A robot teleoperation safety control method and system based on operation intention recognition and adaptive control obstacle function

Abstract

The present invention belongs to the technical field related to robot teleoperation, and discloses a robot teleoperation safety control method and system that uses operation intention recognition and adaptive control obstacle functions. The method includes: establishing a virtual agent between the teleoperated master robot and slave robot; calculating the nominal control input of the virtual agent, converting the nominal control input into a safety control input using an adaptive control obstacle function as a constraint, and using the safety control input to control the virtual agent to track the master robot according to a preset desired tracking trajectory; setting the slack variables in the adaptive control obstacle function according to the operator's interaction intention with the target; calculating the expected control input of the slave robot, and using this expected control to control the slave robot to track the virtual agent, thereby achieving teleoperation control of the slave robot by the master robot. The present invention solves the problem of being unable to achieve safety control functions for different operator interaction intentions with the target within a unified control framework.

Description

Technical Field

[0001] The present invention belongs to the technical field related to robot teleoperation, and more specifically, relates to a robot teleoperation safety control method and system for operation intention recognition and adaptive control of obstacle functions. Background Art

[0002] In the field of robotic teleoperation, safety control can effectively prevent accidental collisions and equipment damage caused by operator errors. Commonly used obstacle avoidance methods include artificial potential fields and controlled obstacle functions. However, the artificial potential field method lacks optimization in obstacle avoidance path generation, and its effectiveness relies on the design of the potential field function. While the controlled obstacle function method can generate an optimal obstacle avoidance path, it cannot identify the operator's true interaction intent and can only perform obstacle avoidance for all targets.

[0003] In fact, during robot teleoperation, operators want to flexibly and freely perform contact and obstacle avoidance actions when facing interactive objects. In short, existing safety control methods cannot simultaneously achieve obstacle avoidance and contact functions within a unified control framework. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a robot remote operation safety control method and system for operation intention recognition and adaptive control obstacle function, which solves the problem of being unable to realize the safety control of the operator's different interactive intention functions to the target under a unified control framework.

[0005] To achieve the above objectives, according to one aspect of the present invention, a robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle function is provided, characterized in that the method comprises the following steps:

[0006] Establishing a virtual agent between the teleoperated master and slave robots;

[0007] Calculating a nominal control input for the virtual agent, converting the nominal control input into a safety control input using an adaptive control obstacle function as a constraint, and utilizing the safety control input to control the virtual agent to track the master robot according to a preset desired tracking trajectory; wherein the slack variables in the adaptive control obstacle function are set according to the operator's interaction intention with the target;

[0008] The expected control input of the slave robot is calculated and used to control the slave robot to follow the virtual agent, thereby realizing teleoperation control of the slave robot by the master robot.

[0009] Further preferably, the dynamic model of the virtual agent is as follows:

[0010]

[0011] Among them, p d and is the expected position and expected velocity of the virtual agent, M p is the inertia matrix of the virtual agent, u is the safety control input of the virtual agent, n is the degree of freedom of the system, is the system state derivative, f(x) is the input-independent variable, and g(x) is the input-dependent variable.

[0012] Further preferably, the nominal control input is calculated using a position-force controller, and the calculation formula is as follows:

[0013] u no =u imp +u f

[0014]

[0015] u f =π(K fp (F r -F ext )+K fi ∫(F r -F ext )dt)+F ext

[0016] Among them, u no is the nominal control input of the virtual agent, u imp is the impedance control input, u f is the force control input, K p 、D p are the position-related proportional and differential control gains, p r , are the reference position, velocity, acceleration, K fp , K fi are the proportional and integral control gains related to force, F r is the reference interaction force, π is an activation function, F ext For external environmental forces.

[0017] Further preferably, the desired control input of the slave robot is calculated using a proportional-differential controller, and the calculation formula is as follows:

[0018]

[0019] Among them, K q 、D q are the proportional-derivative control gains in the joint space, q and are the robot joint angles and angular velocities, q dis the virtual agent position in the joint space.

[0020] Further preferably, the formula for the safety input is as follows:

[0021]

[0022] Among them, u no is the nominal control input of the virtual agent, u s is the security input, h(x) is the security certificate describing the variable constraints, is a fixed variable, Δh is a slack variable, is the derivative of the security certificate, ▽hf(x) is the input-independent Lie derivative component, ▽hg(x)u s is the input-related Lie derivative component ▽hg(x)u s , α is a Class function.

