A motion route adaptive planning system for a wheeled robot

Abstract

The application discloses a motion route adaptability planning system of a wheel series robot, and relates to the technical field of route planning of the wheel series robot; in the application, a route planning module obtains working record data from a database, the type, position and occurrence time of obstacles in a target region are counted based on a plurality of working record data, an obstacle hotspot map is drawn, an initial route is planned in combination with task parameters, and finally, the initial route is adaptively and backup-optimized through the obstacle hotspot map to obtain a motion route; according to the technical scheme, the obstacle hotspot map is drawn through the working record data, high-risk obstacle regions can be identified in advance, the calculation frequency of real-time algorithms is reduced, and motion fluctuation caused by frequent path updating is avoided; and the backup route planned in advance can enable the robot to directly switch the route without real-time calculation when encountering obstacles, shorten the obstacle-avoiding response time, and solve the path lag problem.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wheel train robot route planning, and specifically relates to a motion route adaptability planning system of a wheel train robot. BACKGROUND

[0002] Wheel train robots have a wide range of applications in multiple fields due to their simple structure, high motion efficiency, and relatively easy control. For example, they are used for cargo handling and sorting in industrial production and logistics, and for room cleaning in home services.

[0003] In a dynamic environment, the core of wheel train robot path planning lies in the contradiction between the uncertainty of environmental information and the real-time requirement. When the route planning algorithm cannot match the speed of dynamic changes in the environment, not only will the path lag, but also serious problems such as collision may be caused; for example, when a cleaning robot encounters a child during cleaning service, the route planning algorithm needs to generate an obstacle avoidance path within 0.5 seconds, but the child may enter the middle of the obstacle avoidance path within the 0.5 seconds. In addition, in order to respond to environmental changes, path planning needs to be iterated frequently, causing the motion state of the robot to fluctuate frequently, which will lead to unstable path updates and the inability to find a globally optimal path; for example, a wheel train robot in a factory encounters frequently moving shelves, and re-plans the path every 0.1 seconds, causing the wheel train robot to repeatedly turn and start and stop.

[0004] To solve the above technical problems, the application provides a motion route adaptability planning system of a wheel train robot. SUMMARY

[0005] The application aims to at least solve one of the technical problems existing in the prior art; for this purpose, the application provides a motion route adaptability planning system of a wheel train robot, which draws an obstacle hotspot map through work record data, can identify high-risk obstacle areas in advance, reduces the calculation frequency of real-time algorithms, and avoids motion fluctuations caused by frequent path updates; the pre-planned backup route allows the robot to switch routes directly without real-time calculation when encountering obstacles, shortens the obstacle avoidance response time, and solves the path lag problem.

[0006] To achieve the above purpose, the first aspect of the application provides a motion route adaptability planning system of a wheel train robot, which comprises a route planning module and a database connected thereto.

[0007] The route planning module is used to obtain work record data from the database and draw an obstacle hotspot map of a target area based on the work record data; wherein the target area is the working area of the wheel train robot; and

[0008] The wheel series robot is planned a basic route in the target area based on task parameters; and the basic route is adaptively and redundantly optimized by the obstacle hotspot map to obtain a motion route of the wheel series robot.

[0009] In a possible implementation, the obstacle hotspot map of the target area is drawn based on the work record data, including:

[0010] The work record data is extracted, and obstacle records in the target area are counted according to the work record data; wherein the obstacle records include an obstacle type, an obstacle position and an obstacle occurrence time;

[0011] The obstacle hotspot map of the target area is drawn according to the obstacle records.

[0012] In a possible implementation, the obstacle hotspot map of the target area is drawn according to the obstacle records, including:

[0013] A frame map of the target area is drawn, and a total number of times of occurrence of obstacles at each pixel point in the frame map is counted according to the obstacle records;

[0014] Each pixel point is valued by comparing the total number of times with a preset number threshold to obtain a basic hotspot map; and the basic hotspot map is adjusted in combination with robot parameters of the wheel series robot to obtain the obstacle hotspot map.

[0015] In a possible implementation, each pixel point is valued by comparing the total number of times with the preset number threshold, including:

[0016] The number of the work record data is counted, and a number threshold is obtained by multiplying the number by an adjustment coefficient; wherein the adjustment coefficient is less than 1;

[0017] When the total number of times is greater than the number threshold, the corresponding pixel point is valued as 1; otherwise, the corresponding pixel point is valued as 0.

