Intelligent device control method, apparatus, medium, product, and control device

Abstract

The application relates to an intelligent device control method and device, a medium, a product and a control device. The target mass of a load object of an intelligent device is acquired; the change limit information of the target motion parameter of the intelligent device is determined according to the target mass; and the intelligent device load object is controlled to run according to the target motion parameter and the change limit information. In this way, the change limit condition of the target motion parameter of the intelligent device is determined by combining the mass of the load object of the intelligent device, so that when the intelligent device load object is controlled to run, the change limit condition of the target motion parameter of the intelligent device can be dynamically adjusted according to the different masses of the load objects, thereby improving the stability when the intelligent device runs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent device control, and particularly relates to an intelligent device control method and device, a medium, a product and a control device. BACKGROUND

[0002] The existing intelligent device control method usually pre-plans a path, and controls the intelligent device to run according to the pre-planned path.

[0003] However, due to various complex situations in the real scene in which the intelligent device loads an object to move, there are problems such as dropping of the object during the process in which the intelligent device loads the object to move, which leads to poor stability of the intelligent device. SUMMARY

[0004] The embodiments of the present application provide an intelligent device control method and device, a storage medium, a computer program product and a control device, which determine a change limit condition of a target motion parameter of an intelligent device by combining the mass of an object loaded by the intelligent device, so that when the intelligent device loads the object to run, the change limit condition of the target motion parameter of the intelligent device can be dynamically adjusted according to the different masses of the objects loaded by the intelligent device, so as to improve the stability when the intelligent device runs.

[0005] The embodiments of the present application provide an intelligent device control method, which comprises the following steps.

[0006] Obtaining a target mass of an object required to be loaded by an intelligent device;

[0007] Determining change limit information of a target motion parameter of the intelligent device according to the target mass;

[0008] Controlling the intelligent device to load the object to run according to the target motion parameter and the change limit information.

[0009] Correspondingly, the embodiments of the present application provide an intelligent device control device, which comprises the following.

[0010] A target mass obtaining unit, configured to obtain a target mass of an object required to be loaded by an intelligent device;

[0011] A change limit information determining unit, configured to determine change limit information of a target motion parameter of the intelligent device according to the target mass;

[0012] A control unit, configured to control the intelligent device to load the object to run according to the target motion parameter and the change limit information.

[0013] In addition, the embodiment of the present application further provides a storage medium, the storage medium stores a computer program, and the computer program is used to make the smart device processor execute any one of the smart device control methods provided by the embodiment of the present application when the computer program runs on the smart device processor.

[0014] In addition, the embodiment of the present application further provides a computer program product, including a computer program or instructions, and the computer program or instructions are executed by a processor to realize any one of the smart device control methods provided by the embodiment of the present application.

[0015] In addition, the embodiment of the present application further provides a control device, including one or more processors and a memory, the memory stores a computer program, and the processor is used to run the computer program in the memory to realize the smart device control method provided by the embodiment of the present application.

[0016] In the embodiment of the present application, the target mass of the load object of the smart device is acquired, the change limit information of the target motion parameter of the smart device is determined according to the target mass, and the smart device load object is controlled to run according to the target motion parameter and the change limit information. Therefore, by combining the mass of the object loaded by the smart device, the change limit condition of the target motion parameter of the smart device is determined, so that when the smart device load object is controlled to run, the change limit condition of the target motion parameter of the smart device can be dynamically adjusted according to the mass of the object loaded by the smart device, so as to improve the stability when the smart device is controlled to run. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is an implementation environment scene schematic diagram of the smart device control method provided in the embodiment of the present application;

[0019] Figure 2 is a flowchart of the smart device control method provided by an embodiment of the present application;

[0020] Figure 3 is a scene schematic diagram of the smart device control method provided by an embodiment of the present application;

[0021] Figure 4 is another scene schematic diagram of the smart device control method provided by an embodiment of the present application;

[0022] Figure 5 is a scene diagram of a scenario in which local path planning needs to be performed in the intelligent device control method provided by an embodiment of the present application;

[0023] Figure 6 is a diagram of running state information of a position point of a target local path in the intelligent device control method provided by an embodiment of the present application;

[0024] Figure 7 is a scene diagram of local path planning in the intelligent device control method provided by an embodiment of the present application;

[0025] Figure 8 is a flow diagram of a target local path in the intelligent device control method provided by an embodiment of the present application;

[0026] Figure 9 is a structural diagram of the intelligent device control apparatus provided by an embodiment of the present application;

[0027] Figure 10 is a structural diagram of the control apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, any other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0029] In addition, “multiple” in the embodiments of the present application refers to two or more than two. “First” and “second” and the like in the embodiments of the present application are used for distinguishing description, and cannot be understood as implying relative importance.

[0030] The embodiments of the present application provide an intelligent device control method, apparatus, storage medium, computer program product, and control apparatus. The intelligent device control apparatus can be integrated in a control apparatus, which can be a server or a terminal or the like.

[0031] The server can be a stand-alone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery network (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0032] The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the present application.

[0033] Please refer to Figure 1 , for example, the smart device control device is integrated in the control device, Figure 1 The implementation scenario of the smart device control method provided by the embodiment of the present application is shown in the figure, wherein the control device can be a terminal device, by acquiring the target mass of the load object required by the smart device; according to the target mass, the change limit information of the target motion parameter of the smart device is determined; according to the target motion parameter and the change limit information, the smart device load object is controlled to run.

[0034] It should be noted that Figure 1 The implementation environment scenario of the smart device control method shown is only an example, and the implementation environment scenario of the smart device control method described in the embodiment of the present application is to more clearly illustrate the technical scheme of the embodiment of the present application, and does not constitute a limitation on the technical scheme provided by the embodiment of the present application. Those skilled in the art can know that with the evolution of data processing and the emergence of new business scenarios, the technical scheme provided by the present application is also applicable to similar technical problems.

[0035] The scheme provided by the embodiment of the present application is specifically described by the following embodiments. It should be noted that the description order of the following embodiments is not limited as the preferred order of the embodiments.

