation map and behind the offset starting point, where "behind" refers to the direction from the offset starting point to the unmowed area.

[0101] The work map is a pre-built electronic map for the lawnmower robot, containing the legal boundaries of the work area, obstacle distribution, workable range, and preset edge-cutting trajectory. The target mowing point must be confined within the workable area of ​​the work map to prevent the lawnmower robot from leaving the work area and causing safety issues such as collisions, falls, and getting stuck. In this embodiment, limiting the target mowing point to the area behind the offset starting point (pointing to the uncut area) ensures that after the lawnmower robot reaches the point along the target loop trajectory, it can cover the missed areas caused by trajectory offset events when performing mowing operations along the preset edge-cutting trajectory. This achieves backtracking and completion of the deviated section of the work path, ensuring that there are no blind spots or missed areas in the edge-cutting operation, meeting the core requirement of refined edge-cutting operations.

[0102] Optionally, the preset relative positional relationship is: the relative distance between the target mowing point and the offset starting point is less than a preset distance.

[0103] The preset distance is an empirical threshold obtained based on statistical analysis of historical trajectory offset events. Its value is determined by combining parameters such as the coverage radius of the mowing robot's cutting disc, the accuracy requirements for edge operation, and motion control errors, in order to ensure that the target mowing operation point and the offset starting point are kept close enough.

[0104] In one possible embodiment, for the aforementioned scenario of generating a multi-segment loop trajectory, a preset distance determines the position of the backtracking and recutting starting point A4 relative to the offset starting point B1. For example, if the target mowing point is position A4, the distance along the trajectory between it and position B1 is set to 0.3 meters, which is less than the preset distance of 0.5 meters. When the robot starts mowing along the preset trajectory from position A4, its cutter head begins to cover from 0.3 meters behind position B1, ensuring that point B1 itself is completely cut and no uncovered grass strips are left due to positioning errors or turning delays.

[0105] In another possible embodiment, for the aforementioned closed-loop trajectory generation scenario, the target mowing point is A7, and the relative distance between it and the offset starting point B1 is also set to be less than a preset distance, for example, a preset distance of 0.2 meters. Since this scenario uses a reverse backtracking mowing method, the mowing robot starts moving backward from position A6, passes position A7, and continues backtracking to a point behind B1. Therefore, the smaller the distance between position A7 and position B1, the closer the starting point of the reverse backtracking mowing is to the missed mowing point, and the better the mowing effect.

[0106] In another possible embodiment, for the aforementioned scenario of generating a straight, smooth loop trajectory, the target mowing point is A9, and the relative distance between it and the offset starting point B1 is set to be less than a preset distance, for example, a preset distance of 0.1 meters. In scenarios with slight deviations, since the deviation is small, the preset distance can be set even smaller to minimize unnecessary backtracking distances while ensuring the integrity of the re-mowing.

[0107] As can be seen, this embodiment limits the relative distance between the target mowing point and the offset starting point to within a preset distance. This ensures that after the mowing robot reaches the target point along the return trajectory and resumes its edge-cutting operation, the mowing blade can completely cover the missed areas caused by the trajectory offset event. This avoids the problem of local missed mowing due to excessive distance between the two points. At the same time, it can control the overall length of the return trajectory, improve the trajectory return efficiency, reduce unnecessary driving energy consumption, and adapt to the refined edge-cutting needs of different scenarios such as family courtyards and public green spaces, while ensuring no missed mowing and re-mowing.

[0108] Optionally, the preset relative positional relationship is as follows: the trajectory line between the target mowing point and the offset starting point is parallel to the reference trajectory line of the missed mowing area of ​​the trajectory offset event.

[0109] The reference trajectory line refers to the reference trajectory line of the missed mowing area during the trajectory deviation event, that is, the trajectory segment from the starting point of the deviation to the mapping point of the deviation endpoint on the preset working trajectory of the mowing robot. The preset working trajectory refers to one or more continuous, closed-loop mowing routes pre-planned by the mowing robot before starting edge mowing operations, based on the work map and mowing task configuration. These routes closely follow the outer boundary of the work area or the boundary of internal obstacles, and represent the expected path for the robot during normal operation. The mapping point of the deviation endpoint refers to the corresponding point obtained by vertically projecting the deviation endpoint onto the preset working trajectory. Since the deviation endpoint is usually not on the preset trajectory, a unique mapping point can be determined on the preset trajectory by drawing a perpendicular line or finding the shortest distance point. The preset trajectory segment between this mapping point and the starting point of the deviation corresponds to the boundary area that should have been mowed during the period when the robot deviated but was actually missing, serving as the reference trajectory line for the missed mowing area.

[0110] In one possible embodiment, in the reverse mowing operation scenario, the trajectory line between the target mowing operation point and the offset starting point is kept parallel to the reference trajectory line, which enables the reverse edge-following operation path of the mowing robot to match the original preset operation trajectory, thereby effectively eliminating missed mowing areas and ensuring uniform and regular edge mowing results.