[0018] In a possible implementation, the basic hotspot map is adjusted in combination with the robot parameters of the wheel series robot, including:

[0019] An obstacle region in the basic hotspot map is identified according to the result of the valuation;

[0020] It is judged whether a space between adjacent obstacle regions is allowed to pass through the wheel series robot based on the robot parameters; if not, the adjacent obstacle regions are merged to obtain a new obstacle region.

[0021] In a possible implementation, the basic route is adaptively and redundantly optimized by the obstacle hotspot map, including:

[0022] determining whether the basic route intersects with the obstacle region in the obstacle hotspot map; if yes, marking the intersected obstacle region as a to-be-optimized region and extracting the intersection point;

[0023] planning a motion route of the wheeled robot bypassing the to-be-optimized region based on the intersection point as a backup route one; and splicing and fusing the backup route one and the basic route to obtain the motion route.

[0024] In a possible implementation, after the motion route of the wheeled robot bypassing the to-be-optimized region is planned based on the intersection point, the method further includes:

[0025] verifying whether the distance between adjacent to-be-optimized regions is less than a preset distance threshold; if yes, planning a motion route of the wheeled robot bypassing the adjacent to-be-optimized regions simultaneously as a backup route two;

[0026] splicing and fusing the backup route one, the backup route two and the basic route to obtain the motion route.

[0027] In a possible implementation, verifying whether the distance between adjacent to-be-optimized regions is less than a preset distance threshold includes:

[0028] marking the intersection point of the to-be-optimized region and the basic route as a feature point one and a feature point two; wherein the feature point one is closer to the initial position and the feature point two is closer to the target position;

[0029] calculating the route distance between the feature point two of a previous to-be-optimized region and the feature point one of a next to-be-optimized region; and determining that the distance between adjacent to-be-optimized regions is less than the preset distance threshold when the route distance is less than the preset distance threshold.

[0030] In a possible implementation, the total number of times of occurrence of obstacles of each pixel point in the obstacle record statistical framework map includes:

[0031] setting a statistical interval, determining a plurality of statistical time periods according to the statistical interval; and associating the obstacle records with the plurality of statistical time periods according to the collection time;

[0032] based on the associated obstacle records, counting the total number of times of occurrence of obstacles of each pixel point in the plurality of statistical time periods.

[0033] In a possible implementation, the total number of times of occurrence of obstacles of each pixel point in the plurality of statistical time periods is compared with a preset number threshold to perform value assignment processing on each pixel point to obtain a time period hotspot map.

[0034] integrating all the time period hotspot maps according to the time sequence of the plurality of statistical time periods to obtain a basic hotspot map.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] 1. In the present application, the route planning module obtains work record data from the database, counts the type, location and occurrence time of obstacles in the target area based on a plurality of work record data, draws an obstacle hotspot map, combines task parameters to plan an initial route, and finally optimizes the initial route through the obstacle hotspot map to obtain a motion route; the technical scheme draws an obstacle hotspot map through work record data, can identify high-risk obstacle areas in advance, reduces the calculation frequency of real-time algorithms, and avoids motion fluctuations caused by frequent path updates; the pre-planned backup route allows the robot to switch routes directly without real-time calculation when encountering obstacles, shortens the obstacle avoidance response time, and solves the path lag problem.

[0037] 2. In the present application, when drawing the obstacle hotspot map, the total number of obstacle occurrences of each pixel point of the target area frame map is first counted, a threshold value is assigned based on the amount of work record data to obtain a basic hotspot map, and then adjacent obstacle areas that cannot be passed are combined according to the robot parameters; when optimizing the backup route, the backup route one for a single obstacle area is first planned, the distance between adjacent areas to be optimized is then verified, the backup route two that can bypass multiple close-range obstacle areas at the same time is planned, and finally the initial route and the two types of backup routes are fused; the technical scheme can accurately distinguish between obstacles and passable areas through detailed obstacle hotspot map drawing, avoiding backup route redundancy or loss; the two types of backup routes planned in layers can deal with single obstacles and solve the frequent turning caused by multiple close-range obstacles, reducing motion adjustment, energy consumption and hardware loss, and ensuring efficient and continuous task execution. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0039] Figure 1 The system principle diagram of the motion route adaptive planning system in the first embodiment of the present application Figure 1

[0040] Figure 2 The system principle diagram of the motion route adaptive planning system in the first embodiment of the present application Figure 2

[0041] Figure 3 The schematic diagram of the basic hotspot map in the first embodiment of the present application

[0042] Figure 4 The planning schematic diagram of the basic route in the first embodiment of the present application

[0043] ​​Figure 5 Fig. 1 is a schematic diagram of the intersection of the basic route and the obstacle area in the first embodiment of the present application;

[0044] Figure 6 Fig. 2 is a schematic diagram of the planning of the first standby route in the room cleaning scene in the first embodiment of the present application;

[0045] Figure 7 Fig. 3 is a schematic diagram of the planning of the first standby route in the cargo carrying scene in the first embodiment of the present application;

[0046] Figure 8 Fig. 4 is a schematic diagram of the planning of the second standby route of the adjacent to-be-optimized area in the first embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions of the present application will be described in detail below with reference to the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0048] Embodiment I:

[0049] Please refer to Figures 1-2 The first aspect of the present application provides a motion route adaptive planning system for a wheel train robot, which comprises a route planning module and a database connected thereto.

