A narrow road path planning method for intelligent robots

Abstract

The present application provides a narrow passage path planning method for an intelligent robot, including the steps of: obtaining the minimum rectangular frame of the chassis of the intelligent robot, and generating a square physical model simulating the intelligent robot in the hierarchical cost map with the short side of the minimum rectangular frame as the side length; establishing a virtual layer, and generating virtual walls on the boundary lines on both sides of the narrow passage in the virtual layer; integrating the virtual layer into the hierarchical cost map, and updating the hierarchical cost map by increasing the obstacle expansion radius of the narrow passage based on the square physical model until the cost values of the internal grids of the narrow passage and the virtual walls are all greater than zero; planning a passing path for the intelligent robot in the hierarchical cost map after superimposing the virtual layer. The method of the present application simulates the square physical model of the intelligent robot with reverse thinking, establishes virtual walls and updates the hierarchical cost map by increasing the obstacle expansion radius of the narrow passage based on the square physical model, improving the narrow passage passing ability.

Description

Technical Field

[0001] The present application belongs to the field of intelligent robot navigation technology, and more specifically, to a method for planning a narrow lane passage path for an intelligent robot. Background Art

[0002] Intelligent robots have the ability to perceive, make decisions and execute. They mainly involve key technologies such as multi-sensor information fusion, navigation and positioning, path planning, robot vision, intelligent control, and human-machine interface technology. They can replace humans in dangerous and complex labor in unstructured environments. For example, cleaning robots, firefighting robots, patrol robots, and transport robots.

[0003] The current intelligent robot navigation generally uses path planning algorithms and layered cost maps for global and local path planning. However, since its positioning accuracy is around 5 cm and its navigation accuracy is also around 5 cm, collisions are prone to occur when passing through narrow roads, especially irregular narrow roads, and it is even impossible to plan a path. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for planning a narrow road passage path for an intelligent robot, so as to solve the technical problem of insufficient ability of the intelligent robot in the narrow road passage process in the prior art.

[0005] To achieve the above purpose, the technical solution adopted in this application is: to provide a method for planning a narrow passage path for an intelligent robot, comprising the steps of:

[0006] Obtain the minimum rectangular frame of the chassis of the intelligent robot, and use the short side of the minimum rectangular frame as the side length to generate a square physical model of the simulated intelligent robot in the layered cost map;

[0007] A virtual layer is created, and virtual walls are generated on the boundary straight lines on both sides of the narrow road in the virtual layer;

[0008] Integrate the virtual layer into the layered cost map, and based on the square physical model, increase the obstacle expansion radius of the narrow road to update the layered cost map until the cost values ​​of the narrow road and the internal grid of the virtual wall are greater than zero;

[0009] The layered cost map after superimposing the virtual layer plans the path of the intelligent robot.

[0010] In one embodiment of the present application, before establishing the virtual layer, a narrow road detection step is also included:

[0011] In the layered cost map, the distance W between any two points on both sides of the obstacle is measured local ;

[0012] If there are any two points with a width W localSatisfy R w <W local <R l , then these two points are identified as narrow points, where R l is the long side of the smallest rectangle, and R w is the short side of the smallest rectangle;

[0013] Perform curve fitting on the narrow points connected on both sides respectively to obtain the boundary of the narrow passage;

[0014] Define the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage.

[0015] In an embodiment of the present application, the method for defining the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage includes the steps of:

[0016] Obtain the starting point coordinates and ending point coordinates of the boundary of each narrow passage;

[0017] Connect the starting point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage entrance line, and connect the ending point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage exit line;

[0018] Connect the boundaries of a pair of corresponding narrow passages and their corresponding narrow passage entrance lines and narrow passage exit lines to obtain the narrow passage area;

[0019] Set the area other than the narrow passage area in the irregular passage as the non-narrow passage area.

[0020] In an embodiment of the present application, the method for generating virtual walls on the straight lines of the two side boundaries of the narrow passage in the virtual layer includes the steps of:

[0021] Based on the detected exits and entrances of each narrow passage, divide them into two straight virtual walls respectively;

[0022] Among them, the virtual wall is parallel or tangent to the boundary of the corresponding narrow passage.

