Interactive control method and system with frequency adaptation ability

Abstract

The present invention relates to the field of robot motion control and provides an interactive control method and system with frequency adaptability, comprising: a robot obtaining a user's position trajectory and calculating a velocity time series based on the user's position trajectory; constructing a motion model and a feedback controller based on the velocity time series; integrating the motion model and the feedback controller to obtain an integrated control model; introducing frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptability; and controlling the robot to perform follow-up motion using the integrated control model with frequency adaptability. The present invention considers how the robot can make corresponding adjustments to achieve frequency adaptation and achieve dynamic equilibrium with the participant's motion frequency under conditions of human-machine interactive motion collaboration, when the participant's motion frequency changes. Furthermore, this adaptation is long-term and stable, resulting in a better robot following effect.

Description

Technical Field

[0001] The present invention relates to the field of robot motion control, and in particular to an interactive control method and system with frequency adaptability. Background Art

[0002] In human-robot interactions, individuals often need to collaborate with robots to complete a task, requiring the robot to adapt to the individual's movements. Similar to effective interpersonal interaction, the effectiveness of human-robot interaction in this context depends on the ability of the artificial agent to respond and adapt naturally to the actions of its human collaborator. A growing body of research suggests that a wide range of goal-oriented human perceptual motor behaviors are comprised of only two basic types of movement: discrete and rhythmic. The significance of this research lies in the fact that the dynamics of human perceptual behavior can be modeled by combining the fundamental behaviors of two fundamental dynamical systems: point attractors and limit cycles.

[0003] The movement of robots designed with current technology does not take into account the learning and adaptation of movement frequency. This makes the selection of the initial movement frequency of virtual players more demanding. It must be as close as possible to the movement frequency of ordinary people, and it is impossible to adjust the movement according to changes in the movement frequency of human collaborators, which will reduce the efficiency of interactive collaborative tasks. In addition, current technology does not take into account the human-like nature of robot movement, which will also lead to a poor experience for human collaborators in collaborative movement.

[0004] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an interactive control method with frequency adaptability, comprising:

[0006] S1: The robot obtains the user's position trajectory and calculates the velocity time series based on the user's position trajectory;

[0007] S2: constructing a motion model and feedback controller based on the velocity time series;

[0008] S3: Integrate the motion model and the feedback controller to obtain an integrated control model;

[0009] S4: Introducing frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability;

[0010] S5: Control the robot to perform following motion through the integrated control model with frequency adaptability.

[0011] Preferably, in step S1, the expression of the speed time series is:

[0012]

[0013] Among them, t is the running time, h is the sampling time interval, x hp (t) is the current location of the user at time t, x hp (t+h) is the user's current location at time t+h, is the user's current speed at time t.

[0014] Preferably, in step S2:

[0015] The expression of the motion model is:

[0016]

[0017] The expression of the feedback controller is:

[0018]

[0019] Where u is the feedback controller, x vp is the current position of the robot, is the current speed of the robot, is the current acceleration of the robot, x hp is the user's current position, α, β and γ are control parameters, ω represents the frequency at which the robot needs to adapt, and k1 represents the state feedback coefficient.

[0020] Preferably, in step S3, the expression of the integrated control model is:

[0021]

[0022] in, To integrate the control model, ω0 represents the eigenfrequency, ω represents the frequency that the robot needs to adapt to, κ represents the coupling strength, F(t) represents the time-periodic disturbance caused by the user's movement on the robot, v is the relaxation parameter, x1 is the first state variable of the oscillator in rectangular coordinates, and x2 is the second state variable of the oscillator in rectangular coordinates.

[0023] Preferably, in step S4, the expression of the integrated control model with frequency adaptability is:

[0024]

[0025]

[0026] in, To integrate the control model, ω0 represents the eigenfrequency, ω represents the frequency that the robot needs to adapt to, κ represents the coupling strength, F(t) represents the time period disturbance caused by the user's motion on the robot, v is the relaxation parameter, is the first state variable of the oscillator in rectangular coordinates, x2 is the second state variable of the oscillator in rectangular coordinates, is the derivative of x1, is the derivative of x2, fx1 and fx2 represent the state functions of the two state variables respectively.

[0027] An interactive control system with frequency adaptation capability, comprising:

[0028] A speed time series acquisition module is used by the robot to obtain the user's position trajectory and calculate the speed time series based on the user's position trajectory;

[0029] A model building module, configured to build a motion model and a feedback controller using the velocity time series;

[0030] A model integration module, configured to integrate the motion model and the feedback controller to obtain an integrated control model;

[0031] A frequency adaptation introduction module, configured to introduce frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability;

[0032] The following motion module is used to control the robot to perform following motion through the integrated control model with frequency adaptability.