[0050] The route planning module is used to obtain work record data from the database, draw an obstacle hot spot map of the target area based on the work record data, and plan a basic route of the wheel train robot in the target area based on the task parameters. The basic route is adaptively optimized by the obstacle hot spot map to obtain the motion route of the wheel train robot.

[0051] The motion route adaptive planning system for the wheel train robot provided by the embodiment of the present application mainly comprises a route planning module and a database. The route planning module is used to plan the motion route of the wheel train robot when performing a task, so as to avoid the dynamic change of the working environment causing the wheel train robot to be unable to perform efficient obstacle avoidance. The database stores work record data obtained by the wheel train robot in the past working process, which mainly includes working time, encountered obstacles, etc.

[0052] Figure 1As one of the system schematic diagrams of the adaptive motion route planning system, the route planning module and the database can be independent of the wheeled robot, and are applied to the application scenario in which multiple wheeled robots work simultaneously. For example, multiple wheeled robots are simultaneously used for cargo handling in a factory. The route planning module and the database do not need to be integrated in the wheeled robot. The database stores the working record data of multiple wheeled robots, and the route planning module draws or updates the obstacle hotspot map according to the working record data of multiple wheeled robots. When a wheeled robot needs to perform a task, the motion route of the wheeled robot is planned according to the task parameters and the latest obstacle hotspot map, and the wheeled robot performs the working task according to the motion route.

[0053] It also needs to be understood that the multiple wheeled robots in the application scenario only need to be able to complete the working task according to the motion route, and do not need all the wheeled robots to be able to collect the working record data. If necessary, a special device can be set to collect the obstacle data in the target area for planning the motion route.

[0054] Figure 2 As the second system schematic diagram of the adaptive motion route planning system, the route planning module and the database are integrated in the wheeled robot, and are applied to the scenario in which the wheeled robot works independently. For example, a wheeled robot is used for cleaning service in home cleaning. The route planning module and the database are arranged on the wheeled robot. The wheeled robot collects the working record data through the sensor arranged on the wheeled robot, and sends the working record data to the database for storage. The route planning module draws or updates the obstacle hotspot map according to the working record data stored in the database, and plans the motion route of the wheeled robot for performing the working task according to the obstacle hotspot map.

[0055] Currently, the motion route planning scheme mainly combines the route planning algorithm built in the wheeled robot and the collected environmental data. Since the data processing and the algorithm running need a certain time, the planned motion route may lag, and in a dynamically changing environment, the wheeled robot may run unstably due to frequent updating of the route. In view of this, the embodiment of the present application introduces the obstacle hotspot map, identifies the probability of the wheeled robot encountering an obstacle in advance according to the obstacle hotspot map, and plans a standby route for bypassing the obstacle. During the execution of the task by the wheeled robot, the wheeled robot can immediately switch to the standby route as soon as an obstacle is encountered, without the need for real-time planning by the route planning algorithm, so that the route lag can be avoided.

[0056] The key to implementing the technical solution lies in the drawing and updating of the obstacle hotspot map. It is necessary to use sufficient working record data to mine the movement law of the obstacles in the target area, and to draw the probability of the occurrence of the obstacles at each position in the target area, so as to draw the obstacle hotspot map.

[0057] In a preferred embodiment, the method comprises: extracting work record data, counting obstacle records in the target area according to the work record data, and drawing an obstacle heat map of the target area according to the obstacle records.

[0058] The work record data refers to video data or radar data collected by the wheeled robot in the target area during the scanning or task execution process. The obstacle records in the target area can be extracted from the work record data, and the obstacle records include obstacle types, obstacle positions, and obstacle appearance times. The obstacle types can be used to identify whether the obstacle is a fixed obstacle or a mobile obstacle, so as to optimize the obstacle heat map. The obstacle position can be understood as the area covered by the obstacle, so as to show the details in the obstacle heat map and facilitate subsequent backup route planning. The obstacle appearance time refers to the time when the obstacle is identified in the work record data, and can also be used to optimize the obstacle heat map.