[0023] Further preferably, the calculation of the slack variable in the adaptive control function is designed according to the following steps:

[0024] Determine the operator's interaction intention with the target, which includes obstacle avoidance intention and contact intention;

[0025] Calculating a time-varying weight of the interaction intention in the time domain;

[0026] The slack variables are calculated using the time-varying weights.

[0027] Further preferably, the determining of the operator's interaction intention with the target is performed according to the following steps:

[0028] Determine whether the first event is triggered, when h(x)≤h ref , the first event is triggered;

[0029] Determine whether the second event is triggered. The second event is triggered;

[0030] Determine whether the third event is triggered, when p d ≤p r , the third event is triggered;

[0031] When the first event, the second event, and the third event are all triggered, the operator's interaction intention with the target is a contact intention, otherwise it is an obstacle avoidance intention;

[0032] Where h(x) is the security certificate describing the variable constraint, h ref is the preset reference constraint boundary, u no is the nominal control input of the virtual agent, u s For safety input, u refis the preset reference ratio, p r The default reference position of the virtual agent.

[0033] Further preferably, the time-varying weight is calculated according to the following formula:

[0034] When the operator's interaction intention with the target is contact intention, the time-varying weight is calculated according to the following formula:

[0035] TBRS k-1 =1-exp(-r*k)

[0036] TBRS k =(1-TBRS0)*TBRS k-1 +TBRS0

[0037] When the operator's interaction intention with the target is obstacle avoidance, the time-varying weight is calculated according to the following formula:

[0038] TBRS k =TBRS k-1 -(1-D)*TBRS k-1 *Δk

[0039] Among them, TBRS0, TBRS k-1 TBRS k are the time-varying weights at the initial moment, the previous control cycle, and the current control cycle, respectively. r is the rate of increase of the time-varying weights. k is the control cycle. D describes the rate of weight decay. Δk is the unit control cycle length.

[0040] Further preferably, the slack variable is calculated according to the following formula:

[0041] Δh=Δh+β

[0042]

[0043] Among them, Δh is the slack variable, β is the change of the slack variable in each iteration cycle, TBRS k is the time-varying weight of the current control cycle.

[0044] According to another aspect of the present invention, a robot teleoperation safety control system for operation intention recognition and adaptive control of obstacle functions is provided, which includes an actuator for executing the above-mentioned robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle functions.

[0045] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0046] 1. This invention sets up a virtual agent between the master and slave robots. The virtual agent tracks the master robot, and the slave robot tracks the virtual agent. Simultaneously, when the virtual agent tracks the master robot, slack variables in the adaptive control obstacle function are set according to the operator's interaction intention with the target. This allows the master robot to be tracked under different interaction intentions, thereby achieving safe control of the operator's different interaction intentions with the target within a unified control framework.

[0047] 2. The virtual agent used in this invention replaces the safety constraint object from the slave robot with a preset virtual agent, avoiding the impact of the slave robot model uncertainty on the safety constraint and effectively improving the control performance;

[0048] 3. When solving the slack variables of the adaptive obstacle control function, the present invention first determines the operator's interaction intention with the target. Then, time-varying weights are calculated based on the interaction intentions. The slack variables are calculated using the time-varying weights, resulting in different slack variables and adaptive obstacle control function constraints corresponding to different interaction intentions. This innovation can change the degree of safety constraints in real time based on the operator's intentions, allowing for flexible switching between obstacle avoidance and approach actions.

[0049] 4. When controlling the virtual agent to track the master robot, the present invention converts the nominal control input into a safety control input, so that the slave robot can avoid preset obstacle targets in the space while tracking the positions of the virtual agent and the master robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is based on a preferred embodiment of a robot teleoperation safety control framework diagram based on operation intention recognition and adaptive control obstacle function.

[0051] Figure 2 This is a remote operation interaction experiment site according to a preferred embodiment of the present invention, wherein the interaction objects are three space balls.

