Robot path planning method based on improved artificial potential field and related equipment

Abstract

The invention discloses a robot path planning method and related equipment based on an improved artificial potential field, and the method comprises the steps: building a rectangular coordinate system model, and determining a robot coordinate, an obstacle coordinate and a target point coordinate; planning parameters of an artificial potential field method are initialized, the planning parameters comprise the influence range of at least one obstacle and a target point on the robot, the gravitation coefficient and the repulsive force coefficient related to robot movement and the running speed of the robot, and the number of the obstacles is at least one; a gravitation function and a repulsive force function are constructed based on an artificial potential field method, in the repulsive force function, the magnitude of repulsive force is in direct proportion to the distance between the robot and the target point, and in the gravitation function, when the robot is located outside the influence range of the target point on the robot, the magnitude of gravitation is in inverse proportion to the influence range of the target point on the robot; and calculating resultant force of the robot based on the gravitation function, the repulsive force function and the planning parameters and controlling the robot to move. And the problems of overlarge gravitation and unreachable target in the initial stage are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot path planning based on improved artificial potential field, and in particular to a robot path planning method, device, electronic equipment and storage medium based on improved artificial potential field. Background Art

[0002] With the advancement of intelligent operation and maintenance of substations, inspection robots are gradually being used in the daily work of substations. Inspection robots can complete tasks such as reading instrument values ​​and infrared thermal imaging at various inspection points of substations, and accurately obtain operating data and equipment status. Compared with traditional inspection methods, inspection robots are highly automated and accurate, which can greatly improve the efficiency and accuracy of inspection work.

[0003] In mobile robot technology, path planning is the most basic, and good path planning ability is the prerequisite for mobile robots to complete various tasks. Path planning is a mobile robot finding a better path from the current position to the target point through various algorithms. Compared with other algorithms, the artificial potential field method has simple logic, small calculation amount, high efficiency, and easy to understand, and has been widely used in path planning. The basic idea of ​​the artificial potential field method is to construct a virtual force field in the workspace, and the robot moves along the negative gradient direction of the potential field to avoid obstacles and move toward the target point, but the traditional potential field method has the problem of excessive gravity and unreachable target. Summary of the invention

[0004] The present invention provides a path planning method for an inspection robot based on an improved artificial potential field, so as to solve the problems of excessive gravity and unreachable targets existing in a traditional potential field method.

[0005] In a first aspect, the present invention provides a path planning method for an inspection robot based on an improved artificial potential field, comprising:

[0006] Establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates;

[0007] Initializing planning parameters of the artificial potential field method, wherein the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot;

[0008] Based on the artificial potential field method, an attraction function and a repulsion function are constructed. In the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point. In the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot.

[0009] Calculating the total gravitational force and the total repulsive force exerted on the robot based on the gravitational function, the repulsive function and the planning parameters respectively;

[0010] The resultant force is calculated based on the total attraction and repulsion, and the robot is controlled to move according to the resultant force.

[0011] In a second aspect, the present invention provides a robot path planning device based on an improved artificial potential field, comprising:

[0012] The coordinate system establishment module is used to establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates;

[0013] A planning parameter initialization module is used to initialize the planning parameters of the artificial potential field method, wherein the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot;

[0014] A function construction module, used to construct an attraction function and a repulsion function based on an artificial potential field method, wherein in the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point, and in the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot;

[0015] A force calculation module, used for calculating the total gravitational force and the total repulsive force on the robot based on the gravitational function, the repulsive function and the planning parameters respectively;

[0016] The robot control module is used to calculate the resultant force based on the total attraction and the total repulsion, and control the movement of the robot according to the resultant force.

[0017] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the robot path planning method based on the improved artificial potential field described in the first aspect of the present invention.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a processor to implement the robot path planning method based on the improved artificial potential field described in the first aspect of the present invention when executed.

[0022] The embodiment of the present invention provides a robot path planning method based on an improved artificial potential field, which establishes a rectangular coordinate system model, determines the robot coordinates, obstacle coordinates and target point coordinates; initializes the planning parameters of the artificial potential field method, the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot, and the number of obstacles is at least one; constructs a gravitational function and a repulsive function based on the artificial potential field method, in which the magnitude of the repulsive force is proportional to the distance between the robot and the target point, and in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot; calculates the total gravitational force and the total repulsive force on the robot based on the gravitational function, the repulsive function and the planning parameters respectively; calculates the resultant force based on the total gravitational force and the total repulsive force, and controls the movement of the robot according to the resultant force.

