CN112923940A - Path planning method, device, processing equipment, mobile equipment and storage medium - Google Patents
Path planning method, device, processing equipment, mobile equipment and storage medium Download PDFInfo
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Abstract
The application provides a path planning method, a path planning device, a processing device, a mobile device and a storage medium, wherein the method comprises the following steps: acquiring an environment map of a target environment; acquiring a starting point and a target point of the mobile equipment; planning an optimal path for the mobile equipment to travel in a target environment according to the environment map, the starting point and the target point of the mobile equipment and a preset constraint condition; wherein the preset constraint condition comprises: the shortest transit path for the mobile device from the origin to the destination and the least number of turns during travel of the mobile device from the origin to the destination. The method and the device can plan the real optimal path in the actual scene for the mobile device, and further reduce the actual cost spent by the mobile device from the starting point to the target point.
Description
Technical Field
The present application belongs to the technical field of path planning, and in particular, relates to a path planning method, apparatus, processing device, mobile device, and storage medium.
Background
Mobile devices such as AGVs (Automated Guided vehicles), transfer robots, etc. can travel along a given path according to a path plan to perform tasks such as transferring articles. The mobile device may plan the optimal path based on a path planning algorithm, such as a heuristic search algorithm, to minimize the cost of travel (also referred to as overhead), such as time, energy consumption, etc., that the mobile device needs to spend while traveling along the optimal path. However, the inventor has found that the research of the path planning algorithm in the prior art is mainly based on a simulation platform running a virtual mobile device (such as a virtual vehicle), on which the running state of the virtual mobile device is ideal, and the expected required travel cost is also ideal cost, while in an actual application scenario, the actual cost of a real mobile device traveling along an optimal path planned by a conventional algorithm is usually higher than the ideal cost, and the actual cost spent by the mobile device traveling from a starting point to a target point is still higher because the travel path planned by the conventional algorithm is not actually an optimal path in the actual application scenario.
Disclosure of Invention
In order to overcome, at least to some extent, the problem that the actual cost spent by the mobile device to travel from the starting point to the target point is still high in the related art, the present application provides a path planning method, an apparatus, a processing device, a mobile device, and a storage medium, which can plan a true optimal path in an actual scene for the mobile device, and further reduce the actual cost spent by the mobile device from the starting point to the target point.
In order to achieve the purpose, the following technical scheme is adopted in the application:
in a first aspect, the present application provides a path planning method, including: acquiring an environment map of a target environment; acquiring a starting point and a target point of the mobile equipment; planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and preset constraint conditions; wherein the preset constraint condition comprises: the passing path of the mobile device from the starting point to the target point is shortest, and the number of turns of the mobile device in the process of traveling from the starting point to the target point is the least.
Further, the step of planning the optimal path traveled by the mobile device in the target environment according to the environment map, the starting point and the target point of the mobile device, and preset constraints includes: obtaining a steering cost of the mobile device; wherein the steering cost is used for representing the actual cost of the mobile device required to steer; generating an actual cost evaluation function based on the steering cost and a preset constraint condition; wherein the actual cost evaluation function is used for measuring the actual passing cost of the mobile equipment from the starting point to the target point; and planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and the actual cost evaluation function.
Further, the step of obtaining the steering cost of the mobile device includes: acquiring steering cost of the mobile equipment and straight running cost of the mobile equipment for running for a specified path length; determining a ratio of the steering cost to the straight-ahead cost as a steering cost of the mobile device.
Further, the step of generating an actual cost evaluation function based on the steering cost and a preset constraint condition includes: obtaining a theoretical cost evaluation function of a specified path planning algorithm; wherein, the theoretical cost evaluation function of the specified path planning algorithm is used for measuring the theoretical passing cost of the mobile device from the starting point to the target point, and the constraint condition of the theoretical cost evaluation function is as follows: the traffic path of the mobile device from the starting point to the target point is shortest; generating a steering penalty function based on a steering cost of the mobile device; wherein, the constraint condition of the steering penalty function is: the number of turns of the mobile device in the process of traveling from the starting point to the target point is minimum; and generating an actual cost evaluation function according to the theoretical cost evaluation function of the specified path planning algorithm and the steering penalty function.
