Robot control method, robot control system, robot, and storage medium

Abstract

The embodiment of the invention provides a robot control method, a robot control system, a robot and a computer readable storage medium, and the robot control method comprises the steps that an execution part of the robot is controlled to move according to a preset movement track, and in the current control period, external force borne by the execution part of the robot is collected; the preset motion trail is composed of a plurality of discrete pose data; determining a target position of an execution part of the robot in the current control period based on the external force by utilizing an established admittance control model; and mapping the determined target position in the current control period to a corresponding motion track, and controlling an execution part of the robot to move according to a mapping result. Therefore, the target position determined by the admittance control module is mapped to the motion trail, the execution component is controlled to move according to the mapping result, and compliant control is achieved in man-machine interaction.

Description

Technical Field

[0001] The present disclosure relates to the technical field of robot motion control, and in particular to a robot control method, a robot control system, a robot, and a computer-readable storage medium. Background Art

[0002] With the continuous development of science and technology, various types of robots have been developed and applied to various application scenarios. Taking rehabilitation robots in the field of medical devices as an example, rehabilitation robots can be used to help patients with rehabilitation training. Rehabilitation robots can be customized to formulate rehabilitation training plans that are adapted to patients according to the design program, and can effectively replace therapists to provide patients with repetitive, stable, efficient, and high-efficiency training. For example, in the postoperative rehabilitation treatment of hemiplegic patients after stroke, limb rehabilitation robots (e.g., upper limb rehabilitation robots, lower limb rehabilitation robots, upper and lower limb rehabilitation robots) are usually used to assist users in rehabilitation training. This is mainly because auxiliary accessories can be connected to the user's upper and / or lower limbs, and can simultaneously limit the user's upper and / or lower limb extremities to perform circular motions.

[0003] Rehabilitation training requires active patient participation. When patients actively participate in rehabilitation training, they initiate movement intentions, which triggers specific activation patterns in neural networks such as the motor cortex, basal ganglia, and cerebellum. This intention-driven neurophysiological activity can significantly promote the remodeling of synaptic connections and the reorganization of motor control circuits, contributing to the recovery of brain function. Studies have shown that active rehabilitation training is more effective than passive rehabilitation training.

[0004] Rehabilitation training involves a complex human-machine interaction process. Therefore, whether a rehabilitation robot possesses both active compliance and adaptability is crucial to the patient's training experience and is crucial for ensuring repetitive, long-term exercise training. However, current technologies for compliant control of rehabilitation robots still face challenges, such as insufficient adaptability, poor tracking, and challenges in human-machine interaction. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the prior art, the purpose of the present disclosure is to provide a robot control method, a robot control system, a robot, and a computer-readable storage medium to solve the problems in the related art.

[0006] A first aspect of the present disclosure provides a robot control method, comprising:

[0007] Controlling the robot's actuator to move along a predetermined motion trajectory, and collecting the external force applied to the robot's actuator during the current control cycle; the predetermined motion trajectory is composed of a plurality of discrete pose data;

[0008] Determining a target position of an actuator of the robot in a current control cycle based on the external force using the established admittance control model; and

[0009] The target position determined in the current control cycle is mapped to the corresponding motion trajectory, and the robot's actuators are controlled to move according to the mapping results to achieve compliant control.

[0010] In an embodiment of the first aspect, the predetermined motion trajectory is obtained by trajectory recording.

[0011] In an embodiment of the first aspect, obtaining a predetermined motion trajectory through trajectory recording includes: controlling the robot's executive component or the operator operating the robot's executive component to move along the expected motion trajectory; based on the control cycle interval, collecting the posture data of the robot's executive component one by one; the posture data includes position information and posture information; until the robot's executive component completes the expected motion trajectory, a set of discrete posture data is obtained; the set of discrete posture data can form a predetermined motion trajectory.

[0012] In an embodiment of the first aspect, the admittance control model includes: a definite relationship between a comprehensive result of one or more physical parameters of the robot, including inertia, damping, elasticity, and nonlinear force, and the residual force after the external force overcomes the expected interaction force of the robot; the inertia is related to the acceleration change of the robot's current acceleration relative to the expected acceleration, the damping is related to the speed change of the robot's current speed relative to the expected speed, and the elasticity is related to the position change of the robot's current position relative to the expected position.

