Regression path planning method and system for self-moving robot and self-moving robot
By setting multiple regression starting points and selecting different starting points in the regression path planning of the self-moving robot, the problem of work area damage caused by path repetition is solved, and more efficient regression path planning is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU LAIYUFEI TECHNOLOGY CO LTD
- Filing Date
- 2022-10-14
- Publication Date
- 2026-07-21
AI Technical Summary
Existing self-moving robots are prone to causing surface damage to the working area during regression path planning, especially due to excessive repeated rolling along the path.
Multiple return starting points at different locations are set within the preset return area of the base station. The current return starting point is selected according to preset rules, and a path is planned to the base station to avoid duplicate paths.
It effectively reduces the repetition rate of regression paths, minimizes damage to the working area surface, and improves regression efficiency.
Smart Images
Figure CN115903780B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of self-moving robot technology, specifically relating to a regression path planning method, system, and self-moving robot for a self-moving robot. Background Technology
[0002] With the development of science and technology, intelligent self-propelled robots have become well-known. These robots automatically perform tasks according to pre-set programs without human intervention, making them widely used in industrial applications and home products. For example, automatic lawnmowers, which are battery-powered, cannot operate once the battery is depleted. Therefore, they are typically programmed to return to a base station to recharge the battery when its power level falls below a certain threshold.
[0003] In existing technologies, self-moving robots typically return to base stations by following boundaries. This results in excessive trampling of the lawn at the boundaries and excessively deep tracks on the return path, causing damage to the lawn. Another type of self-moving robot uses point-to-point path planning between itself and the base station to move to the base station via the shortest path. However, this method has the problem that when entering the vicinity of the base station, it tends to repeatedly traverse the same path, thereby damaging the surface (lawn) of the work area.
[0004] Therefore, it is necessary to improve the existing technology to overcome the aforementioned defects. Summary of the Invention
[0005] Therefore, the technical problem that this invention aims to solve is that existing self-moving robots often repeat the path regression process, resulting in excessive rolling and surface damage to the working area along the regression path.
[0006] To address the aforementioned technical problems, this invention provides a regression path planning method for a self-moving robot, comprising:
[0007] In response to the return command, control the self-mobile robot to initiate base station return;
[0008] According to preset rules, a regression starting point is determined from multiple regression starting points at different locations as the current regression starting point; wherein, the multiple regression starting points at different locations are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path;
[0009] Control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station.
[0010] In one embodiment, controlling the self-moving robot to move to the current return starting point includes:
[0011] Control the self-moving robot to move toward the base station, and identify whether the self-moving robot has entered the preset return area;
[0012] When the self-moving robot enters the preset return area, control the self-moving robot to move to the current return starting point.
[0013] In one embodiment, controlling the self-moving robot to move to the current return starting point includes:
[0014] Plan the first regression path from the current position to the current regression starting point;
[0015] Control the self-moving robot to move to the current regression starting point along the first regression path.
[0016] In one embodiment, determining a regression starting point from multiple different regression starting points as the current regression starting point according to a preset rule includes:
[0017] The current regression starting point is determined by selecting one of the multiple regression starting points at different positions according to the cyclical order; or...
[0018] The current regression starting point is determined by randomly selecting one of the multiple regression starting points at different locations.
[0019] In one embodiment, controlling the self-moving robot to move from the current return starting point to the base station includes:
[0020] Plan the second regression path from the current regression starting point to the base station;
[0021] The self-moving robot is controlled to move to the base station according to the second return path.
[0022] In one embodiment, the multiple regression starting points at different locations are dispersed at intervals within the preset regression area with the base station as the center.
[0023] Furthermore, the present invention also provides a regression path planning system for a self-moving robot. This regression path planning system includes:
[0024] The regression module is used to respond to regression commands and control the self-mobile robot to initiate base station regression.
[0025] The regression starting point determination module is used to determine a regression starting point as the current regression starting point from multiple regression starting points at different locations according to preset rules; wherein, the multiple regression starting points at different locations are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path;
[0026] The control module is used to control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station.