[0059] It should be understood that the obstacle should be an obstacle that cannot be crossed by the wheeled robot, and the wheeled robot needs to re-plan a path to bypass the obstacle.

[0060] When counting the obstacle records in the target area according to the work record data, it is necessary to ensure that the work record data is sufficient and covers a certain time range. If the work record data is insufficient or the coverage time range is short, the movement of most obstacles in the target area cannot be covered, which will result in an incomplete obstacle heat map and insufficient reliability when used for motion route planning or optimization.

[0061] For example, Figure 1 In the application scenario shown, the database can simultaneously receive work record data of multiple wheeled robots, and the number of work record data can meet the requirements in a very short time. Figure 2 In the application scenario shown, only one or a few wheeled robots provide work record data, and the number of work record data needs to be collected for a longer time or more frequent tasks to meet the requirements.

[0062] After the obstacle records are extracted, the framework range of the target area can be drawn first, and then the obstacle positions and obstacle appearance times in the obstacle records can be rendered in the framework range to draw the obstacle heat map. Subsequently collected work record data can be used to update the obstacle heat map to ensure that the obstacle heat map is consistent with the latest actual situation of the target area.

[0063] When the obstacle hotspot map of the target area is obtained, the obstacles in the obstacle hotspot map need to be verified. The verification is used to determine the high-risk area in the target area, which is avoided when planning the motion route. In addition, the adjacent high-risk areas in the target area are merged to improve the efficiency and reliability of the planned backup route.

[0064] In a preferred embodiment, the obstacle hotspot map of the target area is drawn according to the obstacle records, including: drawing a framework map of the target area, counting the total number of obstacles appearing in each pixel point in the framework map according to the obstacle records; assigning each pixel point by comparing the total number with a preset number threshold to obtain a basic hotspot map; and adjusting the basic hotspot map in combination with the robot parameters of the wheeled robot to obtain the obstacle hotspot map.

[0065] In the process of obtaining the obstacle hotspot map, a framework map of the target area is first drawn using a graphics drawing software. The area in the framework map can be understood as the working area of the wheeled robot. The obstacles in the target area are identified from the obstacle records, and the total number of obstacles appearing in each pixel point in the framework map is counted.

[0066] The total number of obstacles appearing in each pixel point in the framework map can be understood as the probability of the obstacle appearing at this position. If the probability is large, a backup route needs to be set for the obstacle at this position when planning the motion route; otherwise, a backup route can not be set, and when the wheeled robot encounters an obstacle at this position, an obstacle avoidance route is planned using a route planning algorithm. In this way, a backup route is planned for a high-risk position, and a real-time planning scheme is still used for a low-risk position, which can well balance the contradiction between frequent route planning and route lag.

[0067] When determining whether a backup route needs to be planned, the total number is compared with a preset number threshold to determine which pixel points often have obstacles. If the obstacles often appear, the pixel point is determined as a high-risk pixel point. The area composed of adjacent high-risk pixel points is an obstacle area. Through the assignment processing, the pixel points in the framework map can be divided into two categories, and a basic hotspot map can be obtained.

[0068] Considering that the space between adjacent obstacle areas in the basic hotspot map can not allow the wheeled robot to pass, there can be obstacles when a backup route is planned for one of the obstacle areas. Therefore, the basic hotspot map needs to be adjusted in combination with the robot parameters of the wheeled robot. After adjustment, an obstacle hotspot map can be obtained. The robot parameters include size, turning radius, minimum turning angle, etc.

[0069] In a preferred embodiment, the assignment processing of each pixel point is performed by comparing the total number with a preset number threshold, including: counting the number of work record data, and obtaining a number threshold by multiplying the number by an adjustment coefficient; when the total number is greater than the number threshold, the corresponding pixel point is assigned a value of 1; otherwise, the corresponding pixel point is assigned a value of 0.

[0070] When assigning values to each pixel point in the framework graph, it can be based on the probability of the presence of obstacles in the corresponding pixel point, so the probability threshold is a very important factor in the probability judgment process. The number threshold in this embodiment is set according to the number of work record data, which can be multiplied by 0.5 (default) or 0.7 (set according to the specific scene, if it is necessary to avoid real-time planning of the route as much as possible, it can be set to 0.1) as the number threshold. The total number of obstacles in each pixel point is identified according to the obstacle record, and if the total number is greater than the number threshold, it means that there is a high probability of obstacles in the pixel point, and the pixel point is assigned a value of 1 in the framework graph, otherwise the pixel point is assigned a value of 0, so that a basic hotspot map with obvious distinction of obstacle risk can be obtained.