[0013] In an embodiment of the first aspect, the established admittance control model is used to determine the target position of the robot's actuator in the current control cycle based on the external force, including: calculating the target acceleration of the admittance control model in the current control cycle based on the external force; calculating the target speed in the current control cycle based on the target acceleration in the current control cycle and the target acceleration and target speed in the previous control cycle; and calculating the target position in the current control cycle based on the target speed in the current control cycle and the target speed and target position in the previous control cycle.

[0014] In an embodiment of the first aspect, mapping the target position determined within the current control cycle to the corresponding motion trajectory includes: determining, by comparison, the distance accumulation values ​​of at least two moments related to the target position in a distance accumulation set; and calculating, based on the determined distance accumulation values ​​of at least two moments, the posture data of the target position mapped to the motion trajectory.

[0015] A second aspect of the present disclosure provides a robot control system, comprising:

[0016] Motion execution module, including execution components and drive motor;

[0017] a signal acquisition module, configured on the execution component, for acquiring external force applied to the execution component;

[0018] an admittance control model, configured to determine a target position of the actuator of the robot in the current control cycle based on the external force applied to the actuator acquired by the signal acquisition module in the current control cycle through the admittance control model;

[0019] The motion control module is used to map the target position determined in the current control cycle to the corresponding motion trajectory, and output a corresponding control signal to the drive motor to drive the execution component to move according to the mapping result to achieve flexible control.

[0020] In an embodiment of the second aspect, the robot control system further includes a trajectory recording module, configured to obtain a predetermined motion trajectory using a trajectory recording method.

[0021] A third aspect of the present disclosure provides a robot, comprising:

[0022] A motion execution device, comprising an execution component for executing an action;

[0023] A signal acquisition device, used to acquire the external force applied to the actuator;

[0024] A processor, a memory and a communication interface; the communication interface is communicatively coupled to the acquisition device and the motion execution device;

[0025] The memory stores program instructions;

[0026] The processor is used to run the program instructions to execute the robot control method as described above, generate control instructions and send them to the motion execution device through the communication interface, so that the execution components in the motion execution device perform actions according to the control instructions to achieve flexible control.

[0027] A fourth aspect of the present disclosure provides a computer-readable storage medium storing program instructions, which, when executed, execute the robot control method as described above.

[0028] As described above, embodiments of the present disclosure provide a robot control method, a robot control system, a robot, and a computer-readable storage medium. The robot control method includes: controlling the robot's actuator to move along a predetermined motion trajectory, and collecting external forces acting on the robot's actuator during a current control cycle; the predetermined motion trajectory is composed of a plurality of discrete pose data; utilizing an established admittance control model to determine the target position of the robot's actuator within the current control cycle based on the external forces; mapping the determined target position within the current control cycle to a corresponding motion trajectory, and controlling the robot's actuator to move according to the mapping result. Thus, the target position determined by the admittance control module is mapped to the motion trajectory, and the actuator is controlled to move according to the mapping result, thereby achieving compliant control in human-machine interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 FIG. 1 is a flow chart of a motion control method according to an embodiment of the present disclosure.

[0030] Figure 2 Shown is a module diagram of an embodiment of the robot control system disclosed herein

[0031] Figure 3 Shown is a schematic diagram of the circuit structure of the controller disclosed in the present invention.

[0032] Figure 4 Shown is a schematic structural diagram of the robot disclosed in one embodiment.

[0033] Figure 5 Shown is a schematic diagram of the principle of the robot admittance motion control disclosed in the present invention. DETAILED DESCRIPTION

[0034] The following describes the embodiments of the present disclosure through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present disclosure from the information disclosed in this disclosure. The present disclosure can also be implemented or applied through different specific embodiments. The details of the present disclosure can also be modified or changed according to different viewpoints and application modules without departing from the spirit of the present disclosure. It should be noted that the embodiments and features in the embodiments of the present disclosure can be combined with each other unless there is a conflict.

[0035] The following is a detailed description of the embodiments of the present disclosure with reference to the accompanying drawings so that those skilled in the art can easily implement the present disclosure. The present disclosure can be embodied in many different forms and is not limited to the embodiments described herein.

[0036] Throughout the present disclosure, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or a group of embodiments or examples. Furthermore, those skilled in the art may combine and integrate different embodiments or examples, and features of different embodiments or examples, as described in the present disclosure, without conflicting requirements.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the context of this disclosure, "a group" means two or more, unless otherwise specifically defined.

[0038] In order to clearly describe the present disclosure, components not related to the description are omitted, and the same or similar components throughout the specification are denoted by the same reference numerals.