Further, the steering penalty function is a binary function, where the steering penalty function value corresponding to the mobile device when the mobile device is steering is the steering cost, and the steering penalty function value corresponding to the mobile device when the mobile device is moving straight is zero.
Further, the specified path planning algorithm comprises an A star algorithm, a simulated annealing algorithm or an artificial potential field algorithm.
In a second aspect, the present application provides a path planning apparatus, including: the map acquisition module is used for acquiring an environment map of a target environment; the point acquisition module is used for acquiring a starting point and a target point of the mobile equipment; the path planning module is used for planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and preset constraint conditions; wherein the preset constraint condition comprises: the passing path of the mobile device from the starting point to the target point is shortest, and the number of turns of the mobile device in the process of traveling from the starting point to the target point is the least.
In a third aspect, the present application provides a processing apparatus comprising: a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the first aspects.
In a fourth aspect, the present application provides a mobile device configured with the processing device of the third aspect.
In a fifth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any of the first aspects described above.
The above-mentioned path planning method, apparatus, processing device, mobile device and storage medium provided by the present application can obtain an environment map (established based on a target environment) corresponding to the mobile device and obtain a starting point and a target point of the mobile device, and further plan an optimal path for the mobile device to travel in the target environment according to the environment map, the starting point and the target point of the mobile device, and a preset constraint condition, where the preset constraint condition includes: the shortest transit path for the mobile device from the origin to the destination and the least number of turns during travel of the mobile device from the origin to the destination. The method provided by the application fully considers that the actual cost of the real mobile equipment after traveling according to the optimal path planned by the traditional algorithm is usually higher than the ideal cost, and the main reason that the traveled path is actually not the optimal path is the steering overhead of the mobile equipment in the actual traveling process, so that the shortest passing path from the starting point to the target point of the mobile equipment is restrained and the minimum steering times of the mobile equipment are restrained when the path is planned, and the additional time consumption and energy consumption of the mobile equipment caused by multiple actual steering are reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method of path planning in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram of a grid map shown in accordance with an exemplary embodiment;
FIG. 3 is a flow chart illustrating a method of path planning in accordance with an exemplary embodiment;
FIG. 4 is a diagram illustrating a path planning result of an A-star algorithm in accordance with an exemplary embodiment;
FIG. 5 is a diagram illustrating a path planning result of an improved A-star algorithm in accordance with an exemplary embodiment;
fig. 6 is a block diagram illustrating a structure of a path planning apparatus according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
The path planning refers to that mobile equipment such as an AGV (automatic guided vehicle) and a transfer robot reaches a specified target point from a preset starting point according to an optimal path on the premise of depending on a specific policy algorithm and meeting a certain performance index. The research of the existing path planning algorithm is mainly based on a simulation platform for running virtual mobile devices, the traveling cost corresponding to the obtained optimal path is also a theoretical cost (also called an ideal cost), the actual cost of the mobile device in an actual application scene is usually higher than the ideal cost, and from the actual cost of final cost, the path traveled by the mobile device according to the existing path planning algorithm is in fact mostly not a true optimal path. The inventor has found that the main reason is that the running state of the virtual mobile device on the simulation platform is ideal, and the steering overhead of the mobile device is not considered. Specifically, in the prior art, the time and energy consumption of the mobile device for performing a steering action are ignored, and in addition, the mobile device needs to decelerate from the original speed before steering and needs to accelerate to the original speed after steering, so that the operation time which theoretically should be at the original speed is reduced, the energy consumption caused by time consumption and acceleration and deceleration is correspondingly increased, and the working stability of the mobile device (such as easy occurrence of faults, easy failure in accurate alignment with a two-dimensional code landmark and the like) is also reduced by multiple times of steering. It will be appreciated that even if the path lengths of the two paths are the same, the resulting travel costs will be different if the two paths are diverted differently. Although the existing path planning algorithm can plan the shortest path, the actual motion characteristics of the mobile device are not considered in the process of planning the shortest path, so that the final path planning result inevitably increases the actual traveling cost of the mobile device due to unplanned steering.