[0071] If the pixel point is identified to exist obstacles in each work record data, that is, the total number is equal to the number of work record data, it can be considered that the obstacle is a fixed obstacle, and the total number is greater than the preset number threshold.

[0072] Figure 3 The basic hotspot map is a schematic diagram, Figure 3 Each grid in the basic hotspot map represents a pixel point in the corresponding framework graph of the target area. The black grid represents a fixed obstacle, such as a fixed shelf in a factory scene, a storage cabinet in a home environment, etc. The gray grid represents a non-fixed obstacle, which can also be understood as a mobile obstacle, such as a movable device in a factory scene, a chair in a home environment, etc.

[0073] In a preferred embodiment, the basic hotspot map is adjusted in combination with the robot parameters of the wheeled robot, including: identifying the obstacle regions in the basic hotspot map according to the assignment processing result; judging whether the space between adjacent obstacle regions is allowed to pass through the wheeled robot based on the robot parameters; if not, merging the adjacent obstacle regions to obtain a new obstacle region.

[0074] If the wheeled robot cannot pass through the space between adjacent obstacle regions in the basic hotspot map, it needs to consider bypassing the adjacent obstacle region when planning the motion route, so the adjacent obstacle region can be merged as an obstacle region, which can improve the planning efficiency of subsequent motion route planning.

[0075] As shown in Figure 3 If it is calculated according to the robot parameters of the wheeled robot that the path width of the wheeled robot is at least greater than the width of one grid,Figure 3 If there is only one grid space between the obstacle region 1 and the obstacle region 2, the wheeled robot cannot pass between the obstacle region 1 and the obstacle region 2, and thus the obstacle region 1 and the obstacle region 2 are merged to generate an obstacle region, and the new obstacle region is composed of the obstacle region 1, the obstacle region 2 and the grid space therebetween.

[0076] In planning the basic route of the wheeled robot in the target region according to the current position and the target position of the wheeled robot, the moving obstacles in the target region do not need to be considered in planning the basic route, but the boundary of the target region and the fixed obstacles therein need to be considered.

[0077] In planning the motion route of the wheeled robot, the task parameters of the wheeled robot are first obtained, and the task parameters include the initial position and the target position. Then, the basic route of the wheeled robot in the target region is planned based on the task parameters; and the basic route is adaptively and redundantly optimized by the obstacle hotspot map to obtain the motion route of the wheeled robot.

[0078] As described above, the obstacle regions in the obstacle hotspot map are divided into fixed obstacles and moving obstacles. In terms of probability, the fixed obstacles are certain to exist, and the moving obstacles are extremely likely to exist, and thus the fixed obstacles should be considered in planning the motion route of the wheeled robot, i.e., bypassing the fixed obstacles, and the moving obstacles do not necessarily need to be bypassed.

[0079] Figure 4 A schematic diagram for planning the basic route is shown in FIG. 4, Figure 4 In which position A is the initial position, and position B is the target position. In planning the basic route, only the fixed obstacles are considered, i.e., Figure 4 The black grid, and the moving obstacles, i.e., the gray grid, do not need to be considered, and thus the basic route can be planned as Figure 4 The solid line from position A to position B. If there is no moving obstacle in the current target region, the task is most efficient along the basic route.

[0080] In a preferred embodiment, the basic route is adaptively and redundantly optimized by the obstacle hotspot map, including: judging whether the basic route intersects with the obstacle regions in the obstacle hotspot map; if yes, marking the intersecting obstacle regions as to-be-optimized regions, and extracting the intersection points; planning the motion route of the wheeled robot bypassing the to-be-optimized regions as a backup route one based on the intersection points; and splicing and fusing the backup route one with the basic route to obtain the motion route.

[0081] However, if the base route intersects with the obstacle region, it indicates that the wheeled robot may be blocked by the moving obstacle when performing the task along the base route. If the obstacle-avoiding route is planned in real time when the moving obstacle appears, it may cause the route to lag and the wheeled robot to run unstably.

[0082] To solve this problem, a backup route needs to be planned in advance to avoid route lag and unstable running. Before planning the backup route, the obstacle region that needs to be bypassed is determined, and then the backup route is planned according to the intersection of the base route and the obstacle region.

[0083] Figure 5 For the intersection diagram of the base route and the obstacle region, it can be seen that there are intersection points C1 and C2 between the base route and the obstacle region. That is, the wheeled robot has a high probability of being blocked by the obstacle region when performing the task along the base route, and therefore a backup route needs to be planned.