[0023] On the one hand, in the initial stage, the robot is far away from the target point, and the gravitational force of the target point on the robot is usually large at this time. Therefore, this scheme proposes that in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot. The influence range of the target point on the robot is used to limit the potential energy growth rate in the initial stage, thereby reducing the gravitational growth rate; the problem of excessive gravity in the initial stage is solved; on the other hand, in the repulsion function, the magnitude of the repulsive force is proportional to the distance between the robot and the target point. When the robot approaches the target point, the repulsive force will become smaller and smaller, thereby ensuring that the potential energy at the target point is the global minimum, thereby solving the problem of unreachable targets.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 is a flow chart of a robot path planning method based on an improved artificial potential field provided in Embodiment 1 of the present invention;

[0027] Figure 2 is a schematic diagram of a rectangular coordinate system model provided in the first embodiment of the present invention;

[0028] Figure 3is a schematic structural diagram of a robot path planning device based on an improved artificial potential field provided in Embodiment 2 of the present invention;

[0029] Figure 4 It is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] Embodiment 1

[0032] Figure 1 This is a flowchart of a robot path planning method based on an improved artificial potential field provided in the first embodiment of the present invention. This embodiment is applicable to the case of robot path planning based on an improved artificial potential field. The method can be executed by a robot path planning device based on an improved artificial potential field. The robot path planning device based on an improved artificial potential field can be implemented in the form of hardware and / or software. The robot path planning device based on an improved artificial potential field can be configured in an electronic device. Figure 1 As shown, the robot path planning method based on the improved artificial potential field includes:

[0033] S101, establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates.

[0034] Since the robot, obstacles and target points can all be considered to move on a plane, a rectangular coordinate system model can be established to analyze the positional relationship and the relationship between gravity and repulsion among the three. It should be noted that in this step, the robot coordinates, obstacle coordinates and target point coordinates are all coordinates at the current time point, and the coordinates can change as the target object moves. Taking the robot as an example, the robot is continuously moving, so the robot coordinates change in real time. Obstacles and target points are usually fixed, but the situation of obstacles and target points moving is not ruled out. The basic idea of ​​using the artificial potential field method for path planning is to assume that the robot's motion environment is a virtual force field. The robot is affected by the repulsive force of the obstacle and the gravitational force of the target point, and moves under the combined force of the two. Figure 2 is a schematic diagram of a rectangular coordinate system model, such as Figure 1As shown in the figure, the robot motion environment is constructed by establishing a rectangular coordinate system. It is assumed that the robot is a point mass, the obstacle is expanded into a circle, the target point is represented by a triangle, and the robot is subjected to force.

[0035] S102, initializing planning parameters of the artificial potential field method.

[0036] The planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the robot's movement, and the robot's driving speed.

[0037] The number of obstacles is at least one. The influence range of an obstacle on the robot means that the robot will be affected by the repulsive force of the obstacle only when it enters the influence range of the obstacle. The influence range of a target point on the robot means that the robot will be affected by the distance between the target point and the robot only when it is outside the influence range of the target point.

[0038] S103. Construct an attraction function and a repulsion function based on the artificial potential field method. In the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point. In the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot.

[0039] Constructing the gravitational function and the repulsive function based on the artificial potential field method specifically includes: constructing the gravitational potential energy function and the repulsive potential energy function based on the artificial potential field method; calculating the negative gradient of the gravitational potential energy function to obtain the gravitational function; calculating the negative gradient of the repulsive potential energy function to obtain the repulsive function.

[0040] The gravitational or repulsive function is the negative gradient of the corresponding potential energy function. Therefore, the gravitational or repulsive function can be obtained by taking the negative gradient of the corresponding potential energy function.

