Path planning method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a path planning method and device, equipment and a storage medium, and relates to the technical field of path planning. The method comprises the following steps: acquiring an ordered binary decision diagram corresponding to the robot based on a path planning instruction; the ordered binary decision diagram is used for representing all planned routes in a target scene where the robot is located; obtaining a constraint condition and a task target corresponding to the target scene; determining a target planning path which simultaneously meets the constraint condition and the task target in the ordered binary decision diagram; by determining the target planning path which simultaneously meets the constraint condition and the task target in the ordered binary decision diagram, the state space of the planning process is reduced, and the robot path planning efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of path planning, and in particular to a path planning method, device, equipment and storage medium. Background Art

[0002] In the field of robotics, path planning is one of the key technologies for robots to achieve autonomous navigation and intelligent control. Path planning aims to plan an optimal or feasible driving path for the robot based on the robot's starting position, target position, obstacles in the target scene, road conditions and other constraints.

[0003] In the current path planning of complex systems, the search state space is huge. With the increase of system variables, the state space of the system grows exponentially, which easily leads to the problem of state space explosion, resulting in low path planning efficiency.

[0004] Therefore, how to improve the efficiency of robot path planning has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present specification provide a path planning method to solve the problem that state space explosion is very likely to occur in the prior art, resulting in low path planning efficiency.

[0006] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0007] In a first aspect, an embodiment of this specification provides a path planning method, which is applied to a robot and includes:

[0008] Acquire an ordered binary decision graph corresponding to the robot based on the path planning instruction; the ordered binary decision graph is used to represent all planned routes in the target scene where the robot is located;

[0009] Obtaining constraints and task objectives corresponding to the target scenario;

[0010] Determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives;

[0011] The driving path is planned based on the target.

[0012] In a second aspect, an embodiment of the present specification provides a path planning device, which is applied to a robot and includes:

[0013] A first acquisition module is used to acquire an ordered binary decision graph corresponding to the robot based on a path planning instruction; the ordered binary decision graph is used to represent all planned routes in a target scene where the robot is located;

[0014] A second acquisition module is used to acquire the constraint conditions and task objectives corresponding to the target scene;

[0015] A determination module, used to determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives;

[0016] A driving module is used to drive based on the target planning path.

[0017] In a third aspect, an embodiment of the present specification provides a path planning device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the path planning method in scheme one.

[0018] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the path planning method in solution one is implemented.

[0019] An embodiment of the present specification achieves the following beneficial effects: by determining a target planning path in an ordered binary decision graph that satisfies both constraints and task objectives, the state space of the planning process is reduced, thereby improving the efficiency of robot path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 A schematic diagram of a path planning method provided in an embodiment of this specification;

[0022] Figure 2 A schematic diagram of a framework of a path planning method provided in an embodiment of this specification;

[0023] Figure 3 A schematic diagram of a target planning path output in a robot soccer game provided in an embodiment of this specification;

[0024] Figure 4 A schematic diagram of the structure of a path planning device provided in an embodiment of this specification;

[0025] Figure 5 A schematic diagram of the structure of a path planning device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0027] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0028] A path planning method provided in an embodiment of the specification is specifically described in conjunction with the accompanying drawings.

[0029] Figure 1 A schematic diagram of a process flow of a path planning method provided in an embodiment of this specification. From a program perspective, the execution subject of the process can be a program or application client installed on an application server. On the other hand, from a hardware perspective, the execution subject of the process can be a terminal device, and the execution subject can be installed on a robot.

[0030] like Figure 1 As shown, applied to robots, the process may include the following steps:

[0031] Step 110: Obtain an ordered binary decision graph corresponding to the robot based on the path planning instruction; the ordered binary decision graph is used to represent all planned routes in the target scene where the robot is located.