[0027] In one embodiment, the regression path planning system further includes:
[0028] The regression region identification module is used to determine the preset regression region according to the map information of the working area and a preset algorithm.
[0029] In one embodiment, the preset regression region is set by the user, and the regression path planning system further includes:
[0030] The analysis module is used to analyze whether the preset regression region meets preset conditions;
[0031] If the preset regression area does not meet the preset conditions, a reminder signal indicating that the preset regression area setting is inappropriate will be issued.
[0032] In one embodiment, the regression path planning system further includes:
[0033] The identification module is used to identify whether the self-moving robot has entered the preset return area.
[0034] Furthermore, the present invention also provides a self-moving robot. The self-moving robot includes:
[0035] Robot body;
[0036] The controller is mounted on the main body of the robot.
[0037] The controller is configured to perform the following operations:
[0038] In response to the return command, control the self-mobile robot to initiate base station return;
[0039] According to preset rules, a regression starting point is determined from multiple regression starting points at different locations as the current regression starting point; wherein, the multiple regression starting points at different locations are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path;
[0040] Control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station.
[0041] The technical solution provided by this invention has the following advantages:
[0042] The self-moving robot regression path planning method, system, and self-moving robot provided in this invention set multiple regression starting points at different locations within a preset regression area of a base station. When the self-moving robot starts the base station regression task, it determines one of the regression starting points as the current regression starting point according to preset rules, and controls the self-moving robot to return to the base station through the determined current regression starting point, thereby reducing the regression path repetition rate and effectively reducing the risk of damage to the working area surface on the regression path caused by excessive path regression of the self-moving robot. Attached Figure Description
[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0044] Figure 1 A flowchart of a regression path planning method for a self-moving robot provided in an embodiment of the present invention;
[0045] Figure 2 A schematic diagram of a regression scenario for the regression path planning method for a self-moving robot provided in an embodiment of the present invention;
[0046] Figure 3 This is a schematic diagram of the module structure of the return path planning system for a self-moving robot provided in an embodiment of the present invention. Detailed Implementation
[0047] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0049] In this invention, unless otherwise stated, the examples described below are merely specific examples and are not intended to limit the embodiments of the invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of this invention to construct more embodiments not mentioned herein by reading this specification.
[0050] This embodiment provides a regression path planning method for a self-moving robot. In specific implementation, this method is applied to a self-moving robot that autonomously performs tasks, specifically an automatic lawnmower. It should be noted that the self-moving robot listed above is merely illustrative. In specific implementations, depending on the specific application scenario and processing requirements, the self-moving robot may also include inspection robots, nanny robots, etc. This specification does not limit this. The tasks that the self-moving robot must perform in the work area may also differ depending on the specific self-moving robot. For example, if the self-moving robot is an automatic lawnmower, the corresponding work area is a lawn, and the task to be performed in the work area may be mowing. As another example, if the self-moving robot is an inspection robot, the task to be performed in the target area may be inspection. In this specific embodiment, an automatic lawnmower working scenario is used as a specific example.
[0051] One existing type of automatic lawnmower uses a boundary-following approach when initiating a base station return task. After a certain number of returns, the lawnmower compacts the lawn at the boundary more, causing deeper wheel tracks and damaging the grass. Another type of automatic lawnmower uses point-to-point path planning between the lawnmower and the base station to move to the base station via the shortest path. However, this return path method is prone to the lawnmower repeatedly traversing the same path when entering the area near the base station, resulting in repeated compaction of the grass along that path and causing further damage.
[0052] To address the aforementioned problems, this embodiment provides a regression path planning method for a self-moving robot. For example... Figure 1 As shown, the specific implementation of this method may include the following steps:
[0053] S01. In response to the return command, control the self-mobile robot to start the base station return.
[0054] Specifically, a regression command is initiated when the self-moving robot completes its task; or, when the self-moving robot's battery level is less than or equal to a preset battery threshold. These regression commands can also be triggered by the user, and this is not restricted.