[0041] In the gravitational function and repulsive function constructed based on the artificial potential field method in the prior art, the gravitational force of the target point on the robot is proportional to the distance between the target point and the robot. In the initial stage, the robot is far away from the target point (at this time, the robot is usually outside the influence range of the target point on the robot), and the gravitational force of the target point on the robot is too large, which will make the repulsive force of the obstacle on the robot too small. In this case, the robot may drive into the obstacle or collide with the obstacle. Therefore, in the present scheme, it is set that in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot. By introducing the influence range of the target point on the robot in the gravitational function, the problem of excessive initial gravitation is solved, thereby reducing the potential energy growth rate, that is, reducing the gravitational force of the target point on the robot in the initial stage, avoiding the robot from driving into the obstacle or colliding with the obstacle, and planning the route more reasonably.

[0042] In addition, in the repulsion function, the magnitude of the repulsive force is proportional to the distance between the robot and the target point. Therefore, the closer the robot is to the target point, the smaller the repulsive force of the obstacle is. This ensures that the repulsive force received by the robot at the target point is the global minimum, thereby solving the problem of the unreachable target point.

[0043] S104, respectively calculating the total gravitational force and the total repulsive force acting on the robot based on the gravitational function, the repulsive function and the planning parameters.

[0044] After constructing the gravity function and repulsion function, the planning parameters and the measured (calculated) data are substituted into the gravity function and the repulsion function to obtain the total gravity and total repulsion of the robot.

[0045] The total attraction and the total repulsion are both vectors with both magnitude and direction.

[0046] S105. Calculate the resultant force based on the total attraction and the total repulsion, and control the movement of the robot according to the resultant force.

[0047] like Figure 2 As shown, the resultant force is F toal , which is the total gravitational force F att and the total repulsive force F rep After obtaining the resultant force, the robot is controlled to move according to the magnitude and direction of the resultant force.

[0048] Optionally, the number of obstacles is at least one, and the expression of the resultant force is as follows:

[0049] Among them, F toal is the resultant force, F att is the total gravitational force, F rep,i is the repulsive force of the ith obstacle, and m is the number of obstacles.

[0050] It should be noted that the above S101-S105 are control methods for the robot at a time point or time period. The robot can calculate and update the resultant force in real time, and control its own movement based on the resultant force until the robot moves to the target point.

[0051] The embodiment of the present invention provides a robot path planning method based on an improved artificial potential field, which establishes a rectangular coordinate system model, determines the robot coordinates, obstacle coordinates and target point coordinates; initializes the planning parameters of the artificial potential field method, the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot, and the number of obstacles is at least one; constructs a gravitational function and a repulsive function based on the artificial potential field method, in which the magnitude of the repulsive force is proportional to the distance between the robot and the target point, and in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot; calculates the total gravitational force and the total repulsive force on the robot based on the gravitational function, the repulsive function and the planning parameters respectively; calculates the resultant force based on the total gravitational force and the total repulsive force, and controls the movement of the robot according to the resultant force.

[0052] On the one hand, in the initial stage, the robot is far away from the target point. At this time, the gravitational force of the target point on the robot is usually large. Therefore, this scheme proposes that in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot. The influence range of the target point on the robot is used to limit the potential energy growth rate in the initial stage, thereby reducing the gravitational growth rate; the problem of excessive gravity in the initial stage is solved; on the other hand, in the repulsion function, the magnitude of the repulsive force is proportional to the distance between the robot and the target point. When the robot approaches the target point, the repulsive force will become smaller and smaller, thereby ensuring that the potential energy at the target point is the global minimum, thereby solving the problem of unreachable targets.

[0053] In an optional embodiment, the expression of the gravitational potential energy function is:

[0054] U att =U attX +U attV ;

[0055] Among them, U att is the total gravitational potential energy, U attX is the gravitational potential energy of the robot relative to the target point, U attV is the kinetic potential energy when the robot moves, X is the robot coordinate, and V is the moving speed of the robot relative to the target point. In this embodiment, considering that the robot may do non-uniform motion, there will also be corresponding kinetic potential energy. On the basis of the traditional artificial potential field method, the consideration of kinetic potential energy is added, and the gravitational potential energy is composed of the gravitational force and kinetic potential energy of the robot. It should be noted that V here is the relative speed. The target point is mostly fixed, but it is not ruled out that there may be short-distance movement in a few cases, so the moving speed of the robot relative to the target point is set.