[0039] Throughout this specification, when a device is said to be "connected" to another device, this includes not only "direct connection" but also "indirect connection" with other elements interposed therebetween. Furthermore, when a device is said to "include" a certain component, unless otherwise stated, this does not exclude the inclusion of other components but rather implies that the device may include other components.

[0040] Although the terms first, second, etc. are used in this document to represent various elements in some examples, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface, etc. are represented. Furthermore, as used in this document, the singular forms "one," "an," and "the" are intended to also include the plural forms, unless there is a contrary indication in the context. It should be further understood that the terms "comprise" and "include" indicate the presence of the described features, steps, operations, elements, modules, projects, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or a group of other features, steps, operations, elements, modules, projects, types, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B, or C" or "A, B, and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B, and C." Exceptions to this definition only occur when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0041] The technical terms used herein are intended only to refer to specific embodiments and are not intended to limit the present disclosure. The singular form used herein also includes the plural form unless the statement explicitly indicates otherwise. The term "comprising" as used in this specification is intended to specify specific features, regions, integers, steps, operations, elements, and / or components and does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and / or components.

[0042] Although not defined differently, all terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art to which this disclosure belongs. Terms defined in commonly used dictionaries are additionally interpreted as having meanings consistent with relevant technical literature and the current message. Unless otherwise defined, they should not be overly interpreted as ideal or highly formalized meanings.

[0043] At present, there are still some challenges in the compliant control of rehabilitation robots in their interactive motion control, such as insufficient adaptability and poor following effect.

[0044] In view of this, a robot control method is provided in an embodiment of the present disclosure, which maps the target position determined by the admittance control module to a motion trajectory, and controls the execution component to move according to the mapping result, thereby achieving flexible control in human-computer interaction. The following description uses a rehabilitation robot as an example. A rehabilitation robot is a device that can be used to assist people with physical movement disorders in actively or passively performing rehabilitation training movements to overcome their obstacles. For example, the rehabilitation robot may include an end effector that moves within a two-dimensional plane and can rotate in one dimension. The end effector is configured to interact with the user's limbs (e.g., upper limbs, lower limbs) so that it is driven by the user's force when the user's limbs move, or provides a force that drives the user's limbs to move / rotate, etc. It should be noted that the rehabilitation robot is only an example of a robot. In other embodiments, other types of robots can also be used, and the motion control method in the embodiment of the present disclosure can be applied to all of them. The method is not limited to rehabilitation robots.

[0045] like Figure 1 FIG. 1 is a flow chart of a motion control method according to an embodiment of the present invention.

[0046] exist Figure 1 The motion control method comprises the following steps:

[0047] Step S101: controlling the robot's executive component to move along a predetermined motion trajectory, and collecting the external force applied to the robot's executive component during the current control cycle.

[0048] Taking a rehabilitation robot as an example, it may include at least one end effector as an actuator. The end effector is configured to interact with a user's limbs (e.g., upper or lower limbs), that is, to allow the user's affected limb (e.g., upper or lower limbs) to interact with the end effector. For example, the end effector can receive force from the user's affected limb and move accordingly. The end effector can also be driven by a drive motor, which can be controlled by a controller so that the end effector can be controlled to actively move to drive the user's affected limb. Therefore, in the embodiments of the present disclosure, the way in which the user performs training movements through the actuator may include one or more of the following: an active mode, in which the user provides power to drive the actuator to move to perform the training movement; a passive mode, in which the actuator is controlled to move to drive the user to perform the training movement; and an assisted mode, in which the user provides power to drive the actuator to move to perform the training movement while the actuator provides a certain amount of assistance.

[0049] For example, taking the upper limb rehabilitation robot as an example, the hand of the affected limb of the user's upper limb can hold or be mounted on the end effector as the execution component. When the upper limb of the affected limb moves, the end effector can be driven accordingly. At the same time, the end effector can provide a certain amount of assistance under the drive of the drive motor.

[0050] The robot's execution component moves according to a predetermined motion trajectory, which is pre-set.

[0051] During the rehabilitation process, trajectory exercises can help patients re-establish normal movement patterns. For example, for patients with limb movement disorders caused by stroke, by training along a specific trajectory (such as from the proximal to the distal end of the affected limb, along the trajectory of normal joint movement), the neuromuscular pathways can be activated and strengthened, just like re-laying a correct movement "track", allowing the patient's limbs to gradually learn to move along the correct path, thereby restoring basic motor functions. Taking arm rehabilitation as an example, the normal arm movement trajectory is from abduction and adduction of the shoulder joint, to flexion and extension of the elbow joint, and then to fine movements of the wrist joint and fingers. In rehabilitation training, having patients repeat exercises along these normal trajectories can gradually improve the motor function of the arm, enabling patients to complete movements from simply raising their hands to finely grasping objects.