[0023] In an embodiment of the present application, the thickness Q of the virtual wall w Satisfies: Q w ≤W local -R w ; The length Q of the width of the virtual wall l Satisfies: Q l ≥R l .

[0024] In an embodiment of the present application, the method for planning the passing path of an intelligent robot in the layered cost map after superimposing the virtual layer includes the steps of:

[0025] Set temporary target points in each non-narrow passage area in the irregular passage;

[0026] Generate a path from the starting point to the nearest temporary target point using a global path planning algorithm;

[0027] After reaching the temporary target point, use the global path planning algorithm to generate a path to the next temporary target point until reaching the end point.

[0028] In an embodiment of the present application, the temporary target point is located within the virtual wall at the entrance and exit of the narrow passage.

[0029] In an embodiment of the present application, the global path planning algorithm is the Theta* algorithm.

[0030] In an embodiment of the present application, the method for determining that the intelligent robot reaches the temporary target point within the virtual wall includes the steps of:

[0031] Create a virtual wall polygon;

[0032] If each vertex of the minimum bounding rectangle is within the virtual wall polygon, it is determined that the intelligent robot reaches the temporary target point within the virtual wall.

[0033] In an embodiment of the present application, the method for creating a virtual wall polygon includes the steps of:

[0034] Let the starting point of the virtual wall of the first narrow passage be A(x1, y1), the end point be B(x2, y2), the starting point of the virtual wall of the second narrow passage be C(x3, y3), and the end point be D(x4, y4);

[0035] If d1 is greater than d2, connect ABCD in sequence, otherwise connect ABDC in sequence to obtain the virtual wall polygon; where

[0036] .

[0037] The beneficial effect of the narrow passage passing path planning method for the intelligent robot provided by the present application is that: compared with the prior art, it simulates the square physical model of the intelligent robot with reverse thinking, establishes a virtual wall and updates the hierarchical cost map by increasing the obstacle expansion radius of the narrow passage based on the square physical model, fully assigns cost values to each grid inside the narrow passage, and comprehensively overcomes the technical prejudice from four aspects, improving the narrow passage passing ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic diagram of the hierarchical structure of the hierarchical cost map in the prior art;

[0040] Figure 2 It is a schematic diagram of an intelligent robot in the prior art when planning a path;

[0041] Figure 3 It is a flowchart of the narrow passage navigation path planning method for an intelligent robot provided by an embodiment of the present application;

[0042] Figure 4 It is a comparison schematic diagram of the method for an intelligent robot to generate a minimum rectangle and a square physical model;

[0043] Figure 5 It is a schematic diagram of the inscribed circle and circumscribed circle of the minimum rectangle and the square physical model;

[0044] Figure 6 It is a schematic diagram of an intelligent robot provided by an embodiment of the present application when planning a path in a regular channel;

[0045] Figure 7 It is a schematic diagram of an intelligent robot provided by an embodiment of the present application when planning a path in an irregular channel. Detailed implementation manners

[0046] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element.

[0048] It should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0049] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0050] First of all, it must be explained that layered cost maps are an existing mature technology for representing environmental information. Layered cost maps refer to dividing the data in the cost map into several isolated map layers according to different semantics. Each layer of the map only tracks one type of obstacle or constraint, and then modifies the main cost map used for path planning. At the same time, this layered cost map can represent complex situations so that navigation can be performed according to some different situations.

[0051] See also Figure 1 The layered cost map consists of three layers. The first layer is the Static Layer. Figure 1 It is usually generated in advance using the SLAM (Simultaneous Localization and Mapping) algorithm to represent the location of walls and other static obstacles. The second layer is the Obstacle Layer, which collects data from high-precision sensors (such as lasers and RGB-D cameras), and the locations of sensor readings are marked as occupied. The third layer is the Inflation Layer, which inserts a buffer around each fatal obstacle to ensure that the intelligent robot does not collide with the obstacle. These three layers are combined into the main map (the final cost map) for route planning.