[0056] Specifically, based on the above embodiment, in the gravitational potential energy function:

[0057]

[0058] Among them, k attX is the gravitational coefficient of the target point, k attV is the robot's velocity gravitational coefficient, X t is the target point coordinate, ρ(X,X t ) is the distance between the robot and the target point, ρ x is the influence range of the target point on the robot.

[0059] It should be noted here that in physics, two different forms of energy cannot be added directly. Therefore, in order to achieve their direct addition to represent the total energy of the object, coefficients are introduced. At the same time, in order to balance the proportion of repulsive potential energy and dynamic potential energy on the robot's trajectory, they (coefficients) also represent the weights of the two potential energies.

[0060] Based on the above formula, it can be known that when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot. In the initial stage, the robot is far away from the target point, and the gravitational force of the target point on the robot is usually large. Therefore, this scheme proposes that in the gravitational function, when the robot is outside the influence range of the target point on the robot, the magnitude of the gravitational force is inversely proportional to the influence range of the target point on the robot. The influence range of the target point on the robot is used to limit the potential energy growth rate in the initial stage, thereby reducing the gravitational growth rate; the problem of excessive gravitation in the initial stage is solved.

[0061] It is known that the gravitational function is the negative gradient of the gravitational potential energy function, specifically:

[0062] F att = -grad(U att );

[0063] Among them, F att is gravity, U att Gravitational potential energy function, -grad(.) means to find the negative gradient.

[0064] In an optional embodiment, the expression of the repulsive potential energy function is:

[0065]

[0066] Among them, U rep is the repulsive potential energy, k rep is the repulsion coefficient, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, ρ(X,Xo ) is the distance between the robot and the obstacle; ρ(X,X t ) is the distance between the robot and the target point, ρ o is the range of influence of the obstacle on the robot, and n is a random number. Specifically, n is a random number not less than 1.

[0067] In the repulsive force function, the magnitude of the repulsive force is proportional to the distance between the robot and the target point. When the robot approaches the target point, the repulsive force will become smaller and smaller, thereby ensuring that the potential energy at the target point is the global minimum, thereby solving the problem of unreachable targets. In the repulsive potential energy function formula of this embodiment, by introducing the distance function between the robot and the target point into the repulsive potential field function, the repulsive force is affected by the distance between the robot and the target point. When the robot is closer to the target point, ρ x (X,X t ) is smaller, the repulsive force will be smaller, thereby ensuring that the repulsive potential energy at the target point is the global minimum, thus solving the problem of unreachable target.

[0068] In addition, in the prior art gravitational function and repulsive function constructed based on the artificial potential field method, if there are multiple obstacles, when multiple small obstacles gather (or large obstacles block) between the robot and the target object, the robot can easily fall into the local minimum point. Therefore, this embodiment sets n to a random number, which can help to solve the problem of smoothly jumping out of iteration when falling into the local optimal solution, thereby helping to solve the problem of unreachable targets. The robot can escape from the minimum point, bypass obstacles and move toward the target point, solving the problem of the robot not being able to find a path and saving planning time.

[0069] The repulsion function is the negative gradient of the repulsive potential energy function, so the repulsion function:

[0070] Chain derivation of this formula yields:

[0071] Among them, F rep (X) is the total repulsive force, U rep (X) is the repulsive potential energy function, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, F rep1 (X) is the repulsive force component, the direction of the repulsive force component is from the obstacle to the robot, F rep2 (X) is the gravitational component, the direction of the gravitational component is from the robot to the target point, k rep is the repulsion coefficient, and n is a random number.

[0072] After derivation, U rep (X) can be seen to be composed of two parts (F rep1 and Frep2 ), where F rep1 is the value of ρ(X,X o ) is obtained by taking the derivative, which is the repulsive force function in general, and Frep2 is the repulsive force function of ρ X (X,X t The derivative is obtained, which is the gravitational function in general. This scheme improves both the gravitational function and the repulsive function.