[0032] In the embodiments of this specification, the path planning instructions may include all possible movement instructions of the robot from the starting point to the end point, such as moving forward, turning left, turning right, etc. These instructions will be generated and optimized according to the specific characteristics of the application scenario in which the robot is located. The target scenario can be a cargo transportation scenario, a competitive game scenario, etc.

[0033] An ordered binary decision graph is a special type of directed acyclic graph used to represent Boolean functions. In path planning, each node of an ordered binary decision graph represents a decision point, such as choosing a certain move instruction. The two child nodes of each node represent the two possible results of the decision point, such as executing or not executing a certain instruction. Through this structure, all possible path choices can be clearly represented. In the constructed ordered binary decision graph, all possible paths can be found by traversing the nodes and child nodes.

[0034] Step 120: Obtain the constraints and task objectives corresponding to the target scenario.

[0035] In the embodiments of this specification, the constraints may include environmental constraints, regulatory constraints, etc., and the task objectives may include the target position or target area of ​​the robot, target results, etc.

[0036] Taking the cargo transportation scenario as an example, the constraints may be obstacle locations, road conditions, lighting conditions, traffic rules, etc., and the mission objective may be to deliver the cargo to a designated location or area.

[0037] By fully understanding and analyzing this information, the optimal or feasible driving path can be planned for the robot.

[0038] Step 130: Determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives.

[0039] In the embodiments of this specification, starting from the root node of the ordered binary decision graph, traversal is performed according to the decision points of each node, that is, the movement instructions that the robot may take. At each node, it is evaluated whether the current path satisfies all constraints, and for the paths that satisfy the constraints, it is further evaluated whether they can achieve the task objectives. Based on the evaluation results, one or more optimal paths that both satisfy the constraints and can achieve the task objectives are selected.

[0040] Still taking the cargo transportation scenario as an example, the target planning path can be a path that does not violate traffic regulations and can transport the cargo to the designated location.

[0041] Step 140: Driving based on the target planned path.

[0042] In the embodiments of this specification, the target planning path is parsed to extract the location information of each key node, such as turning points, obstacle avoidance points, etc., and the target planning path is converted into an instruction format that can be executed by the robot, such as coordinates, direction angles, speed, etc., so that the robot can travel according to the planned path and complete the task objectives.

[0043] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification can be interchanged according to actual needs, or some steps can be omitted or deleted.

[0044] In the embodiments of this specification, by determining a target planning path in an ordered binary decision graph that satisfies both constraints and mission objectives, the system search space can be greatly reduced, the efficiency of robot path planning can be improved, and the robot can achieve preset mission objectives accurately and efficiently.

[0045] based on Figure 1 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0046] Optionally, before obtaining the ordered binary decision graph corresponding to the robot based on the path planning instruction in the embodiments of this specification, the method may include:

[0047] Get a multi-agent model;

[0048] The multi-agent model is converted into an explanation system using a programming language, and the explanation system is symbolized into the ordered binary decision diagram.

[0049] In the embodiments of this specification, the multi-agent model is a complex system that describes the interaction and collaboration between multiple robots with autonomous decision-making and action capabilities in a specific environment.

[0050] An interpretation system is a system that can understand and process symbolic information. It can abstract complex real-world problems into symbolic representations, making it easier for computers to process them.

[0051] In path planning, the interpretation system is used to convert the various elements in the multi-agent model, such as the position and speed of the robot and the interaction rules between them, into symbolic representations.

[0052] In practical applications, the programming language of the interpretation system is used to describe the environment of the multi-agent model, and each participant in the system is reduced to an agent, with detailed descriptions of the possible states of each robot in a specific environment, possible behaviors in a certain state, and possible subsequent states of the robot in different external environments. The external environment is reduced to Environment, which describes in detail the specific state of the environment where the robot is located, including information such as the location of each robot. The symbolic part of the system detection tool of the multi-agent model is used to describe the symbolization as an ordered binary decision diagram.

