Full-coverage path planning method and device for outdoor scene and medium

Abstract

The invention discloses a full-coverage path planning method and device for an outdoor scene and a medium, and the method comprises the steps: determining a region needing to be covered, and dividing the determined region into a plurality of sub-regions according to a preset shape; obtaining the width of the sub-region, and calculating a dynamic regulation factor of each sub-region according to the width; combining the connected sub-regions with the same dynamic regulation factor, and constructing a coordinate system corresponding to the sub-regions; in each sub-region, constructing a spanning tree by taking the steering times of the minimum coverage path as an optimization target, and obtaining a path containing the minimum steering times; and converting the paths in all the sub-regions to a global map coordinate system and connecting the paths to obtain a complete global coverage path. The cleaning coverage rate, the cleaning efficiency and the cleaning quality of the cleaning robot on the cleaning area can be effectively improved. The robot can be widely applied to the technical field of robots.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a full-coverage path planning method, device and medium for outdoor scenes. Background Art

[0002] In recent years, more and more service robots have been assigned the task of performing simple service functions, such as cleaning robots, food delivery robots, etc. How to improve robot performance, reduce maintenance requirements, and expand the scope of functions has become an important topic. Cleaning work is a labor-intensive job, and the development of robotics technology has promoted the development of cleaning robots. At present, cleaning robots can complete cleaning tasks on structured roads, but there is no mature solution for outdoor semi-structured scenes (sidewalks, parks, etc.). In outdoor semi-structured scenes, cleaning robots need to be able to achieve high coverage, low repeatability, and high efficiency in completing cleaning tasks. Before performing cleaning tasks, unmanned cleaning robots need to pre-build a map of the cleaning environment and plan a fully covered cleaning path so that the cleaning robot can track the path and achieve full coverage cleaning tasks.

[0003] The existing coverage path planning methods are mainly classified as follows:

[0004] (1) Coverage method based on unit decomposition. This method is a classic planning algorithm that is more suitable for simple or regular static environments. The quality of unit decomposition is directly affected by the complexity of the environment. In a complex environment with a large number of obstacles, the decomposition calculation may become complicated and inefficient, and a lot of post-processing is required to ensure complete coverage. After completing the unit decomposition, it is necessary to use the bow-shaped coverage method in each sub-unit to complete the coverage path planning task for the sub-unit, and it may be necessary to optimize the path operation separately for each unit, which increases the difficulty of algorithm implementation and debugging. After completing the path planning within the unit, it is necessary to connect the paths of all units. The paths used for connection may cause path duplication, which may increase the cost of repeated shuttle paths between different units.

[0005] (2) Neural network-based coverage method. Neural network-based coverage method has good learning ability and adaptability in complex scenes, especially in irregular environments and dynamic scenes. However, the performance of neural networks is highly dependent on the quality and quantity of training data. However, outdoor scenes are usually diverse and complex. If the training data cannot cover these diversities, the neural network may not be able to generalize. For large-scale, high-quality coverage tasks, the cost of data collection and labeling is high. In addition, the training and reasoning of neural network models require high computing resources, and it is difficult to deploy complex neural network models on cleaning robots with limited performance.

[0006] (3) Spanning tree-based covering algorithms. Traditional spanning tree-based covering methods often use a single-size grid to discretize the coverable area, which may result in the construction of the spanning tree failing to fully cover the area, resulting in coverage blind spots. Traditional spanning tree-based covering methods perform well in constructing simple covering paths, but usually only guarantee coverage and do not optimize path length or task efficiency, which may lead to low task efficiency. Summary of the invention

[0007] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a full-coverage path planning method, device and medium for outdoor semi-structured scenes.

[0008] The first technical solution adopted by the present invention is:

[0009] A full coverage path planning method for outdoor scenes includes the following steps:

[0010] Determine the area to be covered, and divide the determined area into several sub-areas according to preset shapes;

[0011] Get the width of the sub-region, and calculate the dynamic adjustment factor of each sub-region according to the width;

[0012] Merge the connected sub-regions with the same dynamic adjustment factors and construct the coordinate system corresponding to the sub-regions;

[0013] In each sub-area, a spanning tree is constructed with the optimization goal of minimizing the number of turns of the coverage path to obtain a path with the least number of turns;

[0014] The paths in all sub-areas are converted to the global map coordinate system and connected to obtain a complete global coverage path.