[0073] In general, the robot path planning based on the improved artificial potential field includes the following steps:

[0074] Step 1: Establish a rectangular coordinate system model to determine the robot's initial coordinates, obstacle coordinates, and target point coordinates;

[0075] Step 2: Initialize the robot's driving speed, the influence range of obstacles and target points, the gravitational coefficient and the repulsive coefficient;

[0076] Step 3: Construct attraction function and repulsion function;

[0077] Step 4: Iterate all obstacles, calculate the resultant force, and the robot moves according to the direction of the resultant force;

[0078] Step 5: Update the random number n;

[0079] Step 6: Repeat steps 4 and 5 to dynamically update the resultant force.

[0080] By introducing the distance function between the robot and the target point into the repulsive potential field function, the repulsive force is affected by the distance between the robot and the target point. The closer the robot is to the target point, the smaller the repulsive force will be. This ensures that the repulsive potential energy at the target point is the global minimum, thereby solving the problem of unreachable targets. Setting n to a random number can help to successfully exit the iteration when stuck in a local optimal solution, thereby helping to solve the problem of unreachable targets. The purpose of adding the influence range of the target point on the robot to the formula of the gravitational potential energy function is to solve the problem of excessive initial gravity, thereby reducing the rate of potential energy growth.

[0081] Embodiment 2

[0082] Figure 3 The schematic diagram of the structure of a robot path planning device based on an improved artificial potential field provided in the second embodiment of the present invention. Figure 3 As shown, the robot path planning device based on the improved artificial potential field includes:

[0083] A coordinate system establishment module 301 is used to establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates;

[0084] A planning parameter initialization module 302 is used to initialize the planning parameters of the artificial potential field method, wherein the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot;

[0085] A function construction module 303 is used to construct an attraction function and a repulsion function based on an artificial potential field method. In the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point. In the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot.

[0086] A force calculation module 304, used to calculate the total gravitational force and the total repulsive force on the robot based on the gravitational function, the repulsive function and the planning parameters respectively;

[0087] The robot control module 305 is used to calculate the resultant force based on the total attraction and the total repulsion, and control the movement of the robot according to the resultant force.

[0088] Optionally, the function construction module 303 includes:

[0089] Potential energy function construction submodule, used to construct gravitational potential energy function and repulsive potential energy function based on artificial potential field method;

[0090] A gravitational function construction submodule is used to obtain a negative gradient of the gravitational potential energy function to obtain a gravitational function;

[0091] The repulsion function construction submodule is used to calculate the negative gradient of the repulsion potential energy function to obtain the repulsion function.

[0092] Optionally, the expression of the gravitational potential energy function is:

[0093] U att =U attX +U attV ;

[0094] Among them, U att is the total gravitational potential energy, U attX is the gravitational potential energy of the robot relative to the target point, U attV is the kinetic potential energy of the robot when it moves, X is the robot coordinate, and V is the moving speed of the robot relative to the target point.

[0095] Optional,

[0096]

[0097] Among them, k attX is the gravitational coefficient of the target point, k attV is the robot's velocity gravitational coefficient, X tis the target point coordinate, ρ(X,X t ) is the distance between the robot and the target point, ρ x is the influence range of the target point on the robot.

[0098] Optionally, the expression of the repulsive potential energy function is:

[0099]

[0100] U rep is the repulsive potential energy, k rep is the repulsion coefficient, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, ρ(X,X o ) is the distance between the robot and the obstacle; ρ(X,X t ) is the distance between the robot and the target point, ρ o is the impact range of the obstacle on the robot, and n is a random number.

[0101] Optionally, the expression of the repulsion function is:

[0102]

[0103] Among them, F rep (X) is the total repulsive force, U rep (X) is the repulsive potential energy function, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, F rep1 (X) is the repulsive force component, the direction of the repulsive force component is from the obstacle to the robot, F rep2 (X) is the gravitational component, the direction of the gravitational component is from the robot to the target point, k rep is the repulsion coefficient, and n is a random number.

[0104] Optionally, the number of obstacles is at least one, and the expression of the resultant force is as follows:

[0105] Among them, F toal is the resultant force, F att is the total gravitational force, F rep,i is the repulsive force of the ith obstacle, and m is the number of obstacles.