In order to improve the above problem, embodiments of the present application provide a path planning method, an apparatus, a processing device, a mobile device, and a storage medium, which can fully consider the cost influence of steering overhead on the mobile device in an actual application scenario when planning an optimal path for the mobile device, so as to plan a true optimal path in the actual scenario for the mobile device, and further reduce the actual cost spent by the mobile device from a starting point to a target point.
The embodiment of the present invention provides a path planning method, which is mainly used for planning a path for a mobile device capable of automatically traveling, such as an AGV, a transfer robot, or other transportation equipment similar to a car, and is not limited herein. The method may be performed by a processing device configured for the mobile device, such as a processor on the mobile device, a server communicating with the mobile device, and the like, and is not limited herein. Referring to a flowchart of a path planning method shown in fig. 1, the method mainly includes the following steps S102 to S106:
step S102, obtaining an environment map of a target environment; the environment map is established based on a target environment, and the target environment is the environment where the mobile device is located.
In one embodiment, in order to plan a path for a mobile device more conveniently and quickly, the established environment map may be a grid map, the grid map is composed of point lines, the points are used for representing positioning marks of the mobile device, and the positioning marks may be two-dimensional codes attached to a ground plane; the lines are used to characterize traversable paths of the mobile device. For ease of understanding, reference may be made to a schematic diagram of a grid map as shown in fig. 2, where the dots in fig. 2 represent positioning indicia, the lines represent traversable paths of the mobile device, the arrows at the two ends of the lines represent that the paths are traversable in both directions, and the lower left corner symbolizes the mobile device with black squares. In a specific implementation manner, each dot in the grid map may correspond to a two-dimensional code attached to the ground surface, so that a mobile device such as an AGV cart scans the two-dimensional code to determine point location information, an edge between two-dimensional codes is a passable path, in order to simplify the form of the grid map, all edges may be double-edge lanes with equal length, and no intersection exists between the edges. The position of the mobile device may be represented in two-dimensional coordinates formed by x and y.
Of course, the above grid map is only one form of the environment map, and in practical applications, other forms of maps, such as rendering a 3D map, may also be adopted, and the map form is not limited herein.
Step S104, acquiring a starting point and a target point of the mobile device. The starting point and the target point of the mobile device may be preset and may be directly obtained in this step.
Step S106, planning an optimal path of the mobile device in the target environment according to the environment map, the starting point and the target point of the mobile device and a preset constraint condition; wherein the preset constraint condition comprises: the shortest transit path for the mobile device from the origin to the destination and the least number of turns during travel of the mobile device from the origin to the destination.
The step also fully considers the steering expense of the mobile equipment when planning the shortest path for the mobile equipment, and the shortest passing path and the minimum steering times of the mobile equipment are restrained, so that the traveling cost (time consumption, energy consumption and the like) of the mobile equipment from the starting point to the target point is the lowest possible. If there are multiple shortest paths from the starting point to the destination point, the shortest path with the least number of turns is taken as the best path.
The method provided by the application fully considers that the actual cost of the real mobile equipment after traveling according to the optimal path planned by the traditional algorithm is usually higher than the ideal cost, and the main reason that the traveled path is actually not the optimal path is the steering overhead of the mobile equipment in the actual traveling process, so that the shortest passing path from the starting point to the target point of the mobile equipment is restrained and the minimum steering times of the mobile equipment are restrained when the path is planned, and the additional time consumption and energy consumption of the mobile equipment caused by multiple actual steering are reduced.
In one embodiment, planning the optimal path for the mobile device to travel in the target environment according to the environment map, the starting point and the target point of the mobile device, and the preset constraint condition may be performed with reference to the following steps a to c:
step a, obtaining the steering cost of the mobile equipment; wherein the steering cost is used to represent the actual cost of the mobile device required to steer. Because the mobile device needs to involve processes of deceleration before steering, steering and acceleration after steering in actual traveling, additional steering overhead is brought, the traveling cost of the mobile device after traveling along the path planned according to the conventional path planning rule is often higher, and based on the fact that the steering cost of the mobile device is considered when the path is planned according to the embodiment of the application.