[0055] In one implementation scenario, the aforementioned base station can be a charging station used to replenish the power of the autonomous mobile robot. Of course, the base station can also provide other service functions, such as cleaning services provided by the autonomous mobile robot.
[0056] S02. Determine a regression starting point from multiple different regression starting points as the current regression starting point according to preset rules; wherein, the multiple different regression starting points are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path.
[0057] The preset regression area is pre-set and can be set according to the specific circumstances of the working area. For example, it can be set according to the size of the working area and the location of the base station. In one embodiment, the above method further includes: obtaining the size information of the working area; and determining the area and shape of the preset regression area based on the size information.
[0058] In one embodiment, the aforementioned preset return area is set by the user. Specifically, since the mobile robot opens the setting port, the user can independently set the preset return area according to the actual working area. This allows the user to select the preset return area based on the surface conditions of the specific working area, improving the flexibility of the preset return area setting.
[0059] In another embodiment, the aforementioned preset regression area can be identified and obtained by a self-moving robot. Specifically, the self-moving robot acquires a map of the work area, determines the geographical information of the preset regression area based on the map, and updates the map with the geographical information of the preset regression area. For example, the self-moving robot scans the map of the work area, determines the location of the base station, the shape and size of the work area, calculates the area of the work area based on the shape and size, and if the area falls within a specific area range, determines the area and shape of the preset regression area, centered on the base station.
[0060] In one implementation scenario, the base station is located within a preset regression area. Preferably, it is located near the boundary of the working area within the preset regression area; alternatively, the base station can be located at the boundary of the working area. There are multiple regression points, dispersed within the preset regression area and surrounding the base station.
[0061] The aforementioned preset rules are pre-set, specifically the selection rules for multiple regression starting points. Please refer to the corresponding embodiments below. The current regression starting point is determined from multiple regression starting points at different locations according to the preset rules, serving as a necessary point on the current regression path.
[0062] S03. Control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station.
[0063] In this embodiment, the self-moving robot's return path planning method sets multiple return starting points at different locations within the preset return area of the base station. When the self-moving robot starts the base station return task, it controls the self-moving robot to move to the base station by selecting different return starting points according to preset rules, thereby effectively reducing the damage to the working area surface on the return path caused by excessive path repetition of the self-moving robot.
[0064] In one embodiment, step S03, controlling the self-moving robot to move to the current return starting point, includes:
[0065] S031. Control the self-moving robot to move towards the base station, and identify whether the self-moving robot has entered the preset return area.
[0066] In one embodiment, the self-moving robot first plans the optimal return path from its current location to the base station. Controlling the self-moving robot to move towards the base station includes the following steps: planning the optimal return path from the current location to the base station; controlling the self-moving robot to move towards the base station according to the optimal return path.
[0067] Specifically, the planning method employs point-to-point path planning to calculate the optimal path between two points. The self-moving robot is then controlled to move towards the base station along this optimal path, ensuring high regression efficiency. In a specific implementation scenario, if the self-moving robot encounters an obstacle while moving along the optimal path towards the target location, it performs obstacle avoidance maneuvers, such as using a route around the obstacle, and then continues moving towards the base station along the optimal path. In one implementation, the aforementioned optimal regression path can be the shortest regression path from the current location to the base station. In other words, by planning the regression path from the current location to the base station according to the optimal path rules and controlling the self-moving robot to move towards the base station, regression time is effectively reduced and overall regression efficiency is improved.
[0068] The identification of whether the self-moving robot has entered the preset return area can be achieved using either map labeling or physical markers. Specifically, the self-moving robot determines its current coordinates based on real-time positioning and checks whether its current position is within the preset return area, thereby identifying whether it has entered the preset return area. In another embodiment, the preset return area is a range equidistant from the base station, and the self-moving robot identifies whether it has entered the preset return area by determining the distance between its current coordinates and the base station.
[0069] S033. When the self-moving robot enters the preset return area, control the self-moving robot to move to the current return starting point.