[0036] This embodiment will be described from the perspective of the smart device control device, which can be integrated in the control device. The control device can be a terminal and / or a server, which is not limited in the present application.

[0037] Please refer to Figure 2 , Figure 2 The flowchart of the smart device control method provided by an embodiment of the present application is shown in the figure, which can include the following steps S101-S103:

[0038] S101, acquiring the target mass of the load object required by the smart device.

[0039] Among them, the smart device refers to the device that needs to load the object to run.

[0040] The specific type of smart device can be selected according to actual conditions, which is not limited in the embodiment of the present application.

[0041] In some optional embodiments, the intelligent device comprises at least one of an automated guided vehicle (AGV) and an intelligent mechanical device. The automated guided vehicle can comprise a submerged AGV, a towed AGV, a self-unloading AGV, a lifting AGV, a forklift AGV, etc. The intelligent mechanical device can comprise a robot arm, a robot, etc.

[0042] For example, the intelligent device can be an automated guided vehicle, such as a submerged AGV, a towed AGV, a self-unloading AGV, a lifting AGV, a forklift AGV, etc. The submerged AGV refers to an AGV that penetrates into the bottom of a material vehicle and realizes material delivery and recovery operations through automatic coupling and decoupling mechanisms. The towed AGV refers to an AGV that can automatically lift materials on a platform and can operate synchronously with a production line. Other specific functions of the AGV can be referred to existing AGVs, which will not be described herein. For another example, the intelligent device can also be an intelligent mechanical device, such as a robot arm, a robot, etc.

[0043] The specific type of the object carried by the intelligent device can be selected according to actual conditions, and embodiments of the present application are not limited. For example, the object carried by the intelligent device is a shelf, or the object carried by the intelligent device is a shelf and goods placed on the shelf, or the object carried by the intelligent device is a pallet, or the object carried by the intelligent device is a container.

[0044] The specific content of the target quality can be selected according to actual conditions, and embodiments of the present application are not limited. For example, the target quality can be the actual mass of the object carried by the intelligent device, or the target quality can be the theoretical mass of the object carried by the intelligent device.

[0045] S102, determining change limit information of a target motion parameter of the intelligent device according to the target quality.

[0046] The target motion parameter refers to a motion parameter of the intelligent device that can be limited in change based on the mass. The target motion parameter can be the acceleration of the intelligent device, or the target motion parameter can be the speed of the intelligent device.

[0047] The change limit information refers to a condition for limiting the target motion parameter. The specific content of the change limit information is adjusted according to the target quality.

[0048] S103, controlling the intelligent device to carry the object according to the target motion parameter and the change limit information.

[0049] Therefore, in the intelligent device control method provided in the embodiments of the present application, the target mass of the load object of the intelligent device is acquired, the change limit information of the target motion parameter of the intelligent device is determined according to the target mass, and the intelligent device load object is controlled to run according to the target motion parameter and the change limit information. In this way, by combining the mass of the object loaded by the intelligent device, the change limit condition of the target motion parameter of the intelligent device is determined, so that when the intelligent device load object is controlled to run, the change limit condition of the target motion parameter of the intelligent device can be dynamically adjusted according to the mass of the object loaded by the intelligent device, so as to improve the stability when the intelligent device is controlled to run.

[0050] In some optional embodiments, the process of determining the change limit information of the target motion parameter of the intelligent device according to the target mass can include: determining the maximum acceleration of the intelligent device when the intelligent device meets the stable running condition according to the target mass; and determining the change limit information of the target motion parameter of the intelligent device according to the maximum acceleration.

[0051] The stable running condition refers to a condition that the intelligent device is controlled to run the object of the target mass without the object falling due to too high speed or other reasons.

[0052] The stable running condition includes that the resultant force on the object is not greater than the sliding friction force on the object.

[0053] Specifically, the stable running condition can be represented by the following formula (1):

[0054] f >= F (1)

[0055] Wherein, f refers to the sliding friction force on the object, and F refers to the resultant force on the object.

[0056] Based on this, in some optional embodiments, the process of determining the maximum acceleration of the intelligent device when the intelligent device meets the stable running condition according to the target mass can include: determining the resultant force on the object according to the acceleration of the intelligent device when the intelligent device loads the object and the target mass; determining the sliding friction force on the object according to the pressure on the object and the dynamic friction coefficient; and determining the acceleration when the resultant force on the object is equal to the sliding friction force on the object, and taking the acceleration as the maximum acceleration of the intelligent device when the intelligent device meets the stable running condition.

[0057] Specifically, the resultant force on the object can be determined according to the following formula (2):

[0058] F = ma (2)

[0059] Wherein, F refers to the resultant force of the object, m refers to the target mass, and a refers to the acceleration of the smart device when the smart device is loaded with the object.

[0060] Further, the sliding friction force of the object is determined according to the following formula (3):

[0061] f = μN (3)

[0062] Wherein, f refers to the sliding friction force of the object, μ refers to the dynamic friction factor, and N refers to the pressure of the object.

[0063] In combination with the formula (1) to the formula (3), the formula (4) can be derived, and the above stable running condition is expressed according to the following formula (4):

[0064] μN >= ma (4)

[0065] It should be noted that when in a horizontal condition, the pressure of the object can be expressed according to the following formula (5):

[0066] N = mg (5)

[0067] Wherein, N refers to the pressure of the object, m refers to the target mass, and g refers to the gravitational acceleration.

[0068] Therefore, in the case of known dynamic friction factor and gravitational acceleration, only the mass of the object loaded by the smart device and the acceleration of the smart device when running need to be obtained, and then the stable motion state of the smart device when loaded with the object can be determined.

[0069] In some optional embodiments, the process of determining the sliding friction force of the object according to the pressure of the object and the dynamic friction factor can include: obtaining an influencing factor of the pressure of the object and an influence coefficient of the influencing factor; determining the actual pressure of the object based on the influencing factor, the influence coefficient of the influencing factor, the target mass, and the gravitational acceleration; and determining the sliding friction force of the object based on the actual pressure of the object and the dynamic friction factor.