[0052] Figure 3 This is a teleoperation interaction experiment trajectory according to a preferred embodiment of the present invention, wherein the operation intention of ball a is recognized as obstacle avoidance, the operation intention of ball b is recognized as obstacle avoidance first and then contact, and the operation intention of ball c is recognized as contact. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0054] like Figure 1 As shown in the figure, a robot teleoperation safety control framework based on operation intention recognition and adaptive control obstacle function includes the following steps:

[0055] S1 establishes the dynamic model of the virtual agent and the teleoperated robot. The virtual agent is controlled by reducing the 6 degrees of freedom of the slave robot to 3 degrees of freedom to avoid the influence of the uncertainty of the slave robot model.

[0056] Build dynamic models of virtual agents and teleoperated robots.

[0057] Step S11: First, establish the dynamic model of the virtual agent as follows:

[0058]

[0059] where pd and is the expected position and expected velocity of the virtual agent, Mp is the inertia matrix of the virtual agent, u is the safety control input of the virtual agent, and n is the degree of freedom of the system. represents the system state derivative, f(x) represents the input-independent variable, and g(x) represents the input-dependent variable.

[0060] Step S12: The dynamic model of the teleoperated robot is:

[0061]

[0062] where q, are the robot joint angle, angular velocity, and angular acceleration respectively, Mq is the robot inertia matrix, Cq is the centrifugal force and Coriolis force matrix, G is the gravity matrix, J is the Jacobian matrix, τc is the robot control torque, and Fext is the environmental interaction force.

[0063] S2 uses the position-force controller to obtain the nominal control input required for the virtual agent to track the desired trajectory. Using the adaptive control obstacle function as a constraint, the nominal control input is converted into a safety control input. This safety control input is used to control the virtual agent to track the master robot according to the desired trajectory. S21 uses the impedance-force controller to calculate the nominal control input of the virtual agent as:

[0064] uno=uimp+uf

[0065]

[0066] uf=π(Kfp(Fr-Fext)+Kfi∫(Fr-Fext)dt)+Fext

[0067] Where uno is the nominal control input of the virtual agent, which includes the impedance control input uimp and the force control input uf. In calculating the impedance control input, Kp and Dp are the position-related proportional and differential control gains, respectively. r , are the reference position, velocity, and acceleration respectively. In calculating the force control input, K fp , K fi are the proportional and integral control gains related to force, F r is the reference interaction force, and π is an activation function:

[0068]

[0069] π ensures that the desired position and the reference position are less than the activation distance d act The proportional-derivative term of the force controller takes effect only when

[0070] The specific process of converting S22 nominal control input into safety control input is as follows:

[0071] (1) Establish variable safety constraints using an adaptive control barrier function containing slack variables.

[0072] An adaptive control barrier function is constructed using slack variables and variable safety constraints, and the nominal control input is converted into a safety control input using the adaptive control barrier function. The slack variables are calculated from the interaction intention and its time-varying weight in the time domain.

[0073] The variable safety constraint established using the adaptive control barrier function is:

[0074]

[0075] where h(x) is a security certificate describing the variable constraint, which contains the fixed variables and slack variable Δh. is the derivative of the security certificate, which contains the input-independent Lie derivative component ▽hf(x) and the input-dependent Lie derivative component ▽hg(x)u s α is a Class function.

[0076] The quadratic programming algorithm can be used to calculate the control input u that meets the safety constraints and is closest to the nominal control input u no Safety control input u s :

[0077]

[0078] Quadratic programming ensures that the safety control input obtained in each control cycle is optimal.

[0079] (2) An event-triggered algorithm is used to determine whether the operator's interaction intention with the target is an obstacle avoidance intention or a contact intention.

[0080] A set of event triggering conditions affected by the current state of the system are introduced to determine whether the current operator's operation intention is obstacle avoidance intention or contact intention.

[0081] The first event is: whether the safety certificate in the adaptive control barrier function is close enough to the preset reference constraint boundary. If so, the first event is triggered, otherwise, it is not triggered;

[0082] h(x)≤h ref ?

[0083] where h ref is the reference constraint boundary, h ref This determines the trigger sensitivity of Event 1 and can be determined using a small amount of experimental testing.

[0084] The second event is: whether the ratio of the safety control input to the nominal control input is sufficiently smaller than a preset reference ratio; if so, the second event is triggered; otherwise, it is not triggered;

[0085]

[0086] where u ref is the reference ratio, u ref The lower it is, the more the nominal control input is affected by the safety constraints, which in turn indicates that the operator has a tendency to fight against the safety algorithm, which manifests as contact intention.