[0070] like Figure 2As shown, the working area of the self-moving robot is area A, the base station is E, and the preset return area of base station E is B. In this embodiment, the preset return area B is located within the working area A, and the base station E is located within the preset return area B. In a specific implementation scenario, base station E also includes a charging device for charging the self-moving robot; that is, base station E integrates a charging device, and therefore can also be called a charging station.
[0071] like Figure 2 As shown, in this implementation scenario, only three regression starting points at different locations are marked as an example, namely a, b, and c. Of course, in other embodiments, the number of regression starting points at multiple different locations can be other numbers, such as 2, 4, 5, 6, etc. These multiple regression starting points at different locations can be distributed at intervals around the base station within a preset regression area, or the multiple regression starting points at different locations can be distributed at average intervals near the boundary of the preset regression area.
[0072] Preferably, the location and number of regression start points can be set by the user. For example, a user can set the number and specific location of regression start points via a handheld terminal. Preset regression areas can also be set by the user, allowing them to customize these areas according to their preferences, such as setting the coverage area, size, and shape, or setting multiple parameters simultaneously. This provides greater flexibility for users to set preset regression areas based on the terrain near the base station.
[0073] Preferably, the geometric dimensions of the preset regression region B have a certain geometric relationship with the dimensions of the working region A. The selection principle for the geometric dimensions of the preset regression region B is to ensure that the regression paths are not repeated and that the positions of multiple regression starting points are distributed as evenly as possible. Auxiliary factors to consider include the topography of the working region A.
[0074] Upon responding to a return command, the self-propelled robot moves with the base station as its navigation target. During movement, it identifies whether the self-propelled robot has entered the preset return area. If the self-propelled robot has entered the preset return area, it is controlled to move to the current return starting point, which is different from the return starting point of the previous return path. This return path planning method can ensure the efficiency of the return while preventing the return path from simply repeating itself, thus reducing the damage to the working area on the return path caused by excessive path repetition.
[0075] In an optional embodiment, step S03, controlling the self-moving robot to move to the current return starting point, includes:
[0076] S032. Plan the first regression path from the current position to the current regression starting point.
[0077] S034. Control the self-moving robot to move to the current regression starting point according to the first regression path.
[0078] In one embodiment, the first regression path is the optimal path from the current position to the current regression starting point.
[0079] In this embodiment, after responding to a return command, the self-moving robot moves with the current return starting point as the navigation target, planning a first return path from the current position to the current return starting point; the self-moving robot is then controlled to move to the current return starting point according to the first return path, the current return starting point being different from the return starting point of the previous return path. This return path planning method first moves with the current return starting point as the navigation target, and after the self-moving robot moves to the current return starting point, it moves with the base station as the navigation target. This ensures that the return path is not simply repeated, reducing damage to the working area on the return path caused by high repetition of return paths, and can adapt to different working area scenarios.
[0080] In an optional embodiment, step S02, which involves determining a regression starting point from multiple different regression starting points according to a preset rule, includes: determining one of the multiple different regression starting points as the current regression starting point in a periodic order; or, determining one of the multiple different regression starting points as the current regression starting point in a random order.
[0081] Again Figure 2 The illustrated embodiment is a specific implementation. If one of the multiple different regression starting points is determined as the current regression starting point according to the periodic order, then the regression starting point selection order of the self-mobile robot is a—b—c—a—b—c…... where the frequency of the periodic order is equal to the total number of multiple different positions.
[0082] Specifically, one of the multiple regression starting points at different positions is determined in a random order as the current regression starting point. Preferably, the random order maximizes the interval between occurrences of the same regression starting point.
[0083] In an optional embodiment, controlling the self-moving robot to move from the current return starting point to the base station in step S03 includes:
[0084] S035. Plan the second regression path from the current regression starting point to the base station.
[0085] S036. Control the self-moving robot to move to the base station according to the second return path.
[0086] In this embodiment, when the starting point of the return journey is different, the self-moving robot plans the optimal second return path from the current starting point to the base station and moves to the base station according to the second return path, effectively reducing the return time and improving the overall return efficiency. Here, "optimal" is understood as the shortest return path while avoiding known obstacles. Of course, in actual operation, new obstacles may be encountered, such as moving objects or animals. In this case, the self-moving robot performs obstacle avoidance maneuvers, then bypasses the obstacle and continues to travel along the second return path.