[0052] See also Figure 2 The layered cost map is in the form of a grid map. The cost value of each grid is 0-255. Each grid can be divided into the following five types according to the cost value:

[0053] (1) Fatal conflict: The grid value is 254. There is an obstacle in the grid, and the geometric center of the intelligent robot is not allowed to exist in the grid.

[0054] (2) Inscribed conflict: The grid cost value is 253. When the geometric center of the intelligent robot is located in this grid, there will be obstacles within the inscribed circle contour of the intelligent robot, and a conflict will inevitably occur.

[0055] (3) Risk: The grid cost value is greater than 0 and less than 253. When the geometric center of the intelligent robot is located in the grid, there will be obstacles between the circumscribed circle and the inscribed circle contour of the intelligent robot, which poses a risk of conflict.

[0056] (4) Free area: The grid cost value is 0, without obstacles. When the geometric center of the intelligent robot is located in this grid, all obstacles are outside the circumcircle of the intelligent robot and will not affect the intelligent robot.

[0057] (5) Unknown area: The grid cost value is 255, located outside the map boundary. Generally, the intelligent robot cannot reach this grid.

[0058] In addition, based on the above hierarchical cost map, multiple path planning algorithms can be run for navigation, such as Dijkstra, A*, D*, Theta*. Therefore, the hierarchical cost map and any one of the above path planning algorithms constitute the core technology of intelligent robot navigation (hereinafter referred to as the existing navigation core technology).

[0059] Since the chassis contours are all of various shapes, in order to adapt to the above core technology, people in this field will simulate the chassis of the intelligent robot as a circle, square or rectangle to simplify the algorithm. However, no matter what shape is simulated, it will be larger than the actual chassis area (please refer to Figure 4 to the left algorithm route). In order to further reduce the probability of collision accidents, a certain multiple will be enlarged on the basis of the actual chassis shape. Technicians generally believe that the area of the simulated chassis shape should at least cover the area of the actual chassis, otherwise the risk of collision will inevitably increase, and thus further limit the narrow passage ability of the intelligent robot.

[0060] However, this application belongs to the improvement based on the hierarchical cost map and any one of the above path planning algorithms, and creates a method to improve the narrow passage ability of the intelligent robot with reverse thinking.

[0061] Please refer to Figures 3 to 6 together. Now, the narrow passage path planning method for the intelligent robot provided by the embodiment of this application will be described. The narrow passage path planning method for the intelligent robot is applicable to intelligent robots with rectangular, trapezoidal, triangular, rhombic, parallelogram-shaped and irregular-shaped chassis (excluding squares and circles).

[0062] The narrow passage path planning method for the intelligent robot includes the steps:

[0063] Step S1, obtain the minimum rectangular frame of the chassis of the intelligent robot, and use the short side of the minimum rectangular frame as the side length to generate a square physical model of the simulated intelligent robot in the hierarchical cost map.

[0064] It can be understood that in the field of automatic navigation of intelligent robots, the chassis generally refers to the occupied area from the top-down perspective of the intelligent robot, which includes the body chassis and surrounding protruding structures such as the outer shell, camera, lidar, etc.

[0065] In step S1, first obtain the minimum rectangle of the chassis of the intelligent robot. In the prior art, this minimum rectangle is directly or enlarged proportionally as the physical model of the intelligent robot. However, in this embodiment, instead, a square physical model simulating the intelligent robot is generated with the short side of the minimum rectangle as the side length (please refer to Figure 4 to the right algorithm route). Therefore, its essence is to reduce the chassis area of the intelligent robot. According to the classification and introduction of the cost values of the aforementioned 5 types of grids, for the square physical model after the processing of step S1, compared with the minimum rectangle, both its inscribed circle contour and circumscribed circle contour will become smaller. Therefore, the inscribing conflicts and risky grids in the narrow passage will decrease, and the grids in the free area will increase significantly.

[0066] In this way, the direct effect that step S1 can bring is: increasing the passable area in the narrow passage. However, since this square physical model does not actually completely cover the chassis of the intelligent robot, the intelligent robot will freely choose a path in the free area when planning a path. The direct negative impact of this result is: the risk of collision with obstacles when passing through the narrow passage based on the existing path planning algorithm increases.

[0067] Step S2, establish a virtual layer, and generate virtual walls on the straight lines of the two sides of the narrow passage in the virtual layer.