[0111] As can be seen, this implementation method limits the positional relationship to ensure that the path of the mowing robot moving along the target loop trajectory to the target mowing point is parallel to the core trajectory segment of the missed area. This ensures that when the robot performs reverse edge mowing, the mowing path can accurately fit the boundary direction of the missed area, thereby effectively covering the unworked boundary area in the trajectory deviation event. At the same time, it ensures the continuity and consistency of the edge mowing operation and is suitable for high-precision edge mowing operation scenarios such as irregular boundaries and multi-obstacle boundaries.

[0112] Optionally, after controlling the mowing robot to move along the target loop trajectory to the target mowing point, the method further includes: Continue mowing operations from the target mowing point.

[0113] The lawnmower moves along the target loop trajectory to the target mowing point. Once the regression calibration process after the trajectory deviation is completed, the mowing operation is restarted from that point. This effectively covers and re-mows the missed areas caused by the trajectory deviation event, ensuring the continuity of the edge operation process and avoiding the problems of operation interruption and missed areas due to trajectory deviation. There is no need to perform subsequent global secondary sweeping operations, which effectively improves the integrity and efficiency of the mowing operation.

[0114] In one possible embodiment, the mowing operation can be either the mowing robot continuing to mow according to a preset work trajectory, or it can be creating a work trajectory in real time based on sensing information and mowing according to the newly created work trajectory. This is not a single, limited method; both techniques can achieve mowing coverage of missed areas. Furthermore, after the mowing robot reaches the target mowing point, its orientation can either gradually change and adjust to the target direction while moving along the target loop trajectory to the target mowing point, or it can maintain the same orientation as the offset endpoint while moving along the target loop trajectory to the target mowing point and then turn in place to adjust to the target direction upon reaching the target mowing point.

[0115] The target direction can be either the forward mowing direction or the reverse mowing direction. The forward mowing direction is the same as the direction from the mowed area to the unmowed area, and the reverse mowing direction is the same as the direction from the unmowed area to the mowed area.

[0116] As can be seen, this embodiment explicitly requires the lawnmower robot to start mowing from the target mowing point to ensure that missed areas are actually covered. Simultaneously, by providing two mowing operation modes—one based on a preset trajectory and the other based on a real-time perceived trajectory—it effectively balances operational efficiency and adaptability. Furthermore, by providing two device orientation adjustment methods, it allows for a balance between lawn protection and planning complexity under different working conditions. By clearly defining forward and reverse mowing directions, the re-mowing strategy provided by this embodiment can flexibly adapt to different types of loop trajectories.

[0117] Please see Figure 10 This application also provides a lawn mowing robot, including a memory 102 and a processor 101. The memory 102 stores a computer program, which executes the lawn mowing control method of the lawn mowing robot provided in any embodiment of this application when the processor 101 is running.

[0118] The processor 101 is the computing and control unit of the lawnmower robot. It can be an embedded microprocessor, a microcontroller unit (MCU), an advanced RISC machine chip (ARM chip), or other processing chips with real-time computing capabilities. It is used to perform processing operations such as trajectory deviation event detection, operation map parsing, candidate loop trajectory generation, preset index calculation, comprehensive scoring calculation, and motion control command output. The memory 102 is a non-volatile storage unit used to store computer programs, operation map data, trajectory deviation parameters, preset thresholds, weight coefficients, and historical operation data, ensuring stable storage and fast retrieval of control programs and operation data.

[0119] In one possible embodiment, the lawnmower robot can detect and determine trajectory deviation events through modules such as wheel odometer, inertial measurement unit (IMU), vision sensor, or boundary detection sensor, and acquire the operation map, the location data of the starting point and the ending point of the deviation based on positioning methods such as Simultaneous Localization and Mapping (SLAM) or Light Detection and Ranging (LiDAR).

[0120] In this embodiment, when the computer program is loaded and run by the processor 101, it can realize the process control logic of trajectory offset response, candidate loop trajectory determination, target loop trajectory screening, robot return control, and missed area re-cutting in this application. This enables the lawn mowing robot to automatically complete backtracking and trajectory return after trajectory offset during edge operation, effectively solving the technical problems of serious missed mowing, lawn wear, low operation efficiency, and poor safety. It is suitable for intelligent edge mowing operation needs in various scenarios such as family courtyards, public green spaces, and landscape lawns.

[0121] Please see Figure 11 This application provides a control device 200 for a lawnmower robot, the control device 200 for the lawnmower robot comprising: The information acquisition module 201 is used to acquire the operation map of the lawn mower robot and the starting point and ending point of the lawn mower robot in the trajectory deviation event in response to the trajectory deviation event. The trajectory determination module 202 is used to determine the target loop trajectory based on the work map, the offset start point, and the offset end point; The mobile control module 203 is used to control the lawn mowing robot to move along the target loop trajectory to the target lawn mowing point, and the target lawn mowing point and the offset starting point satisfy a preset relative position relationship.

[0122] The control device 200 for the lawn mowing robot provided in this embodiment can implement the lawn mowing control method provided in any embodiment of this application. To avoid repetition, it will not be described again here.