[0052] In certain embodiments, the predetermined motion trajectory is designed for precise rehabilitation of the patient's affected limb. For example, for upper limb training of hemiplegic stroke patients, illustratively, the predetermined motion trajectory is a horizontal straight line motion trajectory, for example, the horizontal straight line motion trajectory has two endpoints. Exemplarily, the predetermined motion trajectory is a combination of a horizontal straight line and an arc, for example, including three points in a triangle, wherein the first point and the second point are a straight line, the second point and the third point are a straight line, and the second point and the third point are an arc, or the first point and the second point are a straight line, the second point and the third point are an arc, and the second point and the third point are an arc, or the first point and the second point are an arc, the second point and the third point are an arc, and the second point and the third point are a straight line, etc. Exemplarily, the predetermined motion trajectory is a spiral trajectory (shoulder external rotation + flexion).

[0053] In certain embodiments, the predetermined motion trajectory is dynamically matched to the severity of the patient's illness or the stage of rehabilitation. By designing trajectory movements of different difficulty and complexity, the needs of patients with different degrees of illness or patients at different stages of rehabilitation can be met. For example, for patients with a more serious illness or in the early stages of illness, functional recovery training is mainly used, and the motion trajectory can be designed to be simple, for example, the motion trajectory is shorter and the amplitude of change is smaller. For patients with a less serious illness or patients in the later stages of recovery, functional strengthening training is mainly used, and the motion trajectory can be designed to be more complex.

[0054] In certain embodiments, the predetermined motion trajectory is personalized for each patient, for example, taking into account each patient's biomechanical characteristics, comorbidities, psychological state, etc.

[0055] The predetermined motion trajectory is composed of a plurality of discrete position and posture data.

[0056] In some embodiments, the predetermined motion trajectory is obtained by trajectory recording.

[0057] Specifically, obtaining a predetermined motion trajectory by trajectory recording may include:

[0058] Control the robot's actuators or have the operator operate the robot's actuators to move along the expected motion trajectory.

[0059] In terms of functional implementation, the robot can realize the function of human teaching.

[0060] During specific operations, in some examples, the patient's affected limb or the operator's limb can be brought into contact with the actuator to drive the robot's actuator to move along the intended trajectory. Alternatively, in some examples, the operator's limb can be brought into contact with the actuator, and the actuator can be driven along the intended trajectory by dragging, pushing, or pulling.

[0061] During the motion of the actuator, the position and posture data of the robot's actuator is collected one by one at control cycle intervals. In specific operations, during the motion of the actuator, the position and posture data of the actuator are collected at each control cycle interval, where the position and posture data include position information and attitude information. In some examples, the position information in the pose data of the actuator can be collected through visual acquisition. In some examples, the attitude information in the pose data of the actuator can be collected using an inertial measurement unit (IMU) configured on the actuator.

[0062] Until the robot's execution component completes the expected motion trajectory, a set of discrete position and posture data is obtained. The obtained set of discrete position and posture data can form a predetermined motion trajectory.

[0063] For example, it is assumed that the reference point where the robot's actuator contacts the environment is positioned in the global coordinate system O-XYZ as , which contains three-dimensional position variables and three-dimensional posture variables:

[0064]

[0065] in, Represents the projection of the reference point on the X-axis, Y-axis and Z-axis of the coordinate system O-XYZ; Indicates the attitude angles of rotation around the X, Y, and Z axes.

[0066] Assume that the robot's actuator (i.e., reference point) moves along an arbitrary trajectory, and collects and records the initial reference point pose at the initial time (0t time). ; Every other control cycle (1t time), collect and record the reference point pose of the first control cycle ; After one control cycle (2t time), collect and record the reference point pose of the second control cycle ; ...In the Nth control cycle (Nt time), collect and record the reference point pose of the Nth control cycle Finally, through the collection and recording of each interval control cycle, the reference point pose set is obtained as , using these reference point pose sets , a predetermined motion trajectory can be formed, wherein the reference point pose set Can also be recorded as .

[0067] Similarly, the robot can be controlled periodically. Therefore, the external forces acting on the actuator can be collected during each control cycle, and corresponding control can be performed based on the collected external force data. The control results of the current control cycle, such as the target position, can be used as reference data for the next control cycle.