[0084] Figure 6 For the planning diagram of the backup route in the room cleaning scene, if the wheeled robot performs the cleaning task along the base route, it may be blocked by the moving obstacle. Considering the cleaning task, the backup route planned based on the intersection point C1 and the intersection point C2 is along the boundary of the obstacle region, that is, the intersection point C1 is to the right, and then up to the intersection point C2, as shown by the arrow solid line between the intersection point C1 and the intersection point C2 in Figure 6 .

[0085] Figure 7 For the planning diagram of the backup route in the goods carrying scene, if the wheeled robot performs the goods carrying task along the base route, it may be blocked by the moving obstacle. Considering that the goods carrying task only needs to quickly carry the goods to the destination, the backup route planned based on the intersection point C1 and the intersection point C2 bypasses the obstacle region, as shown by the arrow solid line other than the base route in Figure 7 .

[0086] As shown in the examples of Figure 5 and Figure 6 , the starting point and the ending point of the backup route can be the intersection point, such as in the room cleaning scene, or can not be the intersection point, such as in the goods carrying scene, but the starting point and the ending point need to be on the base route and should be as close to the intersection point as possible.

[0087] In a preferred embodiment, after planning the movement route of the wheel system robot around the to-be-optimized region based on the intersection points, the method further comprises: verifying whether the distance between adjacent to-be-optimized regions is less than a preset distance threshold; if yes, planning a movement route around the adjacent to-be-optimized regions based on the intersection points as a backup route two; and splicing and fusing the backup route one, the backup route two and the basic route to obtain the movement route.

[0088] The basic route can pass through multiple obstacle regions at the same time, and multiple to-be-optimized regions can also exist. Generally, a backup route one needs to be planned for each to-be-optimized region to bypass the to-be-optimized region. However, if two to-be-optimized regions are close to each other, the task can be executed along the backup route one, which can result in lower efficiency. In this case, the adjacent to-be-optimized regions are regarded as one to-be-optimized region, and a backup route two is planned to cope with the changing environment in time and improve the task execution efficiency.

[0089] It is worth noting that the movement route includes the basic route, the backup route one and the backup route two. When executing the task, the basic route is preferred to be executed, and if an obstacle is identified in the basic route, the backup route one or the backup route two is selected to avoid the obstacle. If there is no corresponding backup route one and backup route two, a built-in route planning algorithm is used to plan in real time to avoid the obstacle, and the basic route is still integrated after the obstacle is avoided.

[0090] In a preferred embodiment, verifying whether the distance between adjacent to-be-optimized regions is less than a preset distance threshold comprises:

[0091] The intersection points of the to-be-optimized region and the basic route are marked as feature point one and feature point two; wherein the feature point one is close to the initial position, and the feature point two is close to the target position;

[0092] The route distance between the feature point two of the previous to-be-optimized region and the feature point one of the next to-be-optimized region is calculated; when the route distance is less than a preset distance threshold, it is determined that the distance between the adjacent to-be-optimized regions is less than the preset distance threshold.

[0093] Figure 8 A schematic diagram of the planning of the backup route two for the adjacent to-be-optimized regions. Figure 8The middle basic route intersects with two to-be-optimized regions, and the intersection points are C1 and C2, and C3 and C4, respectively. The distance between the intersection points C2 and C3 on the basic route is taken as the route distance, and if the route distance is less than the preset distance threshold, it is determined that the distance between the adjacent to-be-optimized regions is less than the preset distance threshold. If there are obstacles in the two to-be-optimized regions during the execution of the task, the obstacles should be avoided according to the standby route one of each to-be-optimized region, but after avoiding one to-be-optimized region along the standby route one Y1, a distance is advanced along the basic route, and then another to-be-optimized region is avoided along the standby route one Y2. This process will reduce the task efficiency of the wheel train robot, and will also affect the stability of the wheel train robot. In view of the above situation, the standby route two Y3 is planned, which can directly avoid the two to-be-optimized regions with a small distance, and the starting point of the standby route two Y3 should be the starting point of the standby route one Y1, and the end point of the standby route Y3 should be the end point of the standby route one Y2.

[0094] It should be noted that if the distance between the adjacent to-be-optimized regions is small, the wheel train robot may need to immediately bypass the second to-be-optimized region along another standby route one after bypassing one to-be-optimized region along the standby route one. If the distance between the two to-be-optimized regions is small, the motion efficiency of the wheel train robot will be reduced, and it is better to directly cross the two to-be-optimized regions, that is, to plan the standby route two. The preset distance threshold can be set according to the turning radius of the wheel train robot, for example, the preset distance threshold is set to be more than twice the turning radius.