[0053] Optionally, after obtaining the constraint conditions and task objectives corresponding to the target scene in the embodiments of this specification, the method may include:

[0054] The constraint condition is described by using the APTL formula to obtain the constraint condition APTL formula;

[0055] Using the APTL formula to describe the task objective, obtaining the task objective APTL formula;

[0056] The conjunction of the constraint condition APTL formula and the task target APTL formula is converted into a normal form.

[0057] In the embodiment of this specification, the APTL formula is used to describe the properties that the target planning path needs to satisfy. For example, the target planning path to be planned needs to satisfy that there is a strategy for the robot set B so that the interval ending in a certain state on the path satisfies the formula ◇ ＝C＞＞q, and the interval with this state as the starting state satisfies the formula W ＝C＞＞r , it can be reduced to the APTL formula Among them, ＝A＞＞q It means that there is a strategy for the robot set A so that some states on the interval satisfy the formula q, that is, there is a state on the interval that satisfies the atomic formula q, W ＝C＞＞r It means that there is a strategy for the agent set C so that all states in the interval satisfy the atomic formula r. After reducing the system properties using the APTL formula, it is converted into a normal form. The APTL formula in, is the constraint APTL formula, q and r are APTL atomic formulas, ◇ and □ are the timing operators of the APTL formula, and A, B, and C are the robot sets.

[0058] Specifically, NF represents the normal form of the solution formula. NF is the abbreviation of Normal Form. The normal form of the constraint APTL formula is:

[0059]

[0060] The APTL formula is used to describe the constraints of the system, and the APTL formula is also used as the constraint that the system needs to meet for execution.

[0061] For ease of understanding, take a competitive game as an example. In a soccer match between the yellow team robot and the red team robot, plan an execution path that satisfies the yellow team's goal and the yellow team's players' no fouls. For example, when a yellow team player holds or kicks the ball, a teammate on the same team is in an offside position or interferes with the opponent's player, which means that the teammate has committed a foul. APTL formula with constraints

[0062] Where Y is the robot set composed of the yellow team members, y_1.state = shoot means that the state of the yellow team No. 1 is shooting, Environment.y_1x < Environment.r_1x means that the yellow team No. 1 is on the left of the red team No. 1, and the meanings of the other sub-formulas are similar and will not be repeated one by one. This APTL formula describes the situation where there is no yellow team member shooting when the entire path is offside. In the robot football game, if a path is planned so that the yellow team can score, the task goal APTL formula P2 = ◇ ＝Y＞＞yellowscore, where yellowscore is the APTL atomic formula and Y is the robot set. The properties that need to be satisfied by the specification path are specified. The semantics of this formula is that no matter what strategy other robots adopt, the robots in the robot set Y have a strategy that satisfies yellowscore in a certain state in the execution path, and the system state of the yellow team robot (Y) scoring a goal satisfies the atomic formula yellowscore.

[0063] Obtain the conjunction of the constraint APTL formula and the task goal APTL formula, that is, P1∧P2, and transform the conjunction into a normal form.

[0064] The target task APTL formula can be expressed as φ, and the constraint condition APTL formula can be expressed as This can simplify the system model, accurately and quickly plan the path that meets the conditions, and improve the formula Perform lexical and grammatical analysis and convert it into normal form. Formula After conversion to normal form, new APTL formulas will be generated. The newly generated APTL formulas need to be converted into normal form, so converting the normal form is an iterative process until no new formulas are generated.

[0065] Optionally, the determining of the target planning path in the ordered binary decision graph that satisfies both the constraint condition and the task goal in the embodiment of this specification may specifically include:

[0066] Calculating a planning path satisfying the paradigm on the ordered binary decision graph;

[0067] The output planning path is used as the target planning path.

[0068] In the embodiment of this specification, the path that satisfies the paradigm is calculated on the ordered binary decision diagram to find the path that satisfies the formula The path is the target planning path and is output.

[0069] Figure 2 This is a schematic diagram of the target planning path output in the robot soccer game provided in the embodiment of this specification.