In a specific embodiment, the steering cost of the mobile device and the straight cost of the mobile device for traveling a specified path length can be obtained; the ratio of the steering cost to the straight-ahead cost is then determined as the steering cost of the mobile device. The steering cost can be determined according to the time taken by the mobile device to complete the steering action, the straight-moving cost can be determined according to the time taken by the mobile device to travel for the specified path length, for convenience of calculation, the ratio of the steering cost to the straight-moving cost is used as the steering cost k value of the mobile device, the magnitude of the steering cost k value is mainly determined by the type of the mobile device, the steering cost k values of different mobile devices are different, and the k value can be determined by specifically testing the mobile device in an actual application scene, such as by taking an AGV cart as an example, and determining that the k value is 2.5 through testing and the motion characteristics of the AGV cart. After the steering cost k value is obtained, the k value can be used as an empirical value for evaluating the steering cost of the mobile device and as a calculation basis of a subsequent steering penalty value.
B, generating an actual cost evaluation function based on the steering cost and a preset constraint condition; wherein the actual cost evaluation function is used for measuring the actual passing cost of the mobile device from the starting point to the target point.
In one embodiment, the method can be implemented by referring to the following steps b1 to b 3:
b1, acquiring a theoretical cost evaluation function of the specified path planning algorithm; the theoretical cost evaluation function of the specified path planning algorithm is used for measuring the theoretical passing cost of the mobile equipment from a starting point to a target point, and the constraint condition of the theoretical cost evaluation function is as follows: the transit path of the mobile device from the starting point to the destination point is shortest.
The above-mentioned assigned path planning algorithm may include, for example, an a-star algorithm, a simulated annealing algorithm, or an artificial potential field algorithm, and the like, which is not limited herein. It can be understood that these specified path planning algorithms are all based on the simulation platform, and the operation state of the virtual mobile device operating in the simulation platform is an ideal state, such as no extra time and energy consumption is needed in steering, and what is obtained is the theoretical passing cost. However, in an actual application scenario, due to the motion characteristics of the mobile device, no matter whether the steering action is performed or the steering is prepared to be decelerated or accelerated after steering, extra time and energy consumption are brought, and therefore the theoretical passing cost is lower than the actual passing cost.
Step b2, generating a steering penalty function based on the steering cost of the mobile equipment; the constraint conditions of the steering penalty function are as follows: the number of turns in the travel of the mobile device from the origin to the target point is minimal.
In one embodiment, the steering penalty function is a binary function, where the steering penalty function value corresponding to the mobile device when the mobile device is turning is a steering cost, and the steering penalty function value corresponding to the mobile device when the mobile device is moving straight is zero. The steering penalty function is p (n), if n is a steering point, p (n) is k, and if n is not a steering point, p (n) is 0. The path with the least number of turns is planned as possible by introducing penalty values.
And b3, generating an actual cost evaluation function according to the theoretical cost evaluation function and the steering penalty function of the specified path planning algorithm. In one embodiment, the actual cost evaluation function is the sum of the theoretical cost evaluation function and the steering penalty function, i.e., the actual cost evaluation function is the theoretical cost evaluation function + the steering penalty function.
And c, planning the optimal path of the mobile equipment in the target environment according to the environment map, the starting point and the target point of the mobile equipment and the actual cost evaluation function.
Because the actual cost evaluation function is compatible with the theoretical cost evaluation function and the steering penalty function, the length of a passing path between a starting point and a target point and the number of steering times are considered when the optimal path is planned for the mobile equipment, so that the real optimal path of the mobile equipment in an actual application scene is planned and obtained.