[0070] The pressure of the object under ideal horizontal conditions is shown in the formula (5). However, in reality, the pressure of the object is affected by many factors. Therefore, the actual pressure of the object can be combined with the influencing factor of the pressure of the object and the influence coefficient of the influencing factor.

[0071] Specifically, the actual pressure of the object can be expressed according to the following formula (6):

[0072] N 实际 = mg - ζe g (6)

[0073] Where N actually refers to the actual pressure exerted on the object, ζ refers to the influence coefficient, and e g This refers to influencing factors.

[0074] Taking the target mass as the actual mass as an example, combining the above formulas (4) and (6), formula (7) can be derived, and the maximum acceleration is expressed by the following formula (7):

[0075]

[0076] Among them, a max This refers to the maximum acceleration, m a This refers to the actual quality.

[0077] It should be noted that the actual mass can be expressed using the following formula (8):

[0078] m a =m+m e (8)

[0079] Where, m a m refers to the actual mass, and m refers to the theoretical mass. e This refers to the quality error value.

[0080] By adjusting the maximum acceleration of the smart device by loading different masses of objects onto it, the limiting conditions for the change of the smart device's target motion parameters can be determined. This allows the smart device to dynamically adjust the limiting conditions for the change of its target motion parameters according to the different masses of the objects it is loading, thereby improving the stability of the smart device's operation.

[0081] In some alternative embodiments, the target motion parameters mentioned above include the speed of the smart device.

[0082] Based on this, the process of controlling the intelligent device to carry the object according to the target motion parameters and the change restriction information may include: determining the second speed of the intelligent device at the next moment according to the first speed of the intelligent device at the current moment and the running state model; if the speed direction of the first speed and the speed direction of the second speed are different, then controlling the intelligent device to carry the object to run based on the running state model, the change restriction information, the first speed and the second speed.

[0083] Among them, the operating state model refers to the model determined based on the velocity curve and the maximum acceleration of the intelligent device under load, which is used to ensure that the intelligent device meets the stable operating conditions.

[0084] Wherein, the first speed and the second speed are both vectors. The different speed directions of the first speed and the second speed indicate the turning driving state of the intelligent device.

[0085] It should be noted that, in the turning driving state of the intelligent device, due to the change of the speed direction, the acceleration of the turning process in the direction before the turning may exceed the maximum acceleration configured for the intelligent device by the conventional method, thereby causing the situation that the intelligent device cannot run smoothly. For example, please refer to Figure 3 、 Figure 3 The figure of a speed of the intelligent device in the 90° turning driving state is shown in FIG. 1, wherein v1 is the speed of the intelligent device at the last time in the actual application process, and v2 is the speed of the intelligent device at the current time in the actual application process, wherein v1=v2, which makes the intelligent device run unstably in the turning driving state.

[0086] Since the system cannot directly obtain the acceleration, it is necessary to derive the acceleration and the speed component, in combination with Figure 4 The relationship between the acceleration and the speed v1 and the speed v2 can be expressed according to the following formula (9):

[0087] v2sinθ>=v1-0.06a max

[0088] v2cosθ<0.06a max

[0089] v2<v max (9)

[0090] Wherein, θ is the included angle between the speed v1 and the speed v2, and 0.06 represents a constant, which can be determined according to the running period configured for the intelligent device. v max is the maximum speed, and a max is the maximum acceleration.

[0091] The specific derivation process of the above formula (9) can be referred to the following formula (10):

[0092]

[0093] v 2y =v2sinθ

[0094] V 2x =V2cosθ

[0095]

[0096] It should be noted that the running state model is configured with at least one corresponding relationship between the speed and the time, and the change limit information.

[0097] Specifically, the at least one correspondence between the speed and the time configured by the running state model can be represented as follows:

[0098]

[0099] It should be noted that in the formula (11), v0-v6 are used to represent the initial speed in different time intervals, and v represents the terminal speed determined by the initial speed in different time intervals and the correspondence between the speed and the time in the time interval.

[0100] It should be noted that in order to ensure the efficiency and safety of the intelligent device during the execution of the task, the at least one correspondence between the speed and the time configured by the running state model can be determined in advance.

[0101] Specifically, the formula (11) can be derived as follows:

[0102] Based on the speed curve, the time intervals of the intelligent device in different speed states during running are determined: the speed in the start interval [t0-t1], the speed in the acceleration interval [t1-t2], the speed approaching the stable interval [t2-t3], the speed in the stable interval [t3-t4], the speed from the stable state to the deceleration interval [t4-t5], the speed in the deceleration interval [t5-t6], and the speed in the termination interval [t6-t7];

[0103] The acceleration in each time interval is determined.

[0104] Based on the time interval and the acceleration, the correspondence between the speed and the time is determined.

[0105] Specifically, the acceleration of the above-mentioned seven intervals of the intelligent device during running can be determined as follows:

[0106]

[0107] Where a is the determined running acceleration of the intelligent device at a time. max is the maximum acceleration, and J is the jerk, i.e. the rate of change of acceleration.

[0108] Specifically, the correspondence between the speed and the time can be derived as follows:

[0109] The relationship between the initial acceleration a0 and the terminal acceleration a t in a unit of time can be represented as follows:

[0110] a t = Jt + a0 (13)

[0111] In combination with equation (13), the average acceleration per unit time can be expressed as equation (14) as follows:

[0112]

[0113] In combination with equation (13) to equation (14), and according to the relationship between acceleration, speed and time, the relationship between speed and time can be derived as equation (15) as follows:

[0114]

[0115]

[0116] When the initial acceleration is 0, the relationship between speed and time is indicated as equation (16) as follows:

[0117]

[0118] Since the speed is a variable quantity under the condition of constant acceleration, a variable τ and T can be introduced:

[0119] τ k = t - t k-1

[0120] T k = t k - t k-1 (17)

[0121] Wherein, T k is the total duration of the kth time interval, and τ k is the specific time in a time interval.