[0015] Further, the adjustable width of the covering tool of the cleaning robot is used as the dynamic adjustment factor δ;

[0016] The calculation formula of the dynamic adjustment factor δ is:

[0017]

[0018] Where W is the width of the sub-region and N is a positive integer.

[0019] Furthermore, in each sub-area, a spanning tree is constructed with minimizing the number of turns of the coverage path as the optimization goal to obtain a path with the least number of turns, including:

[0020] A spanning tree-based covering method is used for path planning; the path planned based on the spanning tree covering method is represented by generating a covering path from the starting point along the branches of the spanning tree and finally returning to the starting point;

[0021] When constructing a spanning tree in a sub-area, a mathematical model is established with the minimum number of turns in the covering path as the optimization goal to solve the minimum spanning tree. The turns in the covering path only appear at the end points of the branches or at the connections between branches. The optimization goal of the mathematical model is converted into solving the minimum number of spanning tree branches and the optimal branch connection method.

[0022] Furthermore, the optimization objective of the mathematical model is converted into solving the minimum number of spanning tree branches and the optimal branch connection method, including:

[0023] Each sub-region is discretized into grids according to the dynamic adjustment factor, and a large grid is composed of four small grids;

[0024] Set a spanning tree node in the center of each large grid, using the variable v i To represent the spanning tree node, where v i ∈{v1,v2,……,v n}, n is the number of large grids; introduce a direction operator DIR(v i )∈{H,V} is used to indicate the extension direction of the branch where the node is located, H indicates that the node is in the horizontal direction, and V indicates that the node is in the vertical direction;

[0025] The adjacent nodes that meet the constraints are merged one by one to obtain the spanning tree branches. The constraints are: (1) The DIR (v i ) are all H; (2) The DIR (v i ) are all V; therefore, the total number of branches in the coverable area depends on the directions assigned to these nodes; since each spanning tree branch has two endpoints, in order to simplify the model, solving the minimum number of spanning tree branches is equivalent to solving the minimum number of endpoints, and only the left endpoint of the horizontal branch or the upper endpoint of the vertical branch is calculated.

[0026] Further, the step of solving the minimum number of spanning tree branches is equivalent to solving the minimum number of endpoints, including:

[0027] Define L(v i ) is v i The left node of T(v i ) is v i In order to identify nodes close to the boundary, the boundary variable d is introduced i represents the boundary, when d iWhen DIR(d i )=V;when d i When indicating the left boundary, DIR(d i )=H;

[0028] Define the endpoint discrimination operator END(v i ) is used to identify node v i Whether it is an endpoint, the expression is as follows:

[0029]

[0030] Where END(v i ) is a binary operator used to determine v i Whether it is the endpoint of a spanning tree branch;

[0031] According to the operator END(v i )A mathematical model is established to solve the problem with the minimum number of endpoints, that is, the minimum number of branches.

[0032] Furthermore, according to the operator END(v i )The expression of the mathematical model established is as follows:

[0033]

[0034] Furthermore, the paths in all sub-areas are converted to the global map coordinate system and connected to obtain a complete global coverage path, including:

[0035] After solving the minimum number of spanning tree branches, connect all branches to get a complete spanning tree: the path generated along the spanning tree branch has two turns at both ends, so the turn cost of all unconnected branches is:

[0036] C branches =4kC t

[0037] In the formula, k represents the minimum number of branches solved, C t Indicates the cost of a single turn is 1;

[0038] The total turn cost of the spanning tree is expressed as:

[0039]

[0040] In the formula, Represents node v i and v j The change in the steering cost after the connection, Indicates that the node v i and v j connected edges;

[0041] A greedy strategy is used to select the branch connection method to connect all branches to obtain a spanning tree, so that the total turning cost is minimized;

[0042] All paths are projected into the global coordinate system through the rotation matrix R and the translation matrix T, and connected to obtain a fully covered path with the least number of turns.