In an embodiment, the specified path planning algorithm may be an a-star algorithm, a simulated annealing algorithm, an artificial potential field algorithm, or the like, which is not limited herein, and the specified path planning algorithm is described as the a-star algorithm as an example below, at this time, the path planning method provided by the present application may be considered as an improved a-star algorithm, that is, the steering overhead is considered on the basis of the a-star algorithm planning path. For ease of understanding, the a-star algorithm is first introduced as follows:
the a star algorithm is a classic heuristic Search algorithm in path planning, which may be referred to as an a-star Search algorithm, that is, nodes in all directions in an environment map (such as may be represented in a grid form) are evaluated from a starting point through an evaluation function, and a direction node with the minimum overhead is selected according to an evaluation result as a point through which a mobile device such as a vehicle passes until an end point to which the mobile device reaches is selected, and the algorithm combines the advantages of BFS (break First Search algorithm) and Dijkstra algorithm: and when heuristic search is carried out to improve the algorithm efficiency, an optimal path can be guaranteed to be found based on an evaluation function.
Specifically, in the a-star algorithm, f (n) is an evaluation function, which represents the total estimated cost (theoretical cost) from the starting node to the target node, and g (n) represents the actual cost from the starting point to a node n, h (n) represents the estimated cost from the node n to the target node (i.e., the aforementioned target point), and the node n is the node next to the current node where the mobile device is located; the estimation function of the a-algorithm is then: f (n) ═ g (n) + h (n). H (n) may be estimated using manhattan, that is, h (n) ═ n.x-gold.x | + | n.y-gold.y |, where gold.x is the x coordinate of the target point, gold.y is the y coordinate of the target point, n.x is the x coordinate of node n, and n.y is the y coordinate of node n.
If G (n) is 0, only the evaluation function H (n) from any node n to the target node is calculated, and the cost from the starting point to the node n is not calculated, the A star algorithm can be directly converted into the BFS algorithm, a greedy strategy is used at the moment, the speed is fastest, and only the node nearest to the target node is considered each time
If H (n) is 0, only the shortest path G (n) from the starting point to any node n needs to be calculated, and no evaluation function H (n) is calculated, so that the problem of the shortest path with a single source is converted, namely the direct operation is carried out by Dijkstra algorithm, and the most nodes need to be calculated at the moment.
The A star algorithm combines a BFS algorithm and a Dijkstra algorithm, so that the advantage that the Dijkstra algorithm can obtain an optimal path is possessed, the advantage that the BFS shortens exploration time through a heuristic method is possessed, and the optimal path is planned quickly.
It should be noted that the above mentioned costs can be characterized by time and also by distance, and in the embodiment of the present application, because steering overhead is involved, time characterization can be adopted.
On this basis, referring to the flowchart of a path planning method shown in fig. 3, the method includes the following steps S302 to S308:
step S302, a pre-established environment model is obtained. The environment model, that is, the aforementioned environment map, may be implemented by a two-dimensional planar model in a grid form, or may be implemented by a 3D model, which is not limited herein.
And step S304, determining the steering cost of the AGV. The steering cost, i.e. the aforementioned k value, is not described herein in detail.
And S306, improving the A star algorithm based on the steering cost of the AGV to obtain the improved A star algorithm. The improved a-star algorithm takes the shortest passing path and the minimum number of turns on the passing path as constraint conditions, and can also be understood as that the improved a-star algorithm takes the shortest actual time consumption of the AGV in an actual application scene as a target, so that the traveling cost of the AGV from a starting point to a target point is the lowest.
The improved a star algorithm may be characterized by f (n) ═ g (n) + h (n) + p (n), where p (n) is a steering penalty function, if n points are steering points, p (n) ═ k, and if n points are not steering points, p (n) ═ 0.
When the improved a-star algorithm is used for judging whether a node is a turning point, the judgment can be performed according to the coordinates of the node, and for convenience of understanding, how to judge whether the next node is a turning point is illustrated: if the coordinates of the point a are (0,0), the coordinates of the point b are (1,0), and the coordinates of the point c are (2,0), then there is no turn between b and c because ya ═ yb ═ yc, but if the coordinates of the point c are (1,1), then b to c are a turn process because xa is not equal to xb, and yb is not equal to yc.
And S308, acquiring a starting point and a target point of the AGV, and planning an optimal path from the starting point to the target point for the AGV by using the improved A star algorithm.
The improved A-star algorithm has the advantages that the Dijkstra algorithm can obtain an optimal path, the BFS algorithm shortens exploration time through a heuristic method, steering cost of the mobile equipment in an actual application scene is fully considered, the optimal path with the least steering times can be searched quickly, and the traveling cost of the mobile equipment is reduced.