[0068] In step S2, the virtual layer is established based on the basic framework of the hierarchical cost map, which is beneficial to integrating the virtual layer into the hierarchical cost map. After generating virtual walls on the straight lines of the two sides of the narrow passage in the virtual layer, it is equivalent to extending the length of the narrow passage, that is, there will be a large number of grids in the free area between the virtual walls and the narrow passage.

[0069] Step S3, integrate the virtual layer into the hierarchical cost map, and update the hierarchical cost map by increasing the obstacle inflation radius of the narrow passage based on the square physical model until the cost values of the grids inside the narrow passage and the virtual walls are all greater than zero.

[0070] In step S3, in this embodiment, the obstacle inflation radius of the narrow passage is also increased until the cost values of the grids inside the narrow passage are all greater than zero, that is, the grids in the free area are cleared, so that each grid inside the narrow passage has a certain cost value.

[0071] Specifically, in the first aspect, before entering the narrow passage, the intelligent robot can use the virtual wall as a buffer to effectively avoid obstacles at the entrance of the narrow passage when adjusting its direction; in the first aspect, since the virtual wall is generated from the straight lines of the two sides of the narrow passage and there are no grids in the free area within the virtual wall, the virtual wall can be used to allow the intelligent robot to adjust to the optimal pose in advance. The optimal pose is that the direction of the intelligent robot is directly facing the entrance of the narrow passage, and its position is on the central axis of the narrow passage entrance; in the third aspect, when the intelligent robot enters the narrow passage and uses the existing path planning algorithm for navigation, the cost value needs to be considered at each step, which restricts the freedom of its path selection and forces the path to be planned in the center; in the fourth aspect, since the radius of the obstacle in the narrow passage is inflated, the actual obstacle is smaller than the obstacle during path planning, so this robustness exactly makes up for the aforementioned direct negative impact.

[0072] Step S4: Plan the passing path of the intelligent robot on the layered cost map after superimposing the virtual layer.

[0073] In this way, in step S4, based on the four aspects of the effects in step S3, it can effectively pass through conventional narrow passages (such as turnstiles) and irregular narrow passages (such as S-shaped narrow passages). Most prominently, the method of this embodiment can pass through ultra-narrow terrains where the width of the narrow passage is less than the long side of the minimum rectangular frame of the chassis and greater than the short side, while in the existing path navigation algorithm, this ultra-narrow terrain will be directly regarded as a closed passage.

[0074] Compared with the prior art, the intelligent robot narrow passage passing path planning method provided in this application simulates the square physical model of the intelligent robot with reverse thinking, establishes a virtual wall, updates the layered cost map by increasing the obstacle inflation radius of the narrow passage based on the square physical model, fully assigns cost values to each grid inside the narrow passage, and comprehensively overcomes the technical prejudice from four aspects, improving the narrow passage passing ability.

[0075] In an embodiment of this application, please refer to Figure 7 , before establishing the virtual layer in step S2, it further includes a narrow passage detection step:

[0076] In the layered cost map, measure the distance W between any two points of the obstacles on both sides local ;

[0077] If there exists a width W between any two points local satisfying R w <W local <R l , then these two points are identified as narrow points, where R l is the long side of the minimum rectangular frame, and R w is the short side of the minimum rectangular frame;

[0078] Curve fitting is respectively performed on the narrow points connected on both sides to obtain the boundary of the narrow passage;

[0079] Define the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage.

[0080] It can be understood that when there is W local <R w , it is lower than the limit value that the intelligent robot can pass through, and it is generally recognized as a closed passage; when W local satisfies R w <W local <R l , the narrow passage passing mode is triggered, that is, the steps of steps S2-S4 are executed; when W local >R l , it passes through based on the existing navigation core technology.

[0081] Different from the conventional narrow passage detection definition method, in this embodiment, by detecting the distance W between any two points local to determine the narrow point, and then performing curve fitting on the narrow points to obtain the boundary of the narrow passage. In this way, the irregular passage in the real scene is segmented into several narrow passages according to its bending degree and width change in a segmented form. Then, virtual walls are generated segment by segment inside the continuous irregular passage according to the shape of the narrow passage, and the non-narrow passage area (W local >R l ) in the continuous irregular passage is used as a buffer zone for pre-adjusting to the optimal pose.