[0095] It should be further noted that the fusion of the standby route one, the standby route two and the basic route means that the standby route one and the standby route two are spliced on the basic route to form a motion route with the basic route as the main trunk and the standby route one or the standby route two as the branch. For example, Figure 8 The corresponding motion route includes the basic route from the initial position A to the target position B, the standby route one Y1 bypassing the first to-be-optimized region, the standby route one Y2 bypassing the second to-be-optimized region, and the standby route two Y3 bypassing the two to-be-optimized regions.

[0096] After the motion route planning is completed, the wheeled robot starts from the initial position A along the basic route when performing a task, and if no obstacle affecting the passage is identified in the first to-be-optimized region, the wheeled robot still advances along the basic route, and if no obstacle affecting the passage is identified in the second to-be-optimized region, the wheeled robot advances along the basic route to the target position B. If an obstacle affecting the passage is identified in the first to-be-optimized region, the wheeled robot is controlled to advance along the standby route two, then merges into the basic route and reaches the target position B, or the wheeled robot can advance along the standby route Y1, then merges into the basic route. If no obstacle affecting the passage is identified in the first to-be-optimized region and an obstacle affecting the passage is identified in the second to-be-optimized region, the wheeled robot bypasses the to-be-optimized region through the standby route one Y2.

[0097] It should be understood that, for the case of "if an obstacle affecting the passage is identified in the first to-be-optimized region", if an obstacle affecting the passage is identified in the second to-be-optimized region on this basis, standby route two Y3 is preferentially selected, and if no obstacle affecting the passage can be identified or is not identified in the second to-be-optimized region, standby route one Y1 is preferentially selected.

[0098] Embodiment two:

[0099] In a preferred embodiment, the total number of times that each pixel point in the obstacle record statistical framework diagram appears as an obstacle is counted according to the total number of times that each pixel point appears as an obstacle in the obstacle record statistical framework diagram, including: setting a statistical interval, determining a plurality of statistical time periods according to the statistical interval; associating the obstacle records with the plurality of statistical time periods according to the collection time; and counting the total number of times that each pixel point in the associated obstacle record statistical framework diagram appears as an obstacle within the plurality of statistical time periods.

[0100] In a preferred embodiment, each pixel point is assigned a value by comparing the total number of times within the plurality of statistical time periods with a preset number threshold, to obtain a time period hotspot diagram; and all time period hotspot diagrams are integrated in chronological order of the plurality of statistical time periods to obtain a basic hotspot diagram.

[0101] It is considered that the wheeled robot may perform a task in a specific time period, such as a cleaning robot performing cleaning work at ten o'clock in the morning. If the motion route is planned based on the obstacle hotspot diagram drawn from the work record data of all time periods, it is obviously not in line with the actual task requirements.

[0102] The obstacles appearing in the target area can be counted by time period, and the associated obstacle record in each statistical period is obtained. According to the obstacle record statistics, the total number of times of obstacles appearing in each pixel point in the frame diagram in the corresponding statistical period is counted, and the total number of times is assigned to the preset number threshold (set according to the number of working record data in the statistical period) to obtain the period hotspot diagram in each statistical period. The period hotspot diagrams of each statistical period are integrated in time sequence to obtain the basic hotspot diagram.

[0103] For example, assuming that the statistical interval is one hour, the day can be divided into twenty-four statistical periods, that is, the total number of times of obstacles appearing in each statistical period is obtained by counting the obstacle record in each hour. Then the total number of times is compared with the preset number threshold, and if it is greater than the preset number threshold, it is assigned to 1, otherwise it is assigned to 0, and the period hotspot diagram corresponding to the statistical period is obtained.

[0104] If the wheel series robot performs a task across the statistical period, considering the stability of the fixed obstacle, the basic route planned in the target area is fixed, that is, the probability or position of the moving obstacle may be different as time changes. For this case, the basic route is divided into each statistical period, such as dividing the basic route into statistical period one and statistical period two, that is, if the task is performed along the basic route, the first half is in statistical period one and the second half is in statistical period two. In the part of statistical period one, the standby route one is planned based on the period hotspot diagram corresponding to statistical period one, and in the part of statistical period two, the standby route one is planned based on the period hotspot diagram corresponding to statistical period two. In this case, standby route two can not be planned.

[0105] It is worth noting that if the wheel series robot cannot perform the task according to the motion route during the task execution process, or other unexpected situations occur, the built-in route planning algorithm is used to plan the route in real time to ensure the task execution. The route planning algorithm is, for example, A* algorithm, dynamic window method, etc.