In order to intuitively show the difference between the path planning results of the a-star algorithm and the improved a-star algorithm, see a schematic diagram of the path planning result of the a-star algorithm shown in fig. 4 and a schematic diagram of the path planning result of the improved a-star algorithm shown in fig. 5, the lengths of the optimal paths planned in fig. 4 and 5 are the same, but there are 3 turning points in fig. 4, and there are only 1 turning point in fig. 5, when the mobile device travels according to the optimal path planned in fig. 5, the number of times of turning is the minimum, so the turning cost is the minimum, and the final actual traveling cost is the minimum. Compared with a conventional path planning method, the path planning method provided by the embodiment of the application also considers steering overhead when planning the shortest path, and can select the shortest path with the least steering times, so that the steering overhead of the mobile equipment in an actual application scene is reduced as much as possible, and the traveling cost of the mobile equipment is effectively reduced.
In summary, the route planning method provided in this embodiment restricts the shortest passing route from the starting point to the destination point of the mobile device and also restricts the minimum turning times of the mobile device when planning the route, thereby reducing additional time consumption and energy consumption caused by multiple actual turning of the mobile device.
Corresponding to the foregoing path planning method, an embodiment of the present application further provides a path planning apparatus, referring to a structural block diagram of the path planning apparatus shown in fig. 6, which mainly includes:
a map acquisition module 62 for acquiring an environment map of the target environment; the environment map is established based on a target environment, and the target environment is the environment where the mobile device is located;
a point obtaining module 64, configured to obtain a starting point and a target point of the mobile device;
a path planning module 66, configured to plan an optimal path for the mobile device to travel in the target environment according to the environment map, the starting point and the target point of the mobile device, and preset constraint conditions; wherein the preset constraint condition comprises: the shortest transit path for the mobile device from the origin to the destination and the least number of turns during travel of the mobile device from the origin to the destination.
The device provided by the application fully considers that the actual cost of the real mobile equipment after traveling according to the optimal path planned by the traditional algorithm is usually higher than the ideal cost, and the main reason that the traveled path is actually not the optimal path is the steering overhead of the mobile equipment in the actual traveling process, so that the shortest passing path from the starting point to the target point of the mobile equipment is restrained and the minimum steering times of the mobile equipment are restrained when the path is planned, and the additional time consumption and energy consumption of the mobile equipment caused by multiple actual steering are reduced.
In one embodiment, the path planning module 66 includes:
the steering cost obtaining unit is used for obtaining the steering cost of the mobile equipment; wherein the steering cost is used for representing the actual cost of the mobile equipment required to steer;
the function generating unit is used for generating an actual cost evaluation function based on the steering cost and a preset constraint condition; the actual cost evaluation function is used for measuring the actual passing cost of the mobile equipment from the starting point to the target point;
and the path planning unit is used for planning the optimal path of the mobile equipment in the target environment according to the environment map, the starting point and the target point of the mobile equipment and the actual cost evaluation function.
In one embodiment, the steering cost obtaining unit is further configured to: acquiring the steering cost of the mobile equipment and the straight running cost of the specified path length of the mobile equipment; and determining the ratio of the steering cost to the straight-going cost as the steering cost of the mobile equipment.
In one embodiment, the path planning unit is further configured to: obtaining a theoretical cost evaluation function of a specified path planning algorithm; the theoretical cost evaluation function of the specified path planning algorithm is used for measuring the theoretical passing cost of the mobile equipment from a starting point to a target point, and the constraint condition of the theoretical cost evaluation function is as follows: the passing path of the mobile equipment from the starting point to the target point is shortest; generating a steering penalty function based on the steering cost of the mobile device; the constraint conditions of the steering penalty function are as follows: the number of turns of the mobile device in the process of traveling from the starting point to the target point is minimum; and generating an actual cost evaluation function according to a theoretical cost evaluation function and a steering penalty function of the specified path planning algorithm.