[0087] The third event is: whether the expected position of the virtual agent has crossed the preset reference position, if yes, the third event is triggered, otherwise, it is not triggered;

[0088] p d ≤p r ?

[0089] The third event increases the robustness of the operation intention judgment on the basis of the first and second events, and suppresses the instantaneous misjudgment caused by sensor noise.

[0090] Combining these three events, an event-triggered algorithm is proposed, which uses a combination of logical judgments to determine whether the current operation intention is an obstacle avoidance intention or a contact intention:

[0091] If the first event, the second event, and the third event are all triggered, the operation intention is a contact intention;

[0092] In other cases, the operation intention is to avoid obstacles.

[0093] (3) Use the time resource sharing theory to quantify the time-varying weights of interaction intentions in the time domain.

[0094] The temporal resource sharing algorithm quantifies the interaction intention as a time-varying weight. If the intention is to contact, the time-varying weight in the rising state can be calculated as follows:

[0095] TBRS k-1 =1-exp(-r*k)

[0096] TBRS k =(1-TBRS0)*TBRS k-1 +TBRS0

[0097] Among them, TBRS0, TBRS k-1 TBRS k are the time-varying weights at the initial moment, the previous control cycle, and the current control cycle, r describes the rate of increase of the weight, and k is the control cycle.

[0098] If the intention is to avoid obstacles, the time-varying weight can be calculated as follows:

[0099] TBRS k =TBRS k-1 -(1-D)*TBRS k-1 *Δk

[0100] Where D describes the weight decay rate, and Δk is the unit control cycle length.

[0101] The essence of the temporal resource sharing algorithm is that when a certain operation intention (obstacle avoidance or contact) is detected by time, the operator will not immediately perform the corresponding action, but will gradually increase or decrease his attention over time until the time-varying weight reaches a certain threshold.

[0102] (4) Calculate the slack variables under the influence of time-varying weights according to the iterative learning algorithm, calculate the virtual agent safety control input in S1 under the influence of the slack variables, and realize safety control.

[0103] After obtaining the time-varying weights and trigger thresholds calculated by the time resource sharing theory, iterative learning can be used to calculate the changes in the slack variables of the current control cycle:

[0104]

[0105] Δh=Δh+β

[0106] Where β is the change of the slack variable in each iteration cycle, and one interaction intention judgment is considered as one iteration cycle. k It is a normalized value in the range of (0,1), and the trigger threshold can be selected as 0.5.

[0107] Add saturation constraints to the slack variables to increase the smoothness of the changes:

[0108]

[0109] where Δh min and Δh max are the minimum and maximum slack variables, respectively.

[0110] S3 uses a proportional-derivative controller to calculate the desired control input required for the slave robot to track the virtual agent. This proportional-derivative controller calculates the desired control input, which is then used to control the slave robot to track the virtual agent, thereby achieving teleoperation control of the slave robot by the master robot.

[0111] The desired control input of the teleoperated robot is calculated based on the proportional-derivative controller:

[0112]

[0113] where K q 、D q is the proportional-derivative control gain in the joint space. Since the virtual agent adopts Cartesian space position control, and the teleoperated robot adopts joint space position control, Figure 1 It is shown that a kinematic inverse solution module needs to be added from the virtual agent to the teleoperated space robot.

[0114] In order to verify the effectiveness of the proposed operation intention recognition and adaptive control obstacle function, the accuracy verification was carried out using the embodiment. For this embodiment, a teleoperated robot ball obstacle avoidance and grasping task platform was built for experimental verification. Figure 2 As shown, the system consists of a Virtuose6D TAO master robot, Franka, a slave robot, a gripper, and three balls. During the experimental test, the operator controlled Frank's robotic arm to move the gripper through the center points of the three balls in space, ensuring that the time spent near the balls increased.

[0115] When the method proposed in the present invention is used to perform the remote-controlled robot ball avoidance and grasping tasks, the proposed method can correctly identify the operator's operating intention and achieve the unity of obstacle avoidance and contact functions. Figure 3As shown in the figure, the virtual agent's position is successfully tracked from the robot's position. Since the first ball has the shortest residence time, it is identified as an obstacle avoidance intention. The slack variables remain unchanged, indicating that the safety constraint sphere radius does not decrease. The ball (red dashed line) is enclosed in the safe zone, and the control system demonstrates obstacle avoidance. The second ball has a moderate residence time, falling into the transition zone between obstacle avoidance and contact intention. The slack variables increase appropriately, indicating that the safety constraint sphere radius decreases to near the ball (red dashed line), and the control system demonstrates the critical function of obstacle avoidance and contact. The third ball has the longest residence time, indicating a contact intention. The slack variables increase significantly, indicating that the safety constraint sphere radius decreases significantly. The ball (red dashed line) is exposed outside the safe zone, and the control system demonstrates contact.