[0070] Taking the competition as an example, in the football match between the yellow team robot and the red team robot, the multi-agent model path planning method is used to find the APTL formula that meets the task goal◇ ＝Y＞＞ yellowscore and constraint APTL formula

[0071] The path, such as Figure 2 As shown, this path is a goal path that satisfies the yellow team to score and the yellow team does not commit a foul.

[0072] Further, optionally, in the embodiment of the present specification, the target scenario is a football match, the constraint condition is that the robot does not commit any foul behavior in the football match rules, and the task goal is that the team the robot belongs to scores a goal.

[0073] In practical applications, we need to solve a formula that satisfies The specific calculation process of the target planning path is as follows:

[0074] Step 1: The conjunction of the constraint APTL formula and the task target APTL formula, convert the APTL formula that has not been converted into normal form Marking, that is The formula Transformed into paradigmatic form in in, and is the state formula, that is, the formula satisfied by a single state; ε indicates that the state is the terminal state of the system and there is no successor state; is the newly generated APTL formula and is marked as 0, that is, Let i=0.

[0075] Step 2: Let i increase by 1. If the formula is in the form of but This process calculates the initial state set of the system, satisfying the state formula The Boolean characteristic equation of the intersection of the set of initial and final states of the system. If ss! = 0, it means that the system has both the initial state and the final state and satisfies the formula The Boolean characteristic function of these states is ss, and ss is saved in P[i]. P is a linked list of the characteristic functions corresponding to the path state set, and then go to step 3; if ss==0, that is, there is no state that is both the initial state and the terminal state of the system and satisfies the formula Then proceed to step 2. If the formula is in the form For the new sub-formula generated in the process of transformation paradigm There are some formulas in Indicates that the formula has not been transformed into a normal form, that is, it is a new APTL formula generated during the transformation process. Other formulas Indicates that the formula has been converted to normal form. For all formulas make

[0076] in, l(X, R) calculates the set of successor states of X on the temporal transition relation R. If ss! = 0, save ss in P[i], end the calculation and go to step 3, otherwise, continue to step 2.

[0077] Among them, the collection contains all paths with at least one starting node satisfying the formula status, For state collection The Boolean characteristic function of Pre is defined as Among them, A is the robot set, G1 is the subset of the system global state, t' is the state transition relationship of the system, P A is the strategy of the robot set A. The function Pre seeks the set of predecessor states of the states in the global state set G1 on the transition relationship t'. is the Boolean characteristic function of the initial state set of the multi-agent model, It is the Boolean characteristic equation of the temporal state transition relationship of the state in the multi-agent model.

[0078] Step 3: If an executable path is stored in P, then the path is output. This path completes the task goal and satisfies the domain constraints. If no executable path is stored in P, then a path that meets the requirements cannot be planned.

[0079] Figure 3 A schematic diagram of the framework of the path planning method provided in the embodiments of this specification.

[0080] Specifically, the information of the positioning system, network, wheel speed sensor, perception system, etc. is input into the multi-agent model, the multi-agent model is described using the interpretation system programming language, the multi-agent model is converted into an interpretation system, and the system symbolization part of the multi-agent system model detection tool MCMAS is used to symbolically represent the interpretation system as an ordered binary decision diagram.

[0081] The constraints and task objectives are described using the APTL formula. The planned path needs to satisfy the conjunction of the constraint APTL formula and the task objective APTL formula, and the conjunction is converted into a normal form.

[0082] Calculate the planning path that satisfies the paradigm on the ordered binary decision graph and output the target planning path.

[0083] Figure 4 A schematic diagram of the structure of a path planning device proposed in an embodiment of this specification.

[0084] Corresponding to the aforementioned method embodiment, the path planning device recorded in the embodiment of this specification may include:

[0085] A first acquisition module 402 is used to acquire an ordered binary decision graph corresponding to the robot based on the path planning instruction; the ordered binary decision graph is used to represent all planned routes in the target scene where the robot is located;

[0086] The second acquisition module 404 is used to acquire the constraint conditions and task objectives corresponding to the target scene;

[0087] A determination module 406 is used to determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives;

[0088] The driving module 408 is used to drive based on the target planned path.