[0068] Therefore, in step S101, the robot's actuator is controlled to move along a predetermined motion trajectory, and the external force on the robot's actuator is collected every control cycle. For example, in the current control cycle, the external force on the robot's actuator is collected.

[0069] In some embodiments, the robot can collect the external force on the actuator through a signal collection device. For example, the signal collection device can be a force sensor, etc., which is configured on the actuator.

[0070] Step S103 : using the established admittance control model, determining the target position of the robot's actuator in the current control cycle based on the external force.

[0071] In some embodiments, the admittance control model is a virtual model that simulates the actual situation of a controller reaching a predicted target position under the action of an external force based on an input external force. Therefore, the admittance control model considers physical factors between the external force and the target position, such as the effects of inertia, damping, elasticity, and nonlinear forces. Specifically, the admittance control model includes a definite relationship between the comprehensive result of one or more physical parameters of the robot's inertia, damping, elasticity, and nonlinear force and the residual force after the external force overcomes the robot's expected interaction force. Among them, the inertia is related to the acceleration change of the robot's current acceleration relative to the expected acceleration, the damping is related to the speed change of the robot's current speed relative to the expected speed, and the elasticity is related to the position change of the robot's current position relative to the expected position.

[0072] Therefore, in step S103, using the established admittance control model to determine the target position of the robot's actuator in the current control cycle based on the external force may further include:

[0073] First, based on the external force, the target acceleration of the admittance control model in the current control cycle is calculated.

[0074] Next, the target speed in the current control cycle is calculated based on the target acceleration in the current control cycle and the target acceleration and target speed in the previous control cycle.

[0075] Afterwards, the target position in the current control cycle is calculated based on the target speed in the current control cycle and the target speed and target position in the previous control cycle.

[0076] For example, a formula description of an admittance control model is given:

[0077] (1)

[0078] in, represents the actual external force, Represents the target interaction force, which is the interaction force that the robot needs to have when no external force is applied, and needs to overcome the target interaction force when an external force is applied.

[0079] represents the virtual mass parameter;

[0080] represents the virtual damping parameter;

[0081] represents the virtual elastic coefficient;

[0082] 、 Respectively represent the current acceleration, current speed, and current position of the actuator in the robot;

[0083] They represent the expected acceleration, expected velocity, and expected position of the actuator in the robot respectively;

[0084] Represents nonlinear forces such as virtual friction.

[0085] Thus, according to the admittance control model, the target position of the actuator of the robot in the current control cycle is determined based on the external force.

[0086] Taking the above formula (1) as an example, first, when the duration is The robot motion target is calculated in the nth control cycle. When the actual external force is

[0087] Calculate the target acceleration achieved by the external force in the current nth control cycle of the admittance control model. for:

[0088] (2)

[0089] Then, the midpoint integration method is used to calculate the target acceleration in the current nth control cycle. and the target acceleration in the previous n-1 control cycle and target speed , calculate the target speed in the current nth control cycle

[0090] for (3)

[0091] Afterwards, the midpoint integration method is used to calculate the target speed in the current nth control cycle. and the target speed in the previous n-1 control cycle and target location , calculate the target position in the current nth control cycle for:

[0092] (4)

[0093] Step S105 , mapping the determined target position within the current control cycle to the corresponding motion trajectory, and controlling the robot's actuator to move according to the mapping result to achieve compliant control.

[0094] In some embodiments, mapping the determined target position within the current control cycle to the corresponding motion trajectory may specifically include:

[0095] First, distance accumulation values ​​at at least two moments related to the target position in the distance accumulation set are determined by comparison.

[0096] According to the reference point pose set , calculate the distance between the reference point pose at the initial time 0t and the time 1t for:

[0097]

[0098] In this way, the distance between the reference point pose at the initial time 1t and the time 2t is calculated for:

[0099]

[0100] Similarly, calculate the distance between the initial time nt and the reference point pose at the time interval (n-1)t for:

[0101] Therefore, the distance set can be obtained as .

[0102] Then, the distance accumulation set is obtained by summing the distances at different times: ,in, , .

[0103] Next, the position data of the target position mapped onto the motion trajectory is calculated based on the determined cumulative distance values ​​at at least two moments.

[0104] The target position of the admittance control model is obtained from formula (4): , the target position is Mapped to the predetermined motion trajectory.

[0105] Compute and compare target positions for admittance control models The size relationship in the distance accumulation set is obtained, for example, by comparing:

[0106]

[0107] That is, the target position within the distance of the corresponding control cycle.