In one embodiment, the steering penalty function is a binary function, where the steering penalty function value corresponding to the mobile device when the mobile device is turning is a steering cost, and the steering penalty function value corresponding to the mobile device when the mobile device is moving straight is zero.
In one embodiment, the specified path planning algorithm comprises an a-star algorithm, a simulated annealing algorithm, or an artificial potential field algorithm.
The device provided by the embodiment has the same implementation principle and technical effect as the foregoing embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiment for the portion of the embodiment of the device that is not mentioned.
Further, an embodiment of the present application further provides a processing device, including: a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs a path planning method as in any one of the preceding claims.
Further, an embodiment of the present application also provides a mobile device, where the mobile device is configured with the processing device as described above. The mobile device may be a movable device such as an AGV cart, a transfer robot, or the like.
Further, an embodiment of the present application further provides a storage medium, where a computer program is stored on the storage medium, and the computer program is executed by a processor to perform the steps of any of the path planning methods.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, the meaning of "plurality" means at least two unless otherwise specified.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present, and further, as used herein, connected may include wirelessly connected; the term "and/or" is used to include any and all combinations of one or more of the associated listed items.
Any process or method descriptions in flow charts or otherwise described herein may be understood as: represents modules, segments or portions of code which include one or more executable instructions for implementing specific logical functions or steps of a process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method of path planning, comprising:
acquiring an environment map of a target environment;
acquiring a starting point and a target point of the mobile equipment;
planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and preset constraint conditions; wherein the preset constraint condition comprises: the passing path of the mobile device from the starting point to the target point is shortest, and the number of turns of the mobile device in the process of traveling from the starting point to the target point is the least.
2. The method of claim 1, wherein the step of planning the optimal path for the mobile device to travel in the target environment according to the environment map, the starting point and the target point of the mobile device, and preset constraints comprises:
obtaining a steering cost of the mobile device; wherein the steering cost is used for representing the actual cost of the mobile device required to steer;
generating an actual cost evaluation function based on the steering cost and a preset constraint condition; wherein the actual cost evaluation function is used for measuring the actual passing cost of the mobile equipment from the starting point to the target point;
and planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and the actual cost evaluation function.
3. The method of claim 2, wherein the step of obtaining the steering cost of the mobile device comprises:
acquiring steering cost of the mobile equipment and straight running cost of the mobile equipment for running for a specified path length;
determining a ratio of the steering cost to the straight-ahead cost as a steering cost of the mobile device.
4. The method of claim 2, wherein the step of generating an actual cost evaluation function based on the steering cost and a preset constraint condition comprises:
obtaining a theoretical cost evaluation function of a specified path planning algorithm; wherein, the theoretical cost evaluation function of the specified path planning algorithm is used for measuring the theoretical passing cost of the mobile device from the starting point to the target point, and the constraint condition of the theoretical cost evaluation function is as follows: the traffic path of the mobile device from the starting point to the target point is shortest;
generating a steering penalty function based on a steering cost of the mobile device; wherein, the constraint condition of the steering penalty function is: the number of turns of the mobile device in the process of traveling from the starting point to the target point is minimum;
and generating an actual cost evaluation function according to the theoretical cost evaluation function of the specified path planning algorithm and the steering penalty function.
5. The method of claim 4, wherein the steering penalty function is a binary function, wherein the steering penalty function value corresponding to the mobile device when turning is the steering cost, and wherein the steering penalty function value corresponding to the mobile device when going straight is zero.
6. The method of claim 4, wherein the specified path planning algorithm comprises an A-star algorithm, a simulated annealing algorithm, or an artificial potential field algorithm.
7. A path planning apparatus, comprising:
the map acquisition module is used for acquiring an environment map of a target environment;
the point acquisition module is used for acquiring a starting point and a target point of the mobile equipment;
the path planning module is used for planning an optimal path for the mobile equipment to travel in the target environment according to the environment map, the starting point and the target point of the mobile equipment and preset constraint conditions; wherein the preset constraint condition comprises: the passing path of the mobile device from the starting point to the target point is shortest, and the number of turns of the mobile device in the process of traveling from the starting point to the target point is the least.