[0089] In the embodiments of this specification, by determining a target planning path in an ordered binary decision graph that satisfies both constraints and mission objectives, the system search space can be greatly reduced, the efficiency of robot path planning can be improved, and the robot can achieve preset mission objectives accurately and efficiently.

[0090] Based on the same idea as the aforementioned method embodiment, the embodiments of this specification also provide a device corresponding to the aforementioned method.

[0091] Figure 5 This is a schematic diagram of the structure of a path planning device provided in an embodiment of this specification. Figure 5 As shown, a path planning device provided in an embodiment of the present specification includes a memory 530, a processor 510, and a computer program 520 stored in the memory, and the processor 510 executes the computer program 520 to implement the path planning method described in any of the above embodiments.

[0092] A path planning device provided in an embodiment of the present specification may include a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the path planning method described in any of the above embodiments.

[0093] An embodiment of the present specification provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the path planning method described in any of the above embodiments can be implemented.

[0094] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. Figure 5 As for the device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0095] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0096] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0097] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0098] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0099] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0104] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0105] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0107] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0108] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0109] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A path planning method, characterized in that: Applied to a robot, the method comprises: Acquire an ordered binary decision graph corresponding to the robot based on the path planning instruction; the ordered binary decision graph is used to represent all planned routes in the target scene where the robot is located; Obtaining constraints and task objectives corresponding to the target scenario; Determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives; The driving path is planned based on the target.

2. The method according to claim 1, characterized in that Before obtaining the ordered binary decision graph corresponding to the robot based on the path planning instruction, the method includes: Get a multi-agent model; The multi-agent model is converted into an explanation system using a programming language, and the explanation system is symbolized into the ordered binary decision diagram.

3. The method according to claim 1, characterized in that After obtaining the constraint conditions and task objectives corresponding to the target scene, the method includes: The constraint condition is described by using the APTL formula to obtain the constraint condition APTL formula; Using the APTL formula to describe the task objective, obtaining the task objective APTL formula; The conjunction of the constraint condition APTL formula and the task target APTL formula is converted into a normal form.

4. The method according to claim 3, characterized in that Determining the target planning path in the ordered binary decision graph that satisfies both the constraint condition and the task goal specifically includes: Calculating a planning path satisfying the paradigm on the ordered binary decision graph; The output planning path is used as the target planning path.

5. The method according to any one of claims 1 to 4, characterized in that: The target scene is a football match, the constraint condition is that the robot does not commit any foul behavior in the football match rules, and the task goal is that the team the robot belongs to scores a goal.

6. A path planning device, characterized in that: Applications in robots include: A first acquisition module is used to acquire an ordered binary decision graph corresponding to the robot based on a path planning instruction; the ordered binary decision graph is used to represent all planned routes in a target scene where the robot is located; A second acquisition module is used to acquire the constraint conditions and task objectives corresponding to the target scene; A determination module, used to determine a target planning path in the ordered binary decision graph that satisfies both the constraint conditions and the task objectives; A driving module is used to drive based on the target planning path.

7. The device according to claim 6, characterized in that Before obtaining the ordered binary decision graph corresponding to the robot based on the path planning instruction, the method includes: Get a multi-agent model; The multi-agent model is converted into an explanation system using a programming language, and the explanation system is symbolized into the ordered binary decision diagram.

8. The device according to claim 6, characterized in that After obtaining the constraint conditions and task objectives corresponding to the target scene, the method includes: The constraint condition is described by using the APTL formula to obtain the constraint condition APTL formula; Using the APTL formula to describe the task objective, obtaining the task objective APTL formula; The conjunction of the constraint condition APTL formula and the task target APTL formula is converted into a normal form.

9. A path planning device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images