[0122] In combination with equation (12) to equation (17) above, equation (11) above can be derived.

[0123] Specifically, the change limit information can be expressed in the running state model as equation (18) as follows:

[0124] V next sinθ > = V now - 0.06a max

[0125] V next cosθ < 0.06a max

[0126] V next < V max (18)

[0127] wherein 0.06 represents a constant, which can be determined according to a running period configured for the intelligent device. next represents a speed calculated based on the running state model, i.e., a second speed, v now represents a speed input into the running state model for indicating a corresponding relationship between the speed and time, i.e., a first speed, θ represents a value of an included angle between v next and v now . max represents a maximum speed.

[0128] In combination with the formula (7) and the formula (18), the formula (19) representing the above change limiting information can be derived:

[0129]

[0130] V next <V max (19)

[0131] In combination with the formula (9) and the formula (17), it can be known that, in a case that the mass of the object carried by the intelligent device is known, the running state model can be controlled to run stably in combination with the running state model.

[0132] In some optional embodiments, the running state model is configured with at least one corresponding relationship between the speed and time.

[0133] Based on this, the process of determining the second speed of the intelligent device at a next time according to the first speed of the intelligent device at a current time and the running state model can include: determining a target corresponding relationship from the multiple corresponding relationships between the speed and time configured by the running state model according to the first speed of the intelligent device at the current time; and determining the second speed of the intelligent device at the next time according to the first speed of the intelligent device at the current time and the target corresponding relationship.

[0134] wherein the target corresponding relationship is used to indicate that the running state model is configured with at least one corresponding relationship between the speed and time, and the corresponding relationship is used to calculate the second speed.

[0135] In some optional embodiments, the intelligent device control method can further include: if the speed direction of the first speed and the speed direction of the second speed are the same, controlling the intelligent device to carry the object to run according to the second speed.

[0136] Specifically, if the speed direction of the first speed and the speed direction of the second speed are the same, it indicates that the running state of the intelligent device is straight running, and the intelligent device can be directly controlled to carry the object to run according to the second speed determined by the above formula (11).

[0137] In some optional embodiments, the process of controlling the smart device to load the object to run based on the running state model, the change limit information, the first speed, and the second speed includes: determining an included angle value between a speed direction of the first speed and a speed direction of the second speed; determining a speed value range of the second speed according to the included angle value, the first speed, the running state model, and the change limit information; and controlling the smart device to load the object to run based on the second speed and the speed value range.

[0138] Specifically, the speed value range can be determined based on the formula (19).

[0139] In some optional embodiments, the speed value range includes a maximum speed value and a minimum speed value, and the maximum speed value is not greater than a maximum running speed of the smart device.

[0140] Based on this, the process of controlling the smart device to load the object to run based on the second speed and the speed value range can include: if the second speed is less than the minimum speed value, controlling the smart device to load the object to run according to the minimum speed value; if the second speed is greater than the maximum speed value, controlling the smart device to load the object to run according to the maximum speed value; and if the second speed falls within the speed value range, controlling the smart device to load the object to run according to the second speed.

[0141] In some optional embodiments, the process of controlling the smart device to load the object to run according to the target motion parameter and the change limit information can include: determining a running path of the smart device; generating running state information of a plurality of position points of the running path based on the running path, the target motion parameter, and the change limit information; generating running control information based on the running state information of the plurality of position points of the running path; and controlling the smart device to load the object to run according to the running path based on the running control information.

[0142] The running path of the smart device can be a global path, or the running path of the smart device can be a local path, which can be adjusted according to actual conditions, and the embodiments of the present application are not limited.

[0143] The running state information includes attitude information of the smart device corresponding to the position point on a map, position information of the position point on the map, self-attitude information of the smart device corresponding to the position point, a maximum acceleration of the smart device when loading the object, and a maximum speed of the smart device.

[0144] The operation control information refers to instructions or tasks or other information for controlling the smart device to load the object and operate according to the operation path.

[0145] In some optional embodiments, the process of generating the operation state information of the plurality of position points of the operation path based on the operation path, the target motion parameter, and the change limit information can include: determining a starting position point and an ending position point of a target local path that needs to be generated from the operation path if local path planning is needed; determining a target local path according to the starting position point and the ending position point; and generating operation state information of a plurality of position points of the target local path according to the target local path, the target motion parameter, and the change limit information.

[0146] The case where local path planning is needed can be adjusted according to actual conditions, and the embodiments of the present application do not limit it. For example, when the smart device operates according to the operation path, it is detected that there is an obstacle on the operation path, and then local path planning is needed. For another example, a local path planning instruction is received from the industrial computer, and then local path planning is needed.

[0147] Specifically, please refer to Figure 5 , Figure 5 is a scene diagram of the present application needing local path planning, wherein S represents a starting position point, G represents an ending position point, and the black part is an obstacle. The target local path is determined in combination with the starting position point and the ending position point, and the number of position points of the target local path and the operation state information of each position point can be determined in combination with the operation state model, the planned operation time, and the operation cycle. For example, 2000 position points and the operation state information of each position point are shown in Figure 6 , wherein the left side is the acceleration process, the middle is the constant speed process, and the right side is the deceleration process. The 2000 position points obtained above are substituted into the target local path, the entire target local path is divided into 2000 points, and the operation state information of each position point is determined (x, y, theta, a max , v max ). The theta is used to indicate the attitude information (such as the angle, position information) of the smart device itself.

[0148] In some optional embodiments, the process of determining a target local path based on the starting position point and the ending position point may include: obtaining constraint parameters, the constraint parameters including at least one of the angle difference between the starting position point and the ending position point, the distance between the starting position point and an obstacle on the running path, and the speed difference between the starting position point and the ending position point; determining a target local path based on the constraint parameters, the starting position point, and the ending position point, wherein the target local path does not include the obstacle.