[0043] Furthermore, the turn cost corresponding to any node of the spanning tree is expressed as:

[0044]

[0045] In the formula, deg(v i ) represents node v i The degree of is the number of its neighboring nodes;

[0046] There are five ways to connect any two spanning tree branches: L-shaped connection, n-shaped connection, T-shaped connection, h-shaped connection and H-shaped connection; connecting any two branches will change the connection node v i and v j The degree of change affects the total turn cost C of the spanning tree; The expression is:

[0047]

[0048] The second technical solution adopted by the present invention is:

[0049] An electronic device comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a full-coverage path planning method for outdoor scenes as described above.

[0050] The third technical solution adopted by the present invention is:

[0051] A computer-readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a full-coverage path planning method for outdoor scenes as described above.

[0052] The fourth technical solution adopted by the present invention is:

[0053] A computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0054] The beneficial effects of the present invention are as follows: the present invention introduces a dynamic adjustment factor to reasonably divide a number of sub-areas with different geometric features, and constructs a coordinate system corresponding to the sub-areas to improve the overall coverage rate. In each sub-area, a spanning tree is constructed with the optimization goal of minimizing the number of turns in the coverage path, and a path with the least number of turns can be obtained. Finally, the paths in all sub-areas are converted to the global map coordinate system and connected to obtain a complete global coverage path. The present invention can effectively improve the cleaning coverage rate, cleaning efficiency and cleaning quality of the cleaning robot for the cleaning area. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 is a schematic diagram of the sub-region division and merging in an embodiment of the present invention; wherein, Figure 1 (a) is the preliminary divided sub-area. Figure 1 (b) is the merged sub-region;

[0057] Figure 2 is a schematic diagram of solving the minimum number of spanning tree branches in an embodiment of the present invention;

[0058] Figure 3 is a schematic diagram of a spanning tree branch connection method in an embodiment of the present invention; Figure 3 (a) is an L-shaped connection. Figure 3 (b) is an n-type connection. Figure 3 (c) is a T-shaped connection. Figure 3 (d) is an H-shaped connection. Figure 3 (e) is an H-shaped connection;

[0059] Figure 4 is a flow chart of a full coverage cleaning path planning method in an embodiment of the present invention;

[0060] Figure 5It is a flowchart of the steps of a full coverage path planning method for outdoor scenes in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0062] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do 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 understood as a limitation on the present invention.

[0063] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0064] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0065] Example 1

[0066] like Figure 4 and Figure 5 As shown, this embodiment provides a full coverage path planning method for outdoor scenes, which specifically includes the following steps:

[0067] S1. Determine the area to be covered, and divide the determined area into several sub-areas according to preset shapes;

[0068] S2, obtaining the width of the sub-region, and calculating the dynamic adjustment factor of each sub-region according to the width;

[0069] S3, merging the sub-regions with the same dynamic adjustment factor and being connected, and constructing the coordinate system corresponding to the sub-regions;

[0070] S4. In each sub-area, a spanning tree is constructed with the optimization goal of minimizing the number of turns of the coverage path, and a path with the least number of turns is obtained;

[0071] S5. Convert and connect the paths in all sub-areas to the global map coordinate system to obtain a complete global coverage path.

[0072] The working principle of this embodiment is as follows: Since the cleaning robot has the characteristic of dynamically adjusting the covering tool, a dynamic adjustment factor can be introduced according to the dynamic adjustment range of the covering tool to improve the regional coverage rate. The method first divides the area into several sub-areas with different geometric features according to the dynamic adjustment factor. Then, a spanning tree is constructed in each sub-area with the goal of minimizing the number of path turns, so as to obtain a full coverage path with the least number of turns in the sub-area. Finally, the coverage path in each sub-area is projected into the global map coordinate system, and all the sub-area paths are connected to obtain a full coverage cleaning path with high coverage, few turns, and no path duplication.