[0082] That is, this embodiment can segment the irregular narrow passage in the conventional sense into the narrow passage defined in this embodiment, and then independently execute the above-mentioned steps S2-S5 for each segmented narrow passage. In this way, the path of the intelligent robot is finely guided and corrected through multiple virtual walls. Each virtual wall is parallel to the boundary of the segmented narrow passage to ensure that the intelligent robot can adapt to the bending and inclination of the narrow passage and improve the passing ability of the irregular narrow passage.

[0083] Furthermore, please refer to Figure 7 , in step S2, the method for defining the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage includes the steps:

[0084] Obtain the starting point coordinates and ending point coordinates of the boundary of each narrow passage;

[0085] Connect the starting point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage entrance line, and connect the ending point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage exit line;

[0086] Connect the boundaries of a pair of corresponding narrow passages and their corresponding narrow passage entrance lines and narrow passage exit lines to obtain the narrow passage area;

[0087] Set the area in the irregular channel except the narrow channel area as the non - narrow channel area.

[0088] It can be understood that according to the boundary definition of the narrow channel in this embodiment, it can be found that the non - narrow channel area is located on both sides of the narrow channel area. Furthermore, the virtual wall is located in the non - narrow channel area. According to the above narrow channel detection steps, the space of the non - narrow channel area is larger than that of the narrow channel area, that is, it is restricted that the virtual wall is only generated in the area with sufficient space, so the probability of the intelligent robot colliding during pose adjustment is reduced, which is beneficial to the intelligent robot to adjust its pose in place.

[0089] Furthermore, please refer to Figure 7 , in step S2, the method of generating virtual walls on both side boundary lines of the narrow channel in the virtual layer includes the steps:

[0090] Based on the detected exits and entrances of each narrow channel, divide them into two straight - line virtual walls respectively;

[0091] Among them, the virtual wall is parallel or tangent to the boundary of the corresponding narrow channel.

[0092] It can be understood that when the exit or entrance of the boundary of the narrow channel is a straight line, the virtual wall is parallel to the boundary of the corresponding narrow channel. When the exit or entrance of the boundary of the narrow channel is arc - shaped, the virtual wall is tangent to the boundary of the corresponding narrow channel. In this way, the virtual wall and the narrow channel achieve natural transition, optimizing the pose of the intelligent robot before entering the narrow channel.

[0093] Furthermore, in step S2, the thickness Q of the virtual wall w satisfies: Q w ≤W local -R w ; the length Q of the width of the virtual wall l satisfies: Q l ≥R l .

[0094] It can be understood that when the thickness Q of the virtual wall w satisfies Q w =W local -R w , the virtual walls can just accommodate the body position of the intelligent robot; when the length Q of the virtual wall l satisfies Q l =R lWhen the length of the virtual wall extension is exactly the actual length of an intelligent robot, rather than the side length of the square physical model. In this way, when navigating in a continuous irregular narrow passage with limited space, the virtual wall can dynamically adjust the shape of the virtual wall according to the actual space size of each section of the narrow passage, and then adjust as close as possible to the optimal pose in the limited space, further refine and correct the path of the intelligent robot to improve the passing ability of the irregular narrow passage.

[0095] Further, please refer to Figure 7 , in step S4, the method for planning the passing path of the intelligent robot on the layered cost map after superimposing the virtual layer includes the steps of:

[0096] Set temporary target points in each non-narrow passage area within the irregular passage;

[0097] Use the global path planning algorithm to generate the path from the starting point to the nearest temporary target point;

[0098] After reaching the temporary target point, use the global path planning algorithm to generate the path to the next temporary target point until reaching the end point.

[0099] It can be understood that on the one hand, due to the complex environment within the irregular passage, it may also include real-time dynamically changing obstacles and bifurcated sections. Therefore, by setting temporary target points in each non-narrow passage area within the irregular passage and gradually generating the path, it can fully adapt to the above complex environment.