[0149] Furthermore, the weights of the constraint parameters can be determined, and based on the constraint parameters, their weights, the starting point, and the ending point, a target local path can be determined, wherein the target local path does not include the obstacles.

[0150] For example, such as Figure 7 The above, Figure 7 The green portion represents all possible operating scenarios under the change constraint information determined by the above formula (19). From all these possible operating scenarios, combined with the trajectory evaluation function, the following is selected: Figure 8 The target local path is shown.

[0151] The trajectory evaluation function can be expressed by the following formula (20):

[0152] G(v, theta)=α*heading(v, theta)+β*dist(v, theta)+γ*velocity(v, theta) (20)

[0153] Where α, β, and γ are the weights of the constraint parameters, heading(v, theta) is the angle evaluation function used to determine the angle difference between the starting position and the ending position, dist(v, theta) is used to determine the distance between the starting position and the obstacles on the running path, velocity(v, theta) is used to determine the velocity difference between the starting position and the ending position, and G(v, theta) is the evaluation value, which determines the target local path based on the evaluation values ​​of different local paths.

[0154] In some alternative embodiments, there are multiple ways to obtain the target mass of the object that the smart device needs to load.

[0155] For example, when the intelligent device is configured with a jacking device, the jacking device is loaded with the object, and the process of acquiring the target mass of the object required to be loaded by the intelligent device can include: determining a force value of the jacking device in the vertical direction and a first distance between the jacking device and a preset fulcrum; determining a second distance between the center of gravity of the object loaded by the intelligent device and the preset fulcrum; and determining the target mass of the object according to the force value, the first distance, the second distance, and the acceleration of gravity.

[0156] It should be noted that the above content can be adjusted according to actual conditions, and the present application is not limited.

[0157] For example, assuming that the intelligent device is a fork truck type guide vehicle, and is configured with forks and a binocular depth camera. The focal length of the binocular depth camera, the distance between the two cameras, and two reference points on the actual object can be used to determine the distance of the actual object from the camera and the length of the actual object. In combination with the length of the actual object, the initial distance of the actual object from the forks of the fork truck type guide vehicle, and the length of the pallet on which the actual object is located, the center of gravity of the actual object on the forks can be determined. In combination with the center of gravity of the actual object on the forks and the principle of the lever, the mass of the actual object can be determined, and the formula is relatively existing, which will not be described here.

[0158] In some optional embodiments, the target mass includes an actual mass; and the process of determining the target mass of the object according to the force value, the first distance, the second distance, and the acceleration of gravity can include: acquiring a mass error value; determining a theoretical mass of the object according to the force value, the first distance, the second distance, and the acceleration of gravity; and determining an actual mass of the object according to the theoretical mass and the mass error value.

[0159] In order to better implement the intelligent device control method provided by the embodiments of the present application, the embodiments of the present application further provide a device based on the above-mentioned intelligent device control method. The meanings of the terms are the same as those in the above-mentioned intelligent device control method, and the specific implementation details can be referred to the description in the method embodiment.

[0160] For example, as shown in Figure 9 The intelligent device control device can include a target mass acquisition unit 201, a change limit information determination unit 202, and a control unit 203, and the details are as follows:

[0161] The target mass acquisition unit 201 is configured to acquire the target mass of the object required to be loaded by the intelligent device.

[0162] The change limit information determination unit 202 is configured to determine the change limit information of the target motion parameter of the intelligent device according to the target mass.

[0163] The control unit 203 is configured to control the smart device to move the object according to the target motion parameter and the change limit information.

[0164] In an embodiment of the present application, the change limit information determination unit 202 comprises:

[0165] The maximum acceleration determination sub-unit is configured to determine the maximum acceleration of the smart device when the smooth movement condition is met according to the target mass.

[0166] The change limit information determination sub-unit is configured to determine the change limit information of the target motion parameter of the smart device according to the maximum acceleration.

[0167] In an embodiment of the present application, the smooth movement condition comprises that the resultant force acting on the object is not greater than the sliding friction force acting on the object.

[0168] Based on this, the maximum acceleration determination sub-unit comprises:

[0169] The resultant force determination module is configured to determine the resultant force acting on the object according to the acceleration of the smart device when the object is loaded and the target mass.

[0170] The sliding friction force determination module is configured to determine the sliding friction force acting on the object according to the pressure acting on the object and the kinetic friction coefficient.

[0171] The maximum acceleration determination module is configured to determine the acceleration when the resultant force acting on the object is equal to the sliding friction force acting on the object, and take the acceleration as the maximum acceleration of the smart device when the smooth movement condition is met.

[0172] In an embodiment of the present application, the sliding friction force determination module comprises:

[0173] The influencing factor acquisition sub-module is configured to acquire the influencing factor of the pressure acting on the object and the influence coefficient of the influencing factor.

[0174] The actual pressure determination sub-module is configured to determine the actual pressure acting on the object based on the influencing factor, the influence coefficient of the influencing factor, the target mass and the gravitational acceleration.

[0175] The sliding friction force determination sub-module is configured to determine the sliding friction force acting on the object based on the actual pressure acting on the object and the kinetic friction coefficient.

[0176] In an embodiment of the present application, the target motion parameter comprises the speed of the smart device.

[0177] Based on this, the control unit 203 comprises:

[0178] The second speed determining sub-unit is configured to determine a second speed of the smart device at a next time point according to the first speed of the smart device at a current time point and the running state model.

[0179] The first running control sub-unit is configured to control the smart device to run the load object based on the running state model, the change limit information, the first speed, and the second speed if the speed direction of the first speed is different from the speed direction of the second speed.

[0180] In an embodiment of the present application, the running state model is configured with at least one corresponding relationship between speed and time.

[0181] Based on this, the second speed determining sub-unit includes:

[0182] The target corresponding relationship determining module is configured to determine a target corresponding relationship from the multiple corresponding relationships between speed and time configured in the running state model according to the first speed of the smart device at the current time point.

[0183] The second speed determining module is configured to determine the second speed of the smart device at the next time point according to the first speed of the smart device at the current time point and the target corresponding relationship.