[0073] The above method is explained in detail below with reference to the accompanying drawings and specific embodiments.

[0074] See also Figure 4 In the cleaning task of outdoor semi-structured scenes, it is required to cover the cleaning area as completely as possible and reduce the time to complete the cleaning task as much as possible. Therefore, this embodiment proposes a full coverage path planning method for outdoor semi-structured scenes. The method mainly includes calculating the dynamic adjustment factor and sub-area division, and constructing the minimum spanning tree.

[0075] (1) Calculation of dynamic adjustment factors and sub-region division

[0076] Since the outdoor semi-structured area may contain many channels with different widths and inclinations, this embodiment introduces a dynamic adjustment factor δ and performs sub-area division. First, each channel is divided into a sub-area according to the orientation position of each channel, such as Figure 1 As shown in (a), the dynamic adjustment factor δ of each sub-region is calculated:

[0077]

[0078] 1≤δ≤1.5 (2)

[0079] Where W is the geometric width of the feasible channel, δ can be used to represent the size of the discretized grid, and can also represent the adjustable width of the covering tool of the cleaning robot. Merging sub-regions with the same δ can reduce the number of sub-regions and construct a coordinate system for the merged sub-regions, such as Figure 1 As shown in (b), it lays a good foundation for the subsequent coverage path planning task. Figure 1 Before the sub-regions are merged, they include sub-region 1, sub-region 2, sub-region 3, sub-region 4, sub-region 5 and sub-region 6. Since the dynamic adjustment factors δ corresponding to sub-region 1, sub-region 4, sub-region 5 and sub-region 6 are the same, these sub-regions are merged to finally obtain sub-region 1, sub-region 2 and sub-region 3; coordinate systems are constructed for these three sub-regions respectively.

[0080] in Figure 1 The black area in the middle is an obstacle. In some embodiments, the robot collects environmental information (such as radar information or image information) through sensors, identifies the boundary of the object based on the collected information, and identifies the area and obstacles that the robot needs to cover. This can be achieved by existing technical means, which is not the focus of this patent and is not described here. In other embodiments, the environment can be directly input externally without the need for collection through sensors.

[0081] (2) Constructing a minimum spanning tree

[0082] Since the path planned based on the spanning tree coverage (STC) method is expressed as a coverage path generated from the starting point along the branches of the spanning tree and finally returning to the starting point. This path feature is consistent with the cleaning task behavior of the cleaning robot. Therefore, the coverage path planning method of this embodiment takes STC as the core and proposes a full coverage cleaning path planning method with high coverage rate and few turns.

[0083] When constructing a spanning tree in a sub-region, a mathematical model is established with the minimum number of turns in the covering path as the optimization goal to solve the minimum spanning tree. The turns in the covering path only appear at the end points of the branches or at the connection between branches, so the optimization goal of the mathematical model can be converted into solving the minimum number of spanning tree branches and the optimal branch connection method.

[0084] like Figure 2 As shown in the figure, first use the grid to discretize the coverable area. A large grid is composed of four small grids. Assume that the coverable area is discretized into n large grids, and a spanning tree node is set at the center of each large grid. Use the variable v i To represent the spanning tree node, where v i ∈{v1,v2,……,v n}. Introduce a direction operator DIR(vi )∈{H,V} is used to indicate the extension direction of the branch where the node is located, H indicates that the node is in the horizontal direction, and V indicates that the node is in the vertical direction.

[0085] The adjacent nodes that meet the constraints are merged one by one to obtain the spanning tree branches. The constraints are: (1) The DIR (v i ) are all H; (2) The DIR (v i ) are all V. Therefore, the total number of branches in the coverable region depends on the directions assigned to these nodes.