[0100] In addition, since this application is based on the existing navigation core technology, based on the defined narrow passage width of this application, when directly navigating to the end point position, the navigation core technology may show that it is impassable or a detour path. Therefore, adopting the solution of this embodiment can also solve the above mismatch problem.

[0101] Further, please refer to Figure 7 , in step S4, the temporary target point is located within the virtual wall at the entrance and exit of the narrow passage.

[0102] It can be understood that since the virtual wall is located within the non-narrow passage area, when the temporary target point is located within the virtual wall at the entrance and exit of the narrow passage, during the segmented path planning process, it can guide the intelligent robot to adjust its pose before entering the narrow passage to ensure that it is facing the narrow passage entrance and smoothly exits the narrow passage exit along the arc of the narrow passage, reducing collisions; at the same time, by positioning in advance, it is beneficial to reduce the complexity of re-planning the path.

[0103] Further, in step S4, the global path planning algorithm is at least one of Dijkstra algorithm, A* algorithm, and D* algorithm. Preferably, the global path planning algorithm is Theta* algorithm.

[0104] It can be understood that since the square physical model is used in this embodiment and the square physical model does not actually completely cover the chassis of the intelligent robot, there is a risk of the aforementioned direct negative impacts during the narrow passage turning, such as tail swing collision or head swing collision. The characteristic of the Theta* algorithm is that it is suitable for generating smooth paths in complex environments. Therefore, during the movement along the relatively smooth path, the amplitudes of tail swing and head swing can be reduced, and collisions can be reduced.

[0105] Since the Theta* algorithm needs to perform line-of-sight (LOS) detection when expanding each node to determine whether there is an obstacle-free straight path between the current node and the parent node, this process will increase the computational overhead. Especially in complex environments (such as narrow passages), the frequency of line-of-sight detection is high, resulting in a generally longer running time than other algorithms. In addition, the Theta* algorithm generates smooth paths by skipping intermediate nodes through line-of-sight detection. Therefore, in complex environments (such as narrow passages), a local path control algorithm such as the TEB algorithm (time elastic band algorithm) or the DWA algorithm (dynamic window method) also needs to be used for local path planning and dynamic obstacle avoidance, thus further increasing the computational overhead and the running duration. However, in this embodiment, since the continuous irregular passage is segmented into several narrow passages, virtual walls are generated in segments according to the shape of the narrow passages inside the continuous irregular passage, and the non-narrow passage areas are used as buffers. The virtual walls perform local detailed attitude adjustment, which can replace the local detailed control of the local path control algorithm. Furthermore, the computational overhead can be significantly reduced and the running speed can be improved.

[0106] It can be seen that the Theta* algorithm and the segmented narrow passage and virtual wall scheme in the embodiment of the present application form complementary advantages, and can reduce collisions and improve the running speed compared with the prior art.

[0107] Further, after step S4, the method for determining that the intelligent robot reaches the temporary target point inside the virtual wall includes the steps of:

[0108] Create a virtual wall polygon;

[0109] If each vertex of the minimum rectangular frame is inside the virtual wall polygon, it is determined that the intelligent robot reaches the temporary target point inside the virtual wall.

[0110] It can be understood that the virtual wall polygon is an area enclosed by a pair of virtual walls.

[0111] Further, after step S4, the method for creating a virtual wall polygon includes the steps of:

[0112] Let the starting point of the virtual wall of the first narrow passage be A(x1, y1), the ending point be B(x2, y2), the starting point of the virtual wall of the second narrow passage be C(x3, y3), and the ending point be D(x4, y4);

[0113] If d1 is greater than d2, connect ABCD in sequence; otherwise, connect ABDC in sequence to obtain the virtual wall polygon; where

[0114] .

[0115] It can be understood that since the boundaries of the irregular narrow passages are not parallel and their lengths may also be inconsistent due to terrain limitations. Based on the above formula, d1 is equivalent to the distance from A to C plus the distance from B to D, and d2 is equivalent to the distance from A to D plus the distance from B to C. If d1 is greater than d2, connect ABCD in sequence; otherwise, connect ABDC in sequence. In this way, the virtual wall polygon obtained can achieve the maximum area and improve the robustness.