[0184] In an embodiment of the present application, the smart device control apparatus further includes:

[0185] The second running control sub-unit is configured to control the smart device to run the load object according to the second speed if the speed direction of the first speed is the same as the speed direction of the second speed.

[0186] In an embodiment of the present application, the first running control sub-unit includes:

[0187] The included angle value determining module is configured to determine an included angle value between the speed direction of the first speed and the speed direction of the second speed.

[0188] The speed value range module is configured to determine a speed value range of the second speed according to the included angle value, the first speed, the running state model, and the change limit information.

[0189] The control module is configured to control the smart device to run the load object based on the second speed and the speed value range.

[0190] In an embodiment of the present application, the speed value range includes a maximum speed value and a minimum speed value, and the maximum speed value is not greater than the maximum running speed of the smart device.

[0191] Based on this, the control module includes:

[0192] The first control submodule is configured to control the smart device to carry the object according to the minimum speed if the second speed is less than the minimum speed.

[0193] The second control submodule is configured to control the smart device to carry the object according to the maximum speed if the second speed is greater than the maximum speed.

[0194] The third control submodule is configured to control the smart device to carry the object according to the second speed if the second speed falls within the speed range.

[0195] In an embodiment of the present application, the control unit 203 comprises:

[0196] The running path determination subunit is configured to determine a running path of the smart device.

[0197] The running state information determination subunit is configured to generate running state information of a plurality of position points of the running path based on the running path, the target motion parameter, and the change limit information.

[0198] The running control information generation subunit is configured to generate running control information based on the running state information of the plurality of position points of the running path.

[0199] The second control subunit is configured to control the smart device to carry the object according to the running path based on the running control information.

[0200] In an embodiment of the present application, the running state information determination subunit comprises:

[0201] The position point determination module is configured to determine a starting position point and an ending position point of a target local path that needs to be generated from the running path if local path planning is needed.

[0202] The target local path determination module is configured to determine the target local path according to the starting position point and the ending position point.

[0203] The running state information determination module is configured to generate running state information of a plurality of position points of the target local path based on the target local path, the target motion parameter, and the change limit information.

[0204] In an embodiment of the present application, the target local path determination module comprises:

[0205] The constraint parameter acquisition sub-module is configured to acquire constraint parameters, the constraint parameters including at least one of an angle difference between the starting position point and the ending position point, a distance between the starting position point and an obstacle of the running path, and a speed difference between the starting position point and the ending position point.

[0206] The target local path determination sub-module is configured to determine a target local path according to the constraint parameter, the start position point and the end position point, and the target local path does not include the obstacle.

[0207] In an embodiment of the present application, the intelligent device is provided with a jacking device, and the jacking device carries the object.

[0208] Based on this, the target quality acquisition unit 201 comprises:

[0209] The first distance determination sub-unit is configured to determine a force value of the jacking device in the vertical direction and a first distance between the jacking device and the preset fulcrum.

[0210] The second distance determination sub-unit is configured to determine a second distance between the center of gravity of the object carried by the intelligent device and the preset fulcrum.

[0211] The target quality determination sub-unit is configured to determine the target quality of the object according to the force value, the first distance, the second distance and the gravitational acceleration.

[0212] In an embodiment of the present application, the target quality comprises an actual quality.

[0213] Based on this, the target quality determination sub-unit comprises:

[0214] The quality error value acquisition module is configured to acquire a quality error value.

[0215] The theoretical quality determination module is configured to determine a theoretical quality of the object according to the force value, the first distance, the second distance and the gravitational acceleration.

[0216] The actual quality determination module is configured to determine an actual quality of the object according to the theoretical quality and the quality error value.

[0217] In an embodiment of the present application, the intelligent device comprises at least one of a guided vehicle and an intelligent mechanical device.

[0218] Therefore, the intelligent device control device provided by the embodiments of the present application acquires the target quality of the object carried by the intelligent device through the target quality acquisition unit 201, determines the change limitation information of the target motion parameter of the intelligent device according to the target quality through the change limitation information determination unit 202, and controls the intelligent device to carry the object to run according to the target motion parameter and the change limitation information through the control unit 203. In this way, by combining the quality of the object carried by the intelligent device, the change limitation condition of the target motion parameter of the intelligent device is determined, so that when the intelligent device carries the object to run, the change limitation condition of the target motion parameter of the intelligent device can be dynamically adjusted according to the different qualities of the objects carried by the intelligent device, so as to improve the stability when the intelligent device runs.

[0219] In practice, the above modules can be implemented as independent entities, or combined as the same or several entities, and the specific implementation and corresponding beneficial effects of the above modules can be referred to the method embodiments, which will not be repeated here.

[0220] The embodiments of the present application also provide a control device, as shown in the accompanying drawings, which shows a structural schematic diagram of the control device related to the embodiments of the present application, in particular: Figure 10

[0221] The control device can include a processor 301 with one or more processing cores, a memory 302 with one or more storage media, a power supply 303, an input unit 304, and the like. Those skilled in the art can understand that the structure of the control device shown in the accompanying drawings does not constitute a limitation on the control device, and can include more or fewer components than shown, or combine certain components, or different component arrangements. Figure 10

[0222] Among them:

[0223] The processor 301 is the control center of the control device, and connects various parts of the control device through various interfaces and lines, and performs various functions of the control device and processes data by running or executing computer programs and / or modules stored in the memory 302, and calling data stored in the memory 302. Optionally, the processor 301 can include one or more processing cores; preferably, the processor 301 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 301.

[0224] The memory 302 can be used to store computer programs and modules, and the processor 301 executes various function applications by running the computer programs and modules stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store the operating system, at least one computer program required by the function (such as sound playing function, navigation function, etc.), etc.; the data storage area can store data created according to the use of the control device, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 302 can also include a memory controller to provide the processor 301 with access to the memory 302.