[0087] like Figure 3 As shown, this embodiment of the invention also provides a regression path planning system for a self-moving robot. The regression path planning system 100 for a self-moving robot includes a regression module 10, a regression starting point determination module 20, and a control module 30.
[0088] The regression module 10 is used to control the self-mobilizing robot to initiate base station regression in response to a regression command. Specifically, the regression module 10 initiates the regression command when the self-mobilizing robot completes its work task; or, when the self-mobilizing robot's battery level is less than or equal to a preset battery threshold, the regression module 10 initiates the regression command.
[0089] The return starting point determination module 20 is used to determine a return starting point as the current return starting point from multiple return starting points at different locations according to preset rules; wherein, the multiple return starting points at different locations are all located within a preset return area of the base station, and the current return starting point is different from the return starting point of the previous return path. The return starting point determination module 20 may determine one of the multiple return starting points at different locations as the current return starting point in a periodic order; or, it may determine one of the multiple return starting points at different locations as the current return starting point in a random order.
[0090] The control module 30 is used to control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station. In one embodiment, the control module 30 plans a first return path from the current location to the current return starting point, and then controls the self-moving robot to move to the current return starting point according to the first return path.
[0091] In this embodiment, after responding to a return command, the self-moving robot moves with the current return starting point as the navigation target, planning a first return path from the current position to the current return starting point; the self-moving robot is then controlled to move to the current return starting point according to the first return path, the current return starting point being different from the return starting point of the previous return path. This return path planning method first moves with the current return starting point as the navigation target, and after the self-moving robot moves to the current return starting point, it then moves with the base station as the navigation target. This ensures that the return path is not simply repeated, thus reducing the damage to the working area on the return path caused by repeated path regressions, and can adapt to different working area scenarios.
[0092] In an optional embodiment, the self-moving robot's return path planning system 100 further includes an identification module. The identification module is used to identify whether the self-moving robot has entered a preset return area. The return starting point determination module 20 determines a return starting point from multiple different return starting points as the current return starting point according to preset rules; wherein, the multiple different return starting points are all located within the preset return area of the base station, and the current return starting point is different from the return starting point of the previous return path.
[0093] In one embodiment, the regression path planning system further includes a regression region identification module, used to determine a preset regression region according to a preset algorithm based on map information of the work area. Specifically, the self-propelled robot acquires a map of the work area, determines the geographical information of the preset regression region based on the map, and updates the geographical information of the preset regression region to the map. For example, the self-propelled robot scans the map of the work area to determine the location of the base station, the shape and size of the work area, calculates the area of the work area based on the shape and size, and if the area falls within a specific area range, then the area and shape of the preset regression region are determined, and the position of the preset regression region on the map is determined with the base station as the center and marked on the map; if the area falls within another specific area range, then the area and shape of the preset regression region are determined based on the corresponding area range, and the position of the preset regression region on the map is determined with the base station as the center and marked on the map.
[0094] In one embodiment, the preset regression region is set by the user. The regression path planning system also includes an analysis module for analyzing whether the preset regression region meets preset conditions. If the preset regression region does not meet the preset conditions, a warning signal indicating that the preset regression region setting is inappropriate is issued. The analysis module can also issue adjustment suggestions for the preset regression region so that the user can choose to set it. The aforementioned preset conditions can correspond to the preset algorithm of the aforementioned regression region identification module.
[0095] Continue to refer to Figure 2In this embodiment, the working area of the self-moving robot is area A, the base station is E, and the preset return area of base station E is B. In this embodiment, the preset return area B is located within the working area A, and base station E is located within the preset return area B. In this embodiment, only three different return starting points are marked as an example, namely a, b, and c. Of course, in other embodiments, the number of multiple different return starting points can be other numbers, such as 2, 4, 5, 6, etc. These multiple different return starting points can be distributed at intervals around the base station within the return area, or the multiple different return starting points can be distributed at average intervals near the boundary of the preset return area.