[0106] The robot path planning device based on improved artificial potential field provided in the embodiment of the present invention can execute the robot path planning method based on improved artificial potential field provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0107] Embodiment 3

[0108] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0109] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0110] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0111] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as a robot path planning method based on an improved artificial potential field.

[0112] In some embodiments, the robot path planning method based on the improved artificial potential field can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the robot path planning method based on the improved artificial potential field described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to execute the robot path planning method based on the improved artificial potential field by any other appropriate means (e.g., by means of firmware).

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0118] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robot path planning method based on improved artificial potential field, characterized in that: include: Establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates; Initializing planning parameters of the artificial potential field method, wherein the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot; Based on the artificial potential field method, an attraction function and a repulsion function are constructed. In the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point. In the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot. Calculating the total gravitational force and the total repulsive force exerted on the robot based on the gravitational function, the repulsive function and the planning parameters respectively; The resultant force is calculated based on the total attraction and repulsion, and the robot is controlled to move according to the resultant force.

2. The method according to claim 1, characterized in that The method of constructing the attraction function and the repulsion function based on the artificial potential field method includes: Construct gravitational potential energy function and repulsive potential energy function based on artificial potential field method; Calculating the negative gradient of the gravitational potential energy function to obtain a gravitational function; The negative gradient of the repulsive potential energy function is calculated to obtain a repulsive function.

3. The method according to claim 2, characterized in that The expression of the gravitational potential energy function is: IN att =U attX +U attV ; Among them, U att is the total gravitational potential energy, U attX is the gravitational potential energy of the robot relative to the target point, U attV is the kinetic potential energy of the robot when it moves, X is the robot coordinate, and V is the moving speed of the robot relative to the target point.

4. The method according to claim 3, characterized in that: Among them, k attX is the gravitational coefficient of the target point, k attV is the robot's velocity gravitational coefficient, X t is the target point coordinate, ρ(X,X t ) is the distance between the robot and the target point, ρ x is the influence range of the target point on the robot.

5. The method according to claim 2, characterized in that The expression of the repulsive potential energy function is: Among them, U rep is the repulsive potential energy, k rep is the repulsion coefficient, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, ρ(X,X o ) is the distance between the robot and the obstacle; ρ(X,X t ) is the distance between the robot and the target point, ρ o is the impact range of the obstacle on the robot, and n is a random number.

6. The method according to claim 5, characterized in that The expression of the repulsion function is: Among them, F rep (X) is the total repulsive force, U rep (X) is the repulsive potential energy function, X is the robot coordinate, X o is the obstacle coordinate, X t is the target point coordinate, F rep1 (X) is the repulsive force component, the direction of the repulsive force component is from the obstacle to the robot, F rep2 (X) is the gravitational component, the direction of the gravitational component is from the robot to the target point, k rep is the repulsion coefficient, and n is a random number.

7. The method according to any one of claims 1 to 6, characterized in that: The number of obstacles is at least one, and the expression of the resultant force is as follows: Among them, F toal is the resultant force, F att is the total gravitational force, F rep,i is the repulsive force of the ith obstacle, and m is the number of obstacles.

8. A robot path planning device based on an improved artificial potential field, characterized in that: include: The coordinate system establishment module is used to establish a rectangular coordinate system model to determine the robot coordinates, obstacle coordinates and target point coordinates; A planning parameter initialization module is used to initialize the planning parameters of the artificial potential field method, wherein the planning parameters include the influence range of obstacles and target points on the robot, the gravitational coefficient and repulsive coefficient related to the movement of the robot, and the driving speed of the robot; A function construction module, used to construct an attraction function and a repulsion function based on an artificial potential field method, wherein in the repulsion function, the magnitude of the repulsion is proportional to the distance between the robot and the target point, and in the attraction function, when the robot is outside the influence range of the target point on the robot, the magnitude of the attraction is inversely proportional to the influence range of the target point on the robot; A force calculation module, used for calculating the total gravitational force and the total repulsive force on the robot based on the gravitational function, the repulsive function and the planning parameters respectively; The robot control module is used to calculate the resultant force based on the total attraction and the total repulsion, and control the movement of the robot according to the resultant force.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the robot path planning method based on the improved artificial potential field described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot path planning method based on an improved artificial potential field as described in any one of claims 1 to 7 when executed.

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