[0225] ​​The control device further comprises a power supply 303 for supplying power to each component. Preferably, the power supply 303 is logically connected to the processor 301 through a power management system, so that the power management system can be used to manage charging, discharging, power consumption management and the like. The power supply 303 can further comprise one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

[0226] The control device can further comprise an input unit 304 for receiving input digital or character information, and generating keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0227] Although not shown, the control device can further comprise a display unit and the like, which will not be described here. In the present embodiment, the processor 301 in the control device loads one or more executable files corresponding to the processes of one or more computer programs into the memory 302 according to the following instructions, and runs the computer programs stored in the memory 302 by the processor 301, thereby implementing various functions such as:

[0228] obtaining a target mass of a load object required by the smart device;

[0229] determining change limit information of a target motion parameter of the smart device according to the target mass;

[0230] controlling the smart device to run the load object according to the target motion parameter and the change limit information.

[0231] Therefore, the smart device provided by the embodiments of the present application obtains a target mass of a load object required by the smart device, determines change limit information of a target motion parameter of the smart device according to the target mass, and controls the smart device to run the load object according to the target motion parameter and the change limit information. In this way, by combining the mass of the load object of the smart device, the change limit condition of the target motion parameter of the smart device is determined, so that when the smart device is controlled to run the load object, the change limit condition of the target motion parameter of the smart device can be dynamically adjusted according to the mass of the load object, thereby improving the stability of the smart device when running.

[0232] The specific implementation and corresponding advantages of each operation can be found in the detailed description of the smart device control method above, and will not be described here.

[0233] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a computer program, or by a computer program controlling relevant hardware, which can be stored in a storage medium and loaded and executed by a processor.

[0234] To this end, an embodiment of the present application provides a storage medium, which stores a computer program capable of being loaded by a processor to execute steps in any of the intelligent device control methods provided by the embodiments of the present application. For example, the computer program can execute the following steps:

[0235] Obtaining a target mass of a load object required by the intelligent device;

[0236] Determining change limit information of a target motion parameter of the intelligent device according to the target mass;

[0237] Controlling the intelligent device to load the object according to the target motion parameter and the change limit information.

[0238] Therefore, the storage medium provided by the embodiments of the present application can obtain a target mass of a load object required by the intelligent device, determine change limit information of a target motion parameter of the intelligent device according to the target mass, and control the intelligent device to load the object according to the target motion parameter and the change limit information. In this way, by combining the mass of the object loaded by the intelligent device, the change limit condition of the target motion parameter of the intelligent device is determined, so that when the intelligent device is controlled to load the object, the change limit condition of the target motion parameter of the intelligent device can be dynamically adjusted according to the mass of the object loaded by the intelligent device, thereby improving the stability when the intelligent device is controlled to load the object.

[0239] The specific implementation manners of the above operations and the corresponding beneficial effects can be referred to the previous embodiments, which will not be described here.

[0240] The storage medium can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0241] Since the computer program stored in the storage medium can execute steps in any of the intelligent device control methods provided by the embodiments of the present application, the beneficial effects that can be achieved by any of the intelligent device control methods provided by the embodiments of the present application can be achieved, which will not be described here.

[0242] According to an aspect of the present application, a computer program product or computer program is provided, which comprises computer instructions stored in a storage medium. A processor of a computer device reads the computer instructions from the storage medium, and the processor executes the computer instructions, so that the computer device executes the intelligent device control method.

[0243] The above describes in detail the intelligent device control method, device, storage medium and control device provided by the embodiments of the present application. The principles and implementation manners of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method and core idea of the present application. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for controlling an intelligent device, characterized in that, The method includes: Obtain the target mass of the object to be loaded by the smart device; Based on the target quality, determine the variation restriction information of the target motion parameters of the smart device; Based on the target motion parameters and the change restriction information, the intelligent device is controlled to carry the object. The target motion parameters include the speed of the smart device, and controlling the smart device to carry the object based on the target motion parameters and the change restriction information includes: Based on the first speed of the intelligent device at the current moment and the operating state model, determine the second speed of the intelligent device at the next moment; If the velocity directions of the first speed and the second speed are different, then based on the operating state model, the change restriction information, the first speed, and the second speed, the intelligent device is controlled to carry the object. The change restriction information is determined according to the following formula: ； in, Refers to the first velocity Second speed The angle between them This refers to the maximum speed. This refers to the maximum acceleration; The operating state model is determined according to the following formula: in, to Used to represent the initial velocity in different time intervals. This represents the final velocity determined using the initial velocity over different time intervals, and the correspondence between velocity and time over those time intervals. This refers to the degree of urgency. This refers to the total duration of the k-th time interval. This refers to the specific time within the k-th time interval, where k is a positive integer not greater than 7. The different time intervals include the start interval [t0-t1], the acceleration interval [t1-t2], the near-stable interval [t2-t3], the stable interval [t3-t4], the deceleration interval from the stable state [t4-t5], the deceleration interval [t5-t6], and the termination interval [t6-t7].

2. The intelligent device control method according to claim 1, characterized in that, The step of determining the change limit information of the target motion parameters of the smart device based on the target quality includes: Based on the target mass, determine the maximum acceleration of the intelligent device when it meets the conditions for stable operation; Based on the maximum acceleration, determine the change limit information of the target motion parameters of the smart device.

3. The intelligent device control method according to claim 2, characterized in that, The stable operating conditions include the net force acting on the object not exceeding the sliding friction force acting on the object; determining the maximum acceleration of the intelligent device when it meets the stable operating conditions based on the target mass includes: The net force acting on the object is determined based on the acceleration of the object when the smart device is loaded and the target mass. Determine the sliding friction force on the object based on the pressure and coefficient of kinetic friction. The acceleration at which the net force on the object equals the sliding friction force on the object is determined, and this acceleration is taken as the maximum acceleration of the smart device when it meets the conditions for smooth operation.