[0096] Preferably, the geometric dimensions of the preset regression region B have a certain geometric relationship with the dimensions of the working region A. The selection principle for the geometric dimensions of the preset regression region B is to ensure that the regression paths are not repeated and that the positions of multiple regression starting points are distributed as evenly as possible. Auxiliary factors to consider include the topography of the working region A.
[0097] Specifically, the control module 30 controls the self-moving robot to move towards the base station E, and identifies whether the self-moving robot has entered the preset return area B. Identifying whether the self-moving robot has entered the preset return area can be done using a map marker method or a physical marker method. If the self-moving robot has entered the preset return area B, it is controlled to move to the current return starting point determined by the return starting point determination module 20.
[0098] In this embodiment, after responding to a return command, the self-moving robot moves with the base station as its navigation target. During the movement, it is determined whether the self-moving robot has entered the preset return area. If the self-moving robot has entered the preset return area, it is controlled to move to the current return starting point, which is different from the return starting point of the previous return path. This return path planning method can ensure the efficiency of the return and also ensure that the return path is not simply repeated, thus reducing the damage to the working area on the return path caused by excessive path repetition.
[0099] This invention also provides a self-moving robot. The self-moving robot includes a robot body and a controller.
[0100] The controller is mounted on the robot body; wherein the controller is configured to perform the following operations:
[0101] In response to a return command, the self-mobilizing robot is controlled to initiate base station return. Specifically, the return command is initiated when the self-mobilizing robot completes its task; or, when the self-mobilizing robot's battery level is less than or equal to a preset battery threshold.
[0102] According to preset rules, a regression starting point is determined from multiple different regression starting points as the current regression starting point; wherein, the multiple different regression starting points are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path.
[0103] Control the self-moving robot to move to the current return starting point, and then control the self-moving robot to move from the current return starting point to the base station.
[0104] In one embodiment, the starting point of the self-moving robot is different each time it returns, thereby ensuring that the return path is not singular and effectively solving the technical problem in the prior art where the self-moving robot has many repeated return paths, which causes damage to the surface of the working area on the return path.
[0105] In one optional embodiment, the controller determines the current regression starting point in a cyclical order: one of the plurality of regression starting points at different positions is selected as the current regression starting point in a cyclical order. In another embodiment, the controller determines the current regression starting point in a random order: one of the plurality of regression starting points at different positions is selected as the current regression starting point in a random order.
[0106] Again Figure 2 The illustrated embodiment is a specific implementation. If one of the multiple different regression starting points is determined as the current regression starting point according to the periodic sequence, then the regression starting point selection order of the self-moving robot is a—b—c—a—b—c…... The frequency of the periodic sequence is equal to the total number of different positions.
[0107] Specifically, one of the multiple regression starting points at different positions is determined in a random order as the current regression starting point. Preferably, the random order maximizes the interval between occurrences of the same regression starting point.
[0108] In one embodiment, the controller can control the self-moving robot to move to the current return starting point in the following manner: controlling the self-moving robot to move towards the base station, identifying whether the self-moving robot has entered the preset return area; if the self-moving robot has entered the preset return area, controlling the self-moving robot to move to the current return starting point. The identification of whether the self-moving robot has entered the preset return area can be achieved using map marking or physical markers.
[0109] In one embodiment, the self-moving robot first plans the optimal regression path from its current location to the base station. Specifically, point-to-point path planning is used to calculate the optimal path between the two points, and the self-moving robot is then controlled to move towards the base station, effectively reducing regression time and improving overall regression efficiency.
[0110] In another optional embodiment, when the controller is implemented, it can control the self-moving robot to move to the current return starting point in the following way: plan a first return path from the current position to the current return starting point; control the self-moving robot to move to the current return starting point according to the first return path.
[0111] In an optional embodiment, the controller can control the self-mobilizing robot to move to the base station in the following manner: planning a second regression path with the optimal length from the current regression starting point to the base station; and controlling the self-mobilizing robot to move to the base station according to the second regression path.