[0033] The present invention has the following beneficial effects:

[0034] The present invention takes into account the changes in the participant's movement frequency under the conditions of human-computer interactive motion coordination, and how the robot should make corresponding adjustments to achieve frequency adaptation and reach a dynamic balance with the participant's movement frequency. This adaptation is long-term and stable. Even if the input signal, that is, the participant's movement information, stops being input, the robot can still maintain the adapted frequency to move, making the robot's following effect better. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a method according to an embodiment of the present invention;

[0036] Figure 2 For the first set of human and robot position and velocity time series;

[0037] Figure 3 Position and velocity time series for the second set of humans and robots;

[0038] Figure 4 is the statistical analysis result of RMSE (time correspondence) value;

[0039] Figure 5 is the statistical analysis result of CV (coordination level) value;

[0040] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] Reference Figure 1 The present invention provides an interactive control method with frequency adaptability, comprising:

[0043] S1: The robot obtains the user's position trajectory and calculates the velocity time series based on the user's position trajectory;

[0044] Specifically, the user's position trajectory refers to the position trajectory of the user's hand, which performs one-dimensional movements with the hand and is required to be as complete and smooth as possible;

[0045] S2: constructing a motion model and feedback controller based on the velocity time series;

[0046] S3: Integrate the motion model and the feedback controller to obtain an integrated control model;

[0047] S4: Introducing frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability;

[0048] S5: Control the robot to perform following motion through the integrated control model with frequency adaptability.

[0049] Furthermore, in step S1, the robot obtains the user's position trajectory to simulate the human visual system, sends the position trajectory to the robot's processing module, and estimates its corresponding velocity time series using the difference method;

[0050] The expression of the velocity time series is:

[0051]

[0052] Among them, t is the running time, h is the sampling time interval, x hp (t) is the current location of the user at time t, x hp (t+h) is the user's current location at time t+h, is the user's current speed at time t.

[0053] Furthermore, in step S2, the robot adjusts its motion amplitude and frequency according to the current state (mainly position and speed) error, generates a motion model and feedback controller to follow the user's motion trajectory:

[0054] The expression of the motion model is:

[0055]

[0056] The expression of the feedback controller is:

[0057]

[0058] Where u is the feedback controller, x vp is the current position of the robot, is the current speed of the robot, is the current acceleration of the robot, x hp is the user's current position, α, β, and γ are control parameters (such as stiffness and damping related to empirically measured properties of human motion), ω represents the frequency at which the robot needs to adapt, and k1 is the state feedback coefficient.

[0059] Furthermore, in step S3, the robot integrates the motion model and the feedback controller to obtain the actual motion (simulating the neuromuscular system of the human body), i.e., the integrated control model;

[0060] The expression of the integrated control model is:

[0061]

[0062] in, To integrate the control model, ω0 represents the eigenfrequency (i.e., the initial frequency of the robot), ω represents the frequency to which the robot needs to adapt, κ represents the coupling strength (the positive or negative value of κ depends on the rotation direction of the state variable in the two-dimensional term space), F(t) represents the time-periodic disturbance caused by the user's movement on the robot, v is the relaxation parameter, x1 is the first state variable of the oscillator in rectangular coordinates, and x2 is the second state variable of the oscillator in rectangular coordinates.

[0063] Furthermore, in step S4, after frequency adaptation is introduced into the integrated control model, the parameter ω is transformed into a new state variable with its own time-varying dynamics;

[0064] The expression of the integrated control model with frequency adaptability is:

[0065]

[0066]

[0067] in, To integrate the control model, ω0 represents the eigenfrequency, ω represents the frequency that the robot needs to adapt to, κ represents the coupling strength, F(t) represents the time period disturbance caused by the user's motion on the robot, v is the relaxation parameter, x1 is the first state variable of the oscillator in rectangular coordinates, x2 is the second state variable of the oscillator in rectangular coordinates, is the derivative of x1, is the derivative of x2, fx1 and fx2 represent the state functions of the two state variables respectively.

[0068] In order to verify the effectiveness of the control method of the present invention, the following experiment is set up:

[0069] Step 1: Set up four groups of players and have two real people play an interactive game. Collect their motion trajectories and calculate two performance indicators. Repeat this five times and take the average value.

[0070] Step 2: Use the robot driven by the control method designed by the present invention to replace one of the human players, collect the motion trajectories of the two and calculate the performance indicators, repeat 5 times and take the average value;

[0071] Step 3: Compare the results obtained by the HP-HP pair and the VP-HP pair to verify the effectiveness of the control method of the present invention. The position and velocity time series of the human and the robot under specific conditions are obtained as follows: Figure 2-3 As shown in the figure, it can be seen that the robot's following motion is very stable.