[0116] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for planning a narrow passage path for an intelligent robot, characterized in that: Including the steps: Obtain the minimum rectangular bounding box of the chassis of the intelligent robot, and use the shorter side of the minimum rectangular bounding box as the side length to generate a square physical model of the simulated intelligent robot in the hierarchical cost map; Establish a virtual layer, and generate virtual walls on the boundary lines on both sides of the narrow passage in the virtual layer; Integrate the virtual layer into the hierarchical cost map, and update the hierarchical cost map by increasing the obstacle inflation radius of the narrow passage based on the square physical model until the cost values of the grids inside the narrow passage and the virtual walls are all greater than zero; Plan the passing path of the intelligent robot in the hierarchical cost map after superimposing the virtual layer.

2. The intelligent robot narrow passage navigation path planning method according to claim 1, wherein Before establishing the virtual layer, it also includes a narrow passage detection step: In the hierarchical cost map, measure the distance W between any two points on the obstacles on both sides local ; If there exists a width W between any two points local satisfying R w <W local <R l , then these two points are identified as narrow points, where R l is the long side of the smallest rectangular box, and R w is the short side of the smallest rectangular box; Perform curve fitting on the narrow points connected on both sides respectively to obtain the boundary of the narrow passage; Define the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage.

3. The intelligent robot narrow passage path planning method according to claim 2, wherein, The method for defining the narrow passage area and the non-narrow passage area according to the boundary of the narrow passage includes the steps: Obtain the starting point coordinates and ending point coordinates of the boundary of each narrow passage; Connect the starting point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage entrance line, and connect the ending point coordinates of the boundaries of a pair of corresponding narrow passages to obtain the narrow passage exit line; Connect the boundaries of a pair of corresponding narrow passages and their corresponding narrow passage entrance lines and narrow passage exit lines to obtain the narrow passage area; Set the area other than the narrow passage area in the irregular passage as the non-narrow passage area.

4. The intelligent robot narrow passage navigation path planning method according to claim 3, characterized in that, The method for generating virtual walls on the boundary lines on both sides of the narrow passage in the virtual layer includes the steps: Based on the detected exits and entrances of each narrow passage, divide them into two straight virtual walls respectively; Among them, the virtual wall is parallel or tangent to the boundary of the corresponding narrow passage.

5. The intelligent robot narrow passage navigation path planning method according to claim 4, characterized in that The thickness Q of the virtual wall w Satisfies: Q w ≤W local -R w ; The length Q of the width of the virtual wall l Satisfies: Q l ≥R l .

6. The intelligent robot narrow passage path planning method according to claim 5, wherein, The method for planning the passing path of the intelligent robot in the hierarchical cost map after superimposing the virtual layer includes the steps: Set temporary target points in each non-narrow passage area in the irregular passage; Use the global path planning algorithm to generate a path from the starting point to the nearest temporary target point; After reaching the temporary target point, use the global path planning algorithm to generate a path to the next temporary target point until reaching the end point.

7. The intelligent robot narrow passage navigation path planning method according to claim 6, wherein The temporary target point is located inside the virtual wall at the entrance and exit of the narrow passage.

8. The intelligent robot narrow passage navigation path planning method according to claim 7, characterized in that, The global path planning algorithm is the Theta* algorithm.

9. The intelligent robot narrow passage path planning method according to claim 7, characterized in that, The method for determining whether the intelligent robot reaches the temporary target point inside the virtual wall includes the steps: Create a virtual wall polygon; If each vertex of the minimum rectangular bounding box is inside the virtual wall polygon, it is determined that the intelligent robot reaches the temporary target point inside the virtual wall.

10. The intelligent robot narrow passage path planning method according to claim 9, wherein, The method for creating a virtual wall polygon includes the steps: Let the starting point of the virtual wall of the first narrow passage be A(x1,y1), the ending point be B(x2,y2), the starting point of the virtual wall of the second narrow passage be C(x3,y3), and the ending point be D(x4,y4); If d1 is greater than d2, connect ABCD in sequence, otherwise connect ABDC in sequence to obtain the virtual wall polygon; where d1 and d2 satisfy: 。

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