[0086] Since each spanning tree branch has two endpoints, in order to simplify the model, solving the minimum number of spanning tree branches is equivalent to solving the minimum number of endpoints. We only calculate the left endpoint of the horizontal branch or the upper endpoint of the vertical branch. We define L(v i ) is v i The left node of T(v i ) is v i In order to identify nodes close to the boundary, the boundary variable d is introduced. i represents the boundary, when d i When DIR(d i )=V;when d i When indicating the left boundary, DIR(d i )=H. Then, we can define the endpoint discrimination operator END(v i ) is used to identify node v i Is it an endpoint, as follows:

[0087]

[0088] Where END(v i ) is a binary operator used to determine v i Is it the endpoint of the spanning tree branch? Then, we use this operator to build a mathematical model to solve the minimum number of endpoints, that is, the minimum number of branches:

[0089]

[0090]

[0091] After solving the minimum number of spanning tree branches, connect all branches to get a complete spanning tree. The path generated along the spanning tree branch has two turns at both ends, so the turn cost of all unconnected branches is:

[0092] C branches =4kC t (6)

[0093] Where k represents the minimum number of branches solved, C t Indicates the cost of a single turn is 1.

[0094] The turn cost corresponding to any node of the spanning tree is expressed as:

[0095]

[0096] Among them, deg(v i ) represents node v i The degree of a node is the number of its adjacent nodes.

[0097] There are five main ways to connect any two spanning tree branches, namely L-shaped connection, n-shaped connection, T-shaped connection, h-shaped connection and H-shaped connection, such as Figure 3 As shown. Connecting any two branches will change the connecting node v i and v j The degree of , thus affecting the turn cost C of the spanning tree. We define the cost function Represents node v i and v j The change in the steering cost after connection:

[0098]

[0099] in Indicates that the node v i and v j Connected edges.

[0100] The total turn cost of the spanning tree can be expressed as:

[0101]

[0102] A greedy strategy is used to select the branch connection method to connect all branches to obtain a spanning tree, so that the total turning cost is minimized.

[0103] In each sub-area, the above planning method is used to solve a coverage path with the least number of turns. These paths are then projected into the global coordinate system through the rotation matrix R and the translation matrix T. For any path point P in the sub-area coordinate system, i (x, y), its position in the global coordinate system can be obtained by solving the equation Y = RX + T. The path of each sub-area is converted to the global map coordinate system, and finally the paths of all sub-areas are connected to obtain a full coverage path with the least number of turns.

[0104] In summary, compared with the traditional full coverage path planning method, the present invention can effectively improve the coverage rate of the coverage path and significantly reduce the number of turns of the global coverage cleaning path, which helps to improve the cleaning quality and efficiency.

[0105] Example 2

[0106] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the following Figure 5 A full coverage path planning method for outdoor scenes is shown.

[0107] It is understood that the memory may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0108] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented separately through a chip.

[0109] Since the electronic device is an electronic device corresponding to a full-coverage path planning method for outdoor scenes in an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0110] Example 3

[0111] The embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 5 A full coverage path planning method for outdoor scenes is shown.

[0112] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0113] Since the storage medium is a storage medium corresponding to a full-coverage path planning method for outdoor scenes in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0114] Example 4

[0115] In some possible implementations, various aspects of the method of the embodiment of the present invention may also be implemented in the form of a program product, which includes a program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of a full coverage path planning method for outdoor scenes according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0116] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0118] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A full coverage path planning method for outdoor scenes, characterized in that: The following steps are involved: Determine the area to be covered, and divide the determined area into several sub-areas according to preset shapes; Get the width of the sub-region, and calculate the dynamic adjustment factor of each sub-region according to the width; Merge the connected sub-regions with the same dynamic adjustment factors and construct the coordinate system corresponding to the sub-regions; In each sub-area, a spanning tree is constructed with the optimization goal of minimizing the number of turns of the coverage path to obtain a path with the least number of turns; The paths in all sub-areas are converted to the global map coordinate system and connected to obtain a complete global coverage path.

2. A full coverage path planning method for outdoor scenes according to claim 1, characterized in that: The adjustable width of the robot's covering tool is used as a dynamic adjustment factor δ; The calculation formula of the dynamic adjustment factor δ is: Where W is the width of the sub-region and N is a positive integer.