[0116] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A robot teleoperation safety control method based on operation intention recognition and adaptive control of obstacle function, characterized in that: The method comprises the following steps: Establishing a virtual agent between the teleoperated master and slave robots; Calculating a nominal control input for the virtual agent, converting the nominal control input into a safety control input using an adaptive control obstacle function as a constraint, and utilizing the safety control input to control the virtual agent to track the master robot according to a preset desired tracking trajectory; wherein the slack variables in the adaptive control obstacle function are set according to the operator's interaction intention with the target; Calculating the expected control input of the slave robot and using the expected control input to control the slave robot to follow the virtual agent, thereby realizing teleoperation control of the slave robot by the master robot; The dynamic model of the virtual agent is as follows: in, and is the expected position and expected velocity of the virtual agent, is the virtual agent inertia matrix, For the security control input of the virtual agent, is the system degree of freedom, is the system state derivative, is an input-independent variable, is the input related variable; The nominal control input is calculated using the position-force controller using the following formula: in, is the nominal control input of the virtual agent, is the impedance controlled input, is the force control input, 、 are the position-related proportional and differential control gains, , , are the reference position, velocity, and acceleration, respectively. 、 are the force-related proportional and integral control gains, is the reference interaction force, is an activation function, F ext For external environmental forces; The desired control input from the robot is calculated using a proportional-differential controller, and the calculation formula is as follows: in, 、 is the proportional-derivative control gain in the joint space, and are the robot joint angles and angular velocities, q d is the virtual agent position in the joint space; The formula for the safety control input is as follows: in, is the nominal control input of the virtual agent, u s For safety input, To describe the security certificate of variable constraints, is a fixed variable, is the slack variable, is the derivative of the security certificate, is the input-independent Lie derivative component, is the input-related Lie derivative component , For a Class function.

2. A robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle function as described in claim 1, characterized in that: The calculation of the slack variables in the adaptive control barrier function is designed according to the following steps: Determine the operator's interaction intention with the target, which includes obstacle avoidance intention and contact intention; Calculating a time-varying weight of the interaction intention in the time domain; The slack variables are calculated using the time-varying weights.

3. The robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle function according to claim 1, characterized in that: The operator's interaction intention with the target is carried out according to the following steps: Determine whether the first event is triggered, then , the first event is triggered; Determine whether the second event is triggered. , the second event is triggered; Determine whether the third event is triggered. , the third event is triggered; When the first event, the second event, and the third event are all triggered, the operator's interaction intention with the target is a contact intention, otherwise it is an obstacle avoidance intention; in, To describe the security certificate of variable constraints, h ref To preset reference constraint boundaries, is the nominal control input of the virtual agent, u s For safety input, u ref is the preset reference ratio, p r The default reference position of the virtual agent.

4. A robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle function as described in claim 2, characterized in that: The time-varying weight is calculated according to the following formula: When the operator's interaction intention with the target is contact intention, the time-varying weight is calculated according to the following formula: When the operator's interaction intention with the target is obstacle avoidance, the time-varying weight is calculated according to the following formula: in, 、 、 are the time-varying weights at the initial moment, the previous control cycle, and the current control cycle, respectively. is the time-varying weight rising rate, To control the cycle, Describes the weight decay rate, The unit controls the cycle length.

5. The robot teleoperation safety control method for operation intention recognition and adaptive control of obstacle function according to claim 1, characterized in that: The slack variable is calculated according to the following formula: in, is the slack variable, is the change in the slack variable in each iteration cycle, is the time-varying weight of the current control cycle.

6. A robot teleoperation safety control system with operation intention recognition and adaptive control of obstacle functions, characterized in that: The system includes an actuator, which is used to execute the robot teleoperation safety control method for operation intention recognition and adaptive control obstacle function as described in any one of claims 1 to 5.

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