8. A processing device, comprising: a processor and a storage device;
the storage device has stored thereon a computer program which, when executed by the processor, performs the method of any of claims 1 to 6.
9. A mobile device, characterized in that the mobile device is provided with a processing device according to claim 8.
10. A storage medium having a computer program stored thereon, wherein the computer program is adapted to perform the steps of the method according to any of the claims 1 to 6 when executed by a processor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113507413A (en) * | 2021-07-22 | 2021-10-15 | 中国联合网络通信集团有限公司 | Route optimization method and device and computing equipment |
| CN114639088A (en) * | 2022-03-23 | 2022-06-17 | 姜妹英 | Big data automatic navigation method |
| CN116451888A (en) * | 2022-10-21 | 2023-07-18 | 中国科学院沈阳自动化研究所 | Multi-AVG-based flexible production workshop collaborative scheduling method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107121146A (en) * | 2017-06-02 | 2017-09-01 | 西安电子科技大学 | Optimum path planning method based on road chain depth |
| CN108413976A (en) * | 2018-01-23 | 2018-08-17 | 大连理工大学 | A kind of climbing robot intelligence paths planning method and system towards multi-state |
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| CN111026133A (en) * | 2019-12-31 | 2020-04-17 | 北京易控智驾科技有限公司 | Path planning method, vehicle and computer readable medium |
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| CN111721297A (en) * | 2020-06-19 | 2020-09-29 | 重庆大学 | Path planning method for multiple AGV of intelligent garage |
| US20200318981A1 (en) * | 2019-04-03 | 2020-10-08 | Verizon Patent And Licensing Inc. | Pathfinding through a road network with turn complexities |
| CN112113573A (en) * | 2020-09-18 | 2020-12-22 | 武汉理工大学 | Planning method for coverage path of single unmanned measurement boat |
-
2021
- 2021-01-11 CN CN202110030330.5A patent/CN112923940A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107121146A (en) * | 2017-06-02 | 2017-09-01 | 西安电子科技大学 | Optimum path planning method based on road chain depth |
| CN108413976A (en) * | 2018-01-23 | 2018-08-17 | 大连理工大学 | A kind of climbing robot intelligence paths planning method and system towards multi-state |
| CN108444488A (en) * | 2018-02-05 | 2018-08-24 | 天津大学 | Based on etc. steps sample A* algorithms unmanned local paths planning method |
| US20200318981A1 (en) * | 2019-04-03 | 2020-10-08 | Verizon Patent And Licensing Inc. | Pathfinding through a road network with turn complexities |
| CN111026133A (en) * | 2019-12-31 | 2020-04-17 | 北京易控智驾科技有限公司 | Path planning method, vehicle and computer readable medium |
| CN111289007A (en) * | 2020-03-23 | 2020-06-16 | 南京理工大学 | Parking AGV path planning method based on improved ant colony algorithm |
| CN111721297A (en) * | 2020-06-19 | 2020-09-29 | 重庆大学 | Path planning method for multiple AGV of intelligent garage |
| CN112113573A (en) * | 2020-09-18 | 2020-12-22 | 武汉理工大学 | Planning method for coverage path of single unmanned measurement boat |
Non-Patent Citations (1)
| Title |
|---|
| 于军琪等: "并行排序蚁群算法规划仓库AGV路径研究", 《机械科学与技术》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113507413A (en) * | 2021-07-22 | 2021-10-15 | 中国联合网络通信集团有限公司 | Route optimization method and device and computing equipment |
| CN113507413B (en) * | 2021-07-22 | 2022-07-29 | 中国联合网络通信集团有限公司 | Route optimization method and device and computing equipment |
| CN114639088A (en) * | 2022-03-23 | 2022-06-17 | 姜妹英 | Big data automatic navigation method |
| CN116451888A (en) * | 2022-10-21 | 2023-07-18 | 中国科学院沈阳自动化研究所 | Multi-AVG-based flexible production workshop collaborative scheduling method |
| CN116451888B (en) * | 2022-10-21 | 2024-01-09 | 中国科学院沈阳自动化研究所 | Flexible production workshop cooperative scheduling method based on multiple AGVs |
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