4. The intelligent device control method according to claim 3, characterized in that, The step of determining the sliding friction force on the object based on the pressure and coefficient of kinetic friction includes: Obtain the influencing factors of the pressure applied to the object, and the influence coefficients of the influencing factors; Based on the influencing factors, the influence coefficients of the influencing factors, the target mass, and gravitational acceleration, the actual pressure exerted on the object is determined; The sliding friction force on the object is determined based on the actual pressure and coefficient of kinetic friction.

5. The intelligent device control method according to claim 1, characterized in that, The operating state model is configured with at least one correspondence between speed and time; determining the second speed of the smart device at the next moment based on the first speed of the smart device at the current moment and the operating state model includes: Based on the first speed of the intelligent device at the current moment, the target correspondence is determined from the various correspondences between speed and time configured in the operating state model; Based on the first speed of the smart device at the current moment and the correspondence with the target, the second speed of the smart device at the next moment is determined.

6. The intelligent device control method according to claim 1, characterized in that, The method further includes: If the direction of the first speed and the direction of the second speed are the same, then the smart device is controlled to carry the object according to the second speed.

7. The intelligent device control method according to claim 1, characterized in that, The method of controlling the intelligent device to carry the object based on the operating state model, the change restriction information, the first speed, and the second speed includes: Determine the angle between the velocity directions of the first velocity and the second velocity; Based on the included angle value, the first speed, the operating state model, and the change restriction information, determine the speed range that limits the second speed; Based on the second speed and the speed range, the intelligent device is controlled to carry the object.

8. The intelligent device control method according to claim 7, characterized in that, The speed range includes a maximum speed and a minimum speed, wherein the maximum speed is not greater than the maximum operating speed of the smart device; controlling the smart device to carry the object based on the second speed and the speed range includes: If the second speed is less than the minimum speed, the smart device is controlled to carry the object according to the minimum speed. If the second speed is greater than the maximum speed, the smart device is controlled to carry the object according to the maximum speed. If the second speed falls within the range of the speed values, the intelligent device is controlled to carry the object according to the second speed.

9. The intelligent device control method according to claim 1, characterized in that, The step of controlling the smart device to carry the object based on the target motion parameters and the change restriction information includes: Determine the operating path of the intelligent device; Based on the running path, the target motion parameters, and the change restriction information, the running status information of multiple location points on the running path is generated; Based on the running status information of multiple location points along the running path, running control information is generated; Based on the operation control information, the intelligent device is controlled to carry the object according to the operation path.

10. The intelligent device control method according to claim 9, characterized in that, The step of generating running status information for multiple location points along the running path based on the running path, the target motion parameters, and the change restriction information includes: If local path planning is required, the starting and ending points of the target local path to be generated are determined from the running path. Determine the target local path based on the starting point and the ending point; Based on the target local path, the target motion parameters, and the change restriction information, the running status information of multiple location points on the target local path is generated.

11. The intelligent device control method according to claim 10, characterized in that, Determining the target local path based on the starting position point and the ending position point includes: Obtain constraint parameters, the constraint parameters including at least one of the following: the angle difference between the starting position point and the ending position point, the distance between the starting position point and the obstacle on the running path, and the speed difference between the starting position point and the ending position point; A target local path is determined based on the constraint parameters, the starting position point, and the ending position point, wherein the target local path does not include the obstacles.

12. The intelligent device control method according to any one of claims 1 to 11, characterized in that, The smart device is equipped with a lifting device that carries the object. Obtaining the target mass of the object carried by the smart device includes: Determine the force value of the lifting device in the vertical direction, and the first distance between the lifting device and the preset fulcrum; Determine a second distance between the center of gravity of the object carried by the smart device and the preset fulcrum; The target mass of the object is determined based on the force value, the first distance, the second distance, and the gravitational acceleration.

13. The intelligent device control method according to claim 12, characterized in that, The target quality includes the actual quality; Determining the target mass of the object based on the applied force value, the first distance, the second distance, and gravitational acceleration includes: Obtain the quality error value; The theoretical mass of the object is determined based on the force value, the first distance, the second distance, and the gravitational acceleration. The actual mass of the object is determined based on the theoretical mass and the mass error value.

14. The intelligent device control method according to any one of claims 1 to 11, characterized in that, The intelligent device includes at least one of guided vehicles and intelligent mechanical devices.

15. A smart device control device, characterized in that, The device includes: The target mass acquisition unit is used to acquire the target mass of the load object required by the smart device. A change restriction information determination unit is used to determine change restriction information of the target motion parameters of the smart device based on the target mass; The control unit is used to control the intelligent device to carry the object to run based on the target motion parameters and the change restriction information; The target motion parameters include the speed of the smart device, and controlling the smart device to carry the object based on the target motion parameters and the change restriction information includes: Based on the first speed of the intelligent device at the current moment and the operating state model, determine the second speed of the intelligent device at the next moment; If the velocity directions of the first speed and the second speed are different, then based on the operating state model, the change restriction information, the first speed, and the second speed, the intelligent device is controlled to carry the object. The change restriction information is determined according to the following formula: ； in, Refers to the first velocity Second speed The angle between them This refers to the maximum speed. This refers to the maximum acceleration; The operating state model is determined according to the following formula: in, to Used to represent the initial velocity in different time intervals. This represents the final velocity determined using the initial velocity over different time intervals, and the correspondence between velocity and time over those time intervals. This refers to the degree of urgency. This refers to the total duration of the k-th time interval. This refers to the specific time within the k-th time interval, where k is a positive integer not greater than 7. The different time intervals include the start interval [t0-t1], the acceleration interval [t1-t2], the near-stable interval [t2-t3], the stable interval [t3-t4], the deceleration interval from the stable state [t4-t5], the deceleration interval [t5-t6], and the termination interval [t6-t7].

16. A storage medium, characterized in that, Includes a computer program, which, when run on a processor, causes the processor to perform the steps of the intelligent device control method according to any one of claims 1 to 14.

17. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the intelligent device control method according to any one of claims 1 to 14.

18. A control device, characterized in that, It includes one or more processors and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the intelligent device control method according to any one of claims 1 to 14.

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