[0072] After long-term interaction with robots, users will learn the ability to perceive and imitate the behavior of others, that is, social ability. To verify this, we designed a series of performance indicators to measure people's recovery, such as Figure 4 、 5 As shown;

[0073]

[0074] Among them, x vp,i is the position of the robot’s i-th sampling point in the x direction; y vp,i is the position of the robot’s i-th sampling point in the y direction; x hp,i is the position of the i-th sampling point in the x direction; y hp,i is the position of the i-th sampling point of the person in the y direction; RMSE measures the temporal correspondence of the two trajectories. The lower the RMSE value, the better the temporal correspondence.

[0075]

[0076] Where ΔΦ iIt is the phase difference between the motion trajectory of the human and the robot at the i-th sampling point; CV measures the motion coordination level of the two trajectories. The higher the CV value, the higher the motion coordination level.

[0077] Combined with the comparison of the movement trajectories of users after playing interactive games, the effectiveness of the control method designed by the present invention is verified.

[0078] The present invention provides an interactive control system with frequency adaptability, comprising:

[0079] A speed time series acquisition module is used by the robot to obtain the user's position trajectory and calculate the speed time series based on the user's position trajectory;

[0080] A model building module, configured to build a motion model and a feedback controller using the velocity time series;

[0081] A model integration module, configured to integrate the motion model and the feedback controller to obtain an integrated control model;

[0082] A frequency adaptation introduction module, configured to introduce frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability;

[0083] The following motion module is used to control the robot to perform following motion through the integrated control model with frequency adaptability.

[0084] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0085] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent superiority or inferiority of the embodiments. In a unit claim that lists several means, several of these means may be embodied by the same item of hardware. The use of the terms first, second, and third, etc., does not denote any order and should be construed as identifiers.

[0086] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An interactive control method with frequency adaptability, characterized in that: include: S1: The robot obtains the user's position trajectory and calculates the velocity time series based on the user's position trajectory; S2: constructing a motion model and feedback controller based on the velocity time series; S3: Integrate the motion model and the feedback controller to obtain an integrated control model; S4: Introducing frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability; S5: controlling the robot to perform following motion by using the integrated control model with frequency adaptability; In step S2: The expression of the motion model is: The expression of the feedback controller is: Where u is the feedback controller, x vp is the current position of the robot, is the current speed of the robot, is the current acceleration of the robot, x hp is the user's current position, α, β and γ are control parameters, ω represents the frequency at which the robot needs to adapt, and k1 represents the state feedback coefficient; In step S3, the expression of the integrated control model is: in, To integrate the control model, ω0 represents the eigenfrequency, ω represents the frequency that the robot needs to adapt to, κ represents the coupling strength, F(t) represents the time period disturbance caused by the user's motion on the robot, v is the relaxation parameter, x1 is the first state variable of the oscillator in rectangular coordinates, and x2 is the second state variable of the oscillator in rectangular coordinates; In step S4, the expression of the integrated control model with frequency adaptability is: in, To integrate the control model, ω0 represents the eigenfrequency, ω represents the frequency that the robot needs to adapt to, κ represents the coupling strength, F(t) represents the time period disturbance caused by the user's motion on the robot, v is the relaxation parameter, x1 is the first state variable of the oscillator in rectangular coordinates, x2 is the second state variable of the oscillator in rectangular coordinates, is the derivative of x1, is the derivative of x2, fx1 and fx2 represent the state functions of the two state variables respectively.

2. The interactive control method with frequency adaptability according to claim 1, characterized in that: In step S1, the expression of the speed time series is: Among them, t is the running time, h is the sampling time interval, x hp (t) is the current location of the user at time t, x hp (t+h) is the user's current location at time t+h, is the user's current speed at time t.

3. An interactive control system with frequency adaptability, used to implement the interactive control method with frequency adaptability according to any one of claims 1 to 2, characterized in that: include: A speed time series acquisition module is used by the robot to obtain the user's position trajectory and calculate the speed time series based on the user's position trajectory; A model building module, configured to build a motion model and a feedback controller using the velocity time series; A model integration module, configured to integrate the motion model and the feedback controller to obtain an integrated control model; A frequency adaptation introduction module, configured to introduce frequency adaptation into the integrated control model to obtain an integrated control model with frequency adaptation capability; The following motion module is used to control the robot to perform following motion through the integrated control model with frequency adaptability.

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