[0112] In this embodiment, when the regression starting points are different, the self-moving robot plans the optimal second regression path from the current regression starting point to the base station and moves to the base station according to the second regression path, which effectively reduces the regression time and improves the overall regression efficiency.
[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.
[0114] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.
[0115] Obviously, the embodiments described above are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, those skilled in the art can make other variations or modifications without creative effort, and all such variations or modifications should fall within the scope of protection of the present invention.
Claims
1. A regression path planning method for a self-moving robot, characterized in that, The method includes: In response to the return command, control the self-mobile robot to initiate base station return; According to preset rules, a regression starting point is determined from multiple regression starting points at different locations as the current regression starting point; wherein, multiple regression starting points at different locations are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path. The multiple regression starting points at different locations surround the base station and are dispersed at intervals within the preset regression area with the base station as the center. Control the self-moving robot to move toward the base station, and identify whether the self-moving robot has entered the preset return area; When the self-moving robot enters the preset return area, the self-moving robot is controlled to move to the current return starting point, and then the self-moving robot is controlled to move from the current return starting point to the base station.
2. The regression path planning method for a self-moving robot according to claim 1, characterized in that, Controlling the self-moving robot to move to the current return starting point includes: Plan the first regression path from the current position to the current regression starting point; Control the self-moving robot to move to the current regression starting point along the first regression path.
3. The regression path planning method for a self-moving robot according to claim 1, characterized in that, The step of determining a regression starting point from multiple different regression starting points as the current regression starting point according to preset rules includes: The current regression starting point is determined by selecting one of the multiple regression starting points at different positions according to the cyclical order; or... The current regression starting point is determined by randomly selecting one of the multiple regression starting points at different locations.
4. The regression path planning method for a self-moving robot according to any one of claims 1 to 3, characterized in that, The control of the self-moving robot to move from the current return starting point to the base station includes: Plan the second regression path from the current regression starting point to the base station; The self-moving robot is controlled to move to the base station according to the second return path.
5. A return path planning system for a self-moving robot, characterized in that, include: The regression module is used to respond to regression commands and control the self-mobile robot to initiate base station regression. The regression starting point determination module is used to determine a regression starting point as the current regression starting point from multiple regression starting points at different locations according to preset rules; wherein, the multiple regression starting points at different locations are all located within the preset regression area of the base station, the current regression starting point is different from the regression starting point of the previous regression path, and the multiple regression starting points at different locations surround the base station and are dispersed at intervals within the preset regression area with the base station as the center; The control module is used to control the self-moving robot to move towards the base station and to identify whether the self-moving robot has entered the preset return area; When the self-moving robot enters the preset return area, the self-moving robot is controlled to move to the current return starting point, and then the self-moving robot is controlled to move from the current return starting point to the base station.
6. The return path planning system for a self-moving robot according to claim 5, characterized in that, The regression path planning system also includes: The regression region identification module is used to determine the preset regression region according to the map information of the working area and a preset algorithm.
7. The return path planning system for a self-moving robot according to claim 5, characterized in that, The preset regression region is set by the user, and the regression path planning system also includes: The analysis module is used to analyze whether the preset regression region meets preset conditions; If the preset regression area does not meet the preset conditions, a reminder signal indicating that the preset regression area setting is inappropriate will be issued.
8. A self-moving robot, characterized in that, include; Robot body; The controller is mounted on the main body of the robot. The controller is configured to perform the following operation: xit In response to the return command, control the self-mobile robot to initiate base station return; According to preset rules, a regression starting point is determined from multiple regression starting points at different locations as the current regression starting point; wherein, multiple regression starting points at different locations are all located within the preset regression area of the base station, and the current regression starting point is different from the regression starting point of the previous regression path. The multiple regression starting points at different locations surround the base station and are dispersed at intervals within the preset regression area with the base station as the center. Control the self-moving robot to move toward the base station, and identify whether the self-moving robot has entered the preset return area; When the self-moving robot enters the preset return area, the self-moving robot is controlled to move to the current return starting point, and then the self-moving robot is controlled to move from the current return starting point to the base station.