[0108] Calculating the target position for the admittance control model Map it to the preset motion trajectory to get the pose :

[0109] (5)

[0110] in, equal , expressed as ( ) time and The distance between the reference point poses at the time interval, express( ) and the reference point's pose at the moment The difference in the reference point's position at the moment, it can be seen that, according to formula (5), by changing the target position Mapping to the preset motion trajectory to obtain the pose , control the robot's actuators according to the mapping result pose movement for smooth control.

[0111] The present disclosure further provides a robot control system.

[0112] See also Figure 2 , which is a schematic diagram of the modules of the robot control system in one embodiment of the present disclosure. It should be noted that the principle and technical implementation of the motion control device can refer to the motion control method in the previous embodiment, so it will not be repeated in this embodiment.

[0113] like Figure 2 As shown, the robot control system includes: a motion execution module 201 , a signal acquisition module 203 , an admittance control module 205 , and a motion control module 207 .

[0114] The motion execution module 201 is used to control the motion of the robot. In some embodiments, the motion execution module 201 includes an execution component and a drive motor, and the drive motor can drive the execution component to move.

[0115] The signal acquisition module 203 is configured within the motion execution module 201 and is used to collect external forces. In certain embodiments, when the motion execution module 201 includes an actuator, the signal acquisition module 203 is configured within the actuator to collect external forces applied to the actuator. As previously described, the signal acquisition module 203 can periodically collect and record external forces applied to the actuator according to a corresponding control cycle.

[0116] The admittance control module 205 is configured to determine the target position of the robot using an admittance control model based on the external forces collected by the signal acquisition module 203. In some embodiments, in each control cycle, the target position of the robot's actuator within the current control cycle is determined using the admittance control model based on the external forces applied to the actuator collected by the signal acquisition module 203 during the current control cycle.

[0117] The motion control module 207 is used to map the target position determined in the current control cycle to the corresponding motion trajectory, and output a corresponding control signal to the drive motor to drive the execution component to move according to the mapping result to achieve flexible control.

[0118] It should be noted that in Figure 2 The various functional modules in the embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a program instruction product. The program instruction product includes one or a group of program instructions. When one or a group of program instructions are loaded and executed on a control, the process or function according to the present disclosure is generated in whole or in part. The control can be a general control, a dedicated control, a control network, or other programmable device. The program instructions can be stored in a control-readable storage medium or transmitted from one control-readable storage medium to another control-readable storage medium.

[0119] and, Figure 2The devices disclosed in the embodiments can be implemented using other module division methods. The device embodiments shown above are merely illustrative. For example, the module division is merely a logical functional division. In actual implementation, other division methods may be used, such as a group of modules or modules that can be combined or dynamically integrated into another system, or some features that can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between devices or modules shown or discussed can be through some interface, and the indirect coupling or communication connection between devices or modules can be electrical or other forms.

[0120] in addition, Figure 2 Each functional module and submodule in the embodiments may be dynamically integrated into a single processing component, each module may exist physically independently, or two or more modules may be dynamically integrated into a single component. The aforementioned dynamic components may be implemented in hardware or as software functional modules. If the aforementioned dynamic components are implemented as software functional modules and sold or used as independent products, they may also be stored in a control-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0121] See also Figure 3 , which is a schematic diagram of the circuit structure of the controller disclosed in the present invention.

[0122] The control device may be exemplified by a controller in the robot, or a processing device communicatively connected to the robot, such as a server, a desktop computer, a laptop computer, a tablet computer, a smart phone, or other terminal.

[0123] The control device includes a bus 301, a processor 302, and a memory 303. The processor 302 and the memory 303 can communicate with each other via the bus 301. The memory 303 can store program instructions. The processor 302 executes the program instructions in the memory 303 to implement the steps of the robot control method in the previous embodiment, such as Figure 1 shown.

[0124] Bus 301 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, although only one thick line is used in the figure, this does not necessarily mean that there is only one bus or only one type of bus.

[0125] In some embodiments, processor 302 may be implemented as a central processing unit (CPU), a microprocessor unit (MCU), a system on a chip (SoC), or a field programmable gate array (FPGA). Memory 303 may include volatile memory, such as random access memory (RAM), for temporarily storing data during program execution.

[0126] The memory 303 may also include a non-volatile memory (non-volatile memory) for data storage, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state disk (SSD).