[0106] The above embodiments are only used to illustrate the technical method of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A motion path adaptive planning system for a wheeled robot, characterized in that, The route planning module comprises a database connected thereto; The route planning module is configured to obtain work record data from the database, and draw an obstacle hotspot map of a target region based on the work record data; the target region is a working region of the wheel train robot; and The wheel train robot is planned a basic route in the target region based on the task parameters; the basic route is adaptively and redundantly optimized based on the obstacle hotspot map to obtain a motion route of the wheel train robot; the adaptive and redundant optimization refers to planning a redundant route for the obstacle; the basic route is planned according to a current position and a target position of the wheel train robot, and the boundary of the target region and the fixed obstacle in the target region are considered in the planning; The obstacle hotspot map of the target region is drawn based on the work record data, comprising: The work record data is extracted, and obstacle records in the target region are counted based on the work record data; the obstacle records comprise an obstacle type, an obstacle position and an obstacle occurrence time, and the obstacle is an obstacle that needs to be bypassed by the wheel train robot; The obstacle hotspot map of the target region is drawn based on the obstacle records; The obstacle hotspot map of the target region is drawn based on the obstacle records, comprising: A frame map of the target region is drawn, and a total number of times of occurrence of obstacles at each pixel point in the frame map is counted based on the obstacle records; Each pixel point is assigned by comparing the total number of times and a preset number threshold to obtain a basic hotspot map; and the basic hotspot map is adjusted in combination with robot parameters of the wheel train robot to obtain the obstacle hotspot map; Each pixel point is assigned by comparing the total number of times and a preset number threshold, comprising: The number of the work record data is counted, and a number threshold is obtained by multiplying the number by an adjustment coefficient; the adjustment coefficient is less than 1; If the total number of times is greater than the number threshold, the corresponding pixel point is assigned a value of 1; otherwise, the corresponding pixel point is assigned a value of 0; The basic hotspot map is adjusted in combination with the robot parameters of the wheel train robot, comprising: Obstacle regions in the basic hotspot map are identified based on the assignment result; It is judged whether a space between adjacent obstacle regions is allowed to pass through by the wheel train robot based on the robot parameters; if not, the adjacent obstacle regions are merged to obtain a new obstacle region; the robot parameters comprise a size, a turning radius and a minimum turning angle.

2. The motion path adaptive planning system of a wheel-legged robot according to claim 1, wherein The basic route is adaptively and redundantly optimized based on the obstacle hotspot map, comprising: It is judged whether the basic route intersects with an obstacle region in the obstacle hotspot map; if so, the intersecting obstacle region is marked as a region to be optimized, and an intersection point is extracted; A motion route of the wheel train robot bypassing the region to be optimized is planned based on the intersection point as a redundant route one; the redundant route one and the basic route are spliced and fused to obtain the motion route.

3. The motion path adaptation planning system of a wheel-legged robot according to claim 2, wherein, After the motion route of the wheel train robot bypassing the region to be optimized is planned based on the intersection point, the method further comprises: Verifying whether a distance between adjacent areas to be optimized is less than a preset distance threshold; if yes, planning a movement route that bypasses the adjacent areas to be optimized simultaneously as a backup route two based on the intersection point; wherein the preset distance threshold is set according to a turning radius of the wheel series robot; Splicing and fusing the backup route one, the backup route two and the basic route to obtain the movement route.

4. The motion path adaptation planning system of a wheel-legged robot according to claim 3, wherein, Verifying whether a distance between adjacent areas to be optimized is less than a preset distance threshold, comprising: Marking the intersection point between the area to be optimized and the basic route as a feature point one and a feature point two; wherein the feature point one is closer to the initial position, and the feature point two is closer to the target position; Calculating a route distance between the feature point two of the previous area to be optimized and the feature point one of the subsequent area to be optimized; when the route distance is less than the preset distance threshold, it is determined that the distance between the adjacent areas to be optimized is less than the preset distance threshold.

5. The motion path adaptation planning system of a wheel-legged robot according to claim 1, wherein, According to the obstacle record, counting the total number of times that each pixel point in the framework map appears an obstacle, comprising: Setting a statistical interval, determining a plurality of statistical time periods according to the statistical interval; associating the obstacle record with a plurality of statistical time periods according to the collection time; Based on the associated obstacle record, counting the total number of times that each pixel point in the framework map appears an obstacle within a plurality of statistical time periods.

6. The motion path adaptation planning system of a wheeled robot according to claim 5, wherein, By comparing the total number of times within a plurality of statistical time periods with a preset number threshold, each pixel point is assigned a value to obtain a time period heat map; Integrating all the time period heat maps according to the time sequence of a plurality of statistical time periods to obtain a basic heat map.

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