[0123] The control device 200 for the lawnmower robot provided in this application responds to the trajectory deviation event of the lawnmower robot, acquires the work map, the deviation start point and the deviation end point, determines the target loop trajectory, and controls the robot to move along the target loop trajectory to the target mowing work point that satisfies the preset relative positional relationship with the deviation start point. This allows the lawnmower robot to completely cover the missed mowing trajectory between the deviation start point and the deviation end point when it starts mowing after reaching the target mowing work point according to the target loop trajectory. That is, it fully covers the missed mowing area in this trajectory deviation event. In this way, the lawnmower robot achieves high coverage and no dead-angle replenishment function for the missed mowing area in the trajectory deviation event, without the need to perform additional global secondary sweeping operation, effectively improving the work integrity and work efficiency of the lawnmower robot, while ensuring the continuity and regularity of the edge-to-edge operation.

[0124] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, executes the lawn mowing control method described in any of the foregoing embodiments.

[0125] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0126] The computer-readable storage medium provided in this embodiment can implement the lawnmower control method provided in any embodiment of this application. To avoid repetition, it will not be described again here.

[0127] In this application, the terms "embodiment" and "implementation" mean that a specific feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of this application. The appearance of these phrases in various locations throughout the specification does not necessarily refer to the same embodiment, nor are they independent or alternative embodiments mutually exclusive with other embodiments. Those skilled in the art will understand, explicitly and implicitly, that the embodiments described in this application can be combined with other embodiments. Furthermore, it should be understood that the features, structures, or characteristics described in the various embodiments of this application can be arbitrarily combined to form another embodiment that does not depart from the spirit and scope of the technical solution of this application, provided there is no contradiction between them.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of this application should not depart from the spirit and scope of the technical solutions of this application.

Claims

1. A method for controlling lawn mowing, characterized in that, include: In response to a trajectory deviation event of the lawnmower robot, the operation map of the lawnmower robot, as well as the starting point and ending point of the deviation of the lawnmower robot in the trajectory deviation event are obtained; The target loop trajectory is determined based on the work map, the offset start point, and the offset end point; The lawnmower robot is controlled to move along the target loop trajectory to the target mowing point, and the target mowing point and the offset starting point satisfy a preset relative positional relationship.

2. The method according to claim 1, characterized in that, Determining the target loop trajectory based on the work map, the offset start point, and the offset end point includes: Candidate loop trajectories are determined from the work map based on the offset start point and the offset end point; Select the target loop trajectory from the candidate loop trajectories.

3. The method according to claim 2, characterized in that, Selecting the target loop trajectory from the candidate loop trajectories includes: The preset indicators for determining the candidate loop trajectory include at least one of the following: loop trajectory in-situ spin angle, loop trajectory length or loop trajectory time, and loop trajectory missed cut rate. The comprehensive score of the candidate loop trajectory is determined based on the preset indicators; The target loop trajectory is selected from the candidate loop trajectories based on the comprehensive score.

4. The method according to claim 3, characterized in that, The step of determining the comprehensive score of the candidate loop trajectory based on the preset index includes: The original indicator data of the preset indicators are normalized for the influence of indicator dimensions and orders of magnitude to obtain the target indicator data. The comprehensive score of the candidate loop trajectory is calculated based on the weight coefficients of the preset indicators and the target indicator data.

5. The method according to claim 2, characterized in that, The step of determining candidate loop trajectories from the work map based on the offset start point and the offset end point includes: The offset endpoint is designated as the starting point of the candidate loop trajectory; The offset starting point is designated as the target mowing point; Using the starting point of the trajectory as the search starting point and the target mowing point as the search ending point, the candidate loop trajectory is determined in the operation map according to a multi-directional search strategy.

6. The method according to claim 5, characterized in that, The multi-directional search strategy includes at least one of the following search directions: The search direction from the search endpoint directly to the search starting point; The search direction is to first search in the direction of the mowed lawn from the search endpoint, and then search in the direction of the search starting point; The search direction is to first search towards the unmowed grass from the search endpoint, and then search towards the search starting point.

7. The method according to any one of claims 1-6, characterized in that, The target mowing point is located on the operation map and is behind the offset starting point, where "behind" refers to the direction from the offset starting point towards the unmowed area.

8. The method according to any one of claims 1-6, characterized in that, The preset relative positional relationship is as follows: The relative distance between the target mowing point and the offset starting point is less than a preset distance.

9. The method according to any one of claims 1-6, characterized in that, The preset relative positional relationship is as follows: The trajectory line between the target mowing point and the offset starting point is parallel to the reference trajectory line of the missed mowing area of ​​the trajectory offset event.

10. The method according to any one of claims 1-6, characterized in that, After controlling the lawnmower robot to move along the target loop trajectory to the target lawnmower work point, the method further includes: Continue mowing operations from the target mowing point.

11. A lawnmower robot, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that executes the lawn mowing control method of any one of claims 1 to 10 when the processor is running.

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