[0127] In certain embodiments, the control device may further include a communication interface 304. The communication interface 304 is used for external communication. In a specific example, the communication interface 304 may include one or a group of wired and / or wireless communication circuit modules. For example, the communication interface 304 may include one or more of a wired network card, a USB module, a serial interface module, and the like. The wireless communication protocols followed by the wireless communication module include, for example, one or more of Near Field Communication (NFC) technology, Infrared (IR) technology, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Bluetooth (BT), and Global Navigation Satellite System (GNSS).

[0128] See also Figure 4, which shows a structural schematic diagram of the robot in an embodiment of the present disclosure.

[0129] As shown in Figure 4 , the robot comprises a motion execution device 401, a signal acquisition device 403, and a control device 405.

[0130] The motion execution device 401 can comprise an execution component for executing an action, which can be sliding, rotating, etc. As an example, the execution component can comprise one or more sliding mechanisms, such as a combination of a sliding rail / slot and a sliding block, a screw rod and a sliding block connected thereto, etc., and one or more shaft rotating mechanisms for rotation.

[0131] As an example, the motion execution device 401 can comprise a motor driver comprising a motion controller (e.g. a PID controller), a current acquisition and control circuit, and a driving motor (e.g. a direct current motor or an alternating current motor).

[0132] The signal acquisition device 403 can comprise a force sensor for acquiring an external force received by the execution component of the robot.

[0133] The control device 405 can be based on Figure 3 The control device 405 in the example can be a controller built into the robot or a processing terminal in communication connection with the robot. The memory of the control device 405 can pre-store the low-dimensional admittance control model and the high-dimensional motion control model to be called when the method is executed.

[0134] The control device 405 can control the motor driver according to the target pose to drive the driving motor to drive the connected execution mechanism (which can drive, for example, an end effector) to output motion and force to the user. For example, the user is actively driven to move, or an interaction force designed in advance by the user is provided during passive movement, such as setting a resistance for rehabilitation training of the user, etc.

[0135] Please refer to Figure 5 , which shows a principle schematic diagram of the admittance motion control of the robot of the present disclosure.

[0136] As shown in Figure 5 , the signal acquisition device 503 of the robot acquires an external force applied by the user to the motion execution device 501 of the robot and outputs a raw signal of the external force. Optionally, the robot further comprises a signal processing circuit 504 for processing the raw signal to obtain a quantized signal. For example, the raw signal is processed by, for example, noise reduction, filtering, etc., and a signal quantization method (e.g. image processing such as gray scale processing, etc.) is performed to obtain a quantized signal recognizable by the control device 505.

[0137] The admittance control model in the control device 505 generates a motion amount feature as a target control amount, such as a target position, according to the quantized signal of the external force. The control device 505 can map the target control amount to a corresponding motion trajectory to obtain a target pose. The control device 505 can generate a motion control signal corresponding to the target pose and send it to the motion execution device 501. The motion execution device 501 controls the robot to achieve the target pose according to the control signal to realize compliant control.

[0138] It should be noted that the flow or method represented by the flowchart of the above-mentioned embodiments of the present disclosure can be understood as representing a module, a segment or a part of code including one or more sets of executable instructions configured to implement a specific logic function or process. And the scope of the preferred embodiments of the present disclosure includes additional implementations, in which the functions can be performed in an order different from that shown or discussed, including in a substantially simultaneous manner or in reverse order according to the functions involved.

[0139] For example, Figure 1 The order of the steps in the method embodiments such as the above can be changed in specific scenarios, and is not limited to the above representation.

[0140] In the embodiments of the present disclosure, a computer-readable storage medium can also be provided, which stores program instructions, and the program instructions are executed to implement the steps in the robot control method in any of the preceding embodiments.

[0141] That is, the method steps in the above-mentioned embodiments are implemented as software or control code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or by original storage in a remote recording medium or non-transitory machine-readable medium downloaded through a network and stored in a local recording medium, so that the method represented herein can be processed by such software on a recording medium using a general-purpose control, a special-purpose processor or programmable or special-purpose hardware (such as ASIC or FPGA).

[0142] In the embodiments of the present disclosure, a computer program product can also be provided, which includes program instructions for executing the steps in the robot control method in any of the preceding embodiments.