3. The full coverage path planning method for outdoor scenes according to claim 1 is characterized in that: In each sub-area, a spanning tree is constructed with minimizing the number of turns of the coverage path as the optimization goal, and a path with the least number of turns is obtained, including: A spanning tree-based covering method is used for path planning; the path planned based on the spanning tree covering method is represented by generating a covering path from the starting point along the branches of the spanning tree and finally returning to the starting point; When constructing a spanning tree in a sub-area, a mathematical model is established with the minimum number of turns in the covering path as the optimization goal to solve the minimum spanning tree. The turns in the covering path only appear at the end points of the branches or at the connections between branches. The optimization goal of the mathematical model is converted into solving the minimum number of spanning tree branches and the optimal branch connection method.

4. The full coverage path planning method for outdoor scenes according to claim 3 is characterized in that: The method of converting the optimization target of the mathematical model into solving the minimum number of spanning tree branches and the optimal branch connection method includes: Each sub-region is discretized into grids according to the dynamic adjustment factor, and a large grid is composed of four small grids; Set a spanning tree node at the center of each large grid, using the variable v i To represent the spanning tree node, where v i ∈{v1,v2,……,v n }, n is the number of large grids; introduce a direction operator DIR(v i )∈{H,V} is used to indicate the extension direction of the branch where the node is located, H indicates that the node is in the horizontal direction, and V indicates that the node is in the vertical direction; The adjacent nodes that meet the constraints are merged one by one to obtain the spanning tree branches. The constraints are: (1) The DIR (v i ) are all H; (2) The DIR (v i ) are all V; therefore, the total number of branches in the coverable area depends on the directions assigned to these nodes; since each spanning tree branch has two endpoints, in order to simplify the model, solving the minimum number of spanning tree branches is equivalent to solving the minimum number of endpoints, and only the left endpoint of the horizontal branch or the upper endpoint of the vertical branch is calculated.

5. The full coverage path planning method for outdoor scenes according to claim 4 is characterized in that: The method of solving the minimum number of spanning tree branches is equivalent to solving the minimum number of endpoints, including: Define L(v i ) is v i The left node of T(v i ) is v i In order to identify nodes close to the boundary, the boundary variable d is introduced i represents the boundary, when d i When DIR(d i )=V;when d i When indicating the left boundary, DIR(d i )=H; Define the endpoint discrimination operator END(v i ) is used to identify node v i Whether it is an endpoint, the expression is as follows: Where END(v i ) is a binary operator used to determine v i Whether it is the endpoint of a spanning tree branch; According to the operator END(v i )A mathematical model is established to solve the problem with the minimum number of endpoints, that is, the minimum number of branches.

6. A full coverage path planning method for outdoor scenes according to claim 5, characterized in that: According to the operator END(v i )The expression of the mathematical model established is as follows:

7. The full coverage path planning method for outdoor scenes according to claim 1, characterized in that: The paths in all sub-areas are converted to the global map coordinate system and connected to obtain a complete global coverage path, including: After solving the minimum number of spanning tree branches, connect all branches to get a complete spanning tree: the path generated along the spanning tree branch has two turns at both ends, so the turn cost of all unconnected branches is: C branches =4kC t In the formula, k represents the minimum number of branches solved, C t Indicates the cost of a single turn is 1; The total turn cost of the spanning tree is expressed as: In the formula, Represents node v i and v j The change in the steering cost after the connection, Indicates that the node v i and v j connected edges; A greedy strategy is used to select the branch connection method to connect all branches to obtain a spanning tree, so that the total turning cost is minimized; All paths are projected into the global coordinate system through the rotation matrix R and the translation matrix T, and connected to obtain a fully covered path with the least number of turns.

8. The full coverage path planning method for outdoor scenes according to claim 7, characterized in that: The turn cost corresponding to any node of the spanning tree is expressed as: In the formula, deg(v i ) represents node v i The degree of is the number of its neighboring nodes; There are five ways to connect any two spanning tree branches: L-shaped connection, n-shaped connection, T-shaped connection, h-shaped connection and H-shaped connection; connecting any two branches will change the connection node v i and v j The degree of , thus affecting the total turning cost C of the spanning tree; Change The expression is:

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

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