[0143] In summary, the disclosed embodiments provide a robot control method, a robot control system, a robot, and a computer-readable storage medium. The robot control method includes: controlling the robot's actuator to move along a predetermined motion trajectory, and collecting external forces acting on the robot's actuator during the current control cycle; the predetermined motion trajectory is composed of a plurality of discrete pose data; utilizing an established admittance control model to determine the target position of the robot's actuator within the current control cycle based on the external forces; mapping the determined target position within the current control cycle to a corresponding motion trajectory, and controlling the robot's actuator to move according to the mapping result. Thus, the target position determined by the admittance control module is mapped to the motion trajectory, and the actuator is controlled to move according to the mapping result, thereby achieving compliant control in human-machine interaction.

[0144] The above embodiments are merely illustrative of the principles and effects of this disclosure and are not intended to limit this disclosure. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this disclosure. Therefore, any equivalent modifications or alterations made by a person skilled in the art without departing from the spirit and technical concepts disclosed herein shall be encompassed by the scope of protection of this disclosure.

Claims

1. A robot control method, characterized in that: include: Controlling the robot's actuator to move along a predetermined motion trajectory, and collecting the external force applied to the robot's actuator during a current control cycle; the predetermined motion trajectory is composed of a plurality of discrete pose data; Determining a target position of an actuator of the robot within a current control cycle based on the external force using the established admittance control model; as well as The target position determined in the current control cycle is mapped to the corresponding motion trajectory, and the robot's actuators are controlled to move according to the mapping results to achieve compliant control.

2. The robot control method according to claim 1, characterized in that: The predetermined motion trajectory is obtained by trajectory recording.

3. The robot control method according to claim 2, characterized in that: The predetermined motion trajectory obtained by trajectory recording includes: Control the robot's actuators or have the operator operate the robot's actuators to move along the expected motion trajectory; Based on the control cycle interval, the posture data of the robot's execution components are collected one by one; the posture data includes position information and posture information; and Until the robot's executive component completes the expected motion trajectory, a set of discrete posture data is obtained; the set of discrete posture data can form a predetermined motion trajectory.

4. The robot control method according to claim 1, wherein: The admittance control model includes: a definite relationship between the comprehensive result of one or more physical parameters of the robot's inertia, damping, elasticity and nonlinear force and the residual force after the external force overcomes the robot's expected interaction force; the inertia is related to the acceleration change of the robot's current acceleration relative to the expected acceleration, the damping is related to the speed change of the robot's current speed relative to the expected speed, and the elasticity is related to the position change of the robot's current position relative to the expected position.

5. The robot control method according to claim 4, characterized in that: Determining the target position of the robot's actuator within a current control cycle based on the external force using the established admittance control model includes: Calculating a target acceleration of the admittance control model in a current control cycle based on the external force; Calculating a target speed in the current control cycle based on the target acceleration in the current control cycle and the target acceleration and target speed in the previous control cycle; and The target position in the current control cycle is calculated based on the target speed in the current control cycle and the target speed and target position in the previous control cycle.

6. The robot control method according to claim 1, characterized in that: Mapping the target position determined in the current control cycle to the corresponding motion trajectory includes: Determine, by comparison, distance accumulation values ​​at at least two moments in the distance accumulation set that are related to the target position; and The position data of the target position mapped onto the motion trajectory is calculated based on the determined distance accumulation values ​​at at least two moments.

7. A robot control system, characterized in that: include: Motion execution module, including execution components and drive motor; a signal acquisition module, configured on the execution component, for acquiring external force applied to the execution component; an admittance control module, configured to determine a target position of the actuator of the robot in the current control cycle based on the external force applied to the actuator acquired by the signal acquisition module in the current control cycle through an admittance control model; as well as The motion control module is used to map the target position determined in the current control cycle to the corresponding motion trajectory, and output a corresponding control signal to the drive motor to drive the execution component to move according to the mapping result to achieve flexible control.

8. The robot control system according to claim 7, characterized in that: It also includes a trajectory recording module for obtaining a predetermined motion trajectory using a trajectory recording method.

9. A robot, characterized in that: include: A motion execution device, comprising an execution component for executing an action; A signal acquisition device, used to acquire the external force applied to the actuator; Control device, including: A processor, a memory and a communication interface; the communication interface is communicatively coupled to the acquisition device and the motion execution device; The memory stores program instructions; The processor is used to run the program instructions to execute the robot control method as described in any one of claims 1 to 6, generate control instructions and send them to the motion execution device through the communication interface, so that the execution components in the motion execution device perform actions according to the control instructions to achieve flexible control.

10. A computer-readable storage medium, characterized in that Program instructions are stored, and when the program instructions are executed, the robot control method according to any one of claims 1 to 6 is executed.

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