CN108333924B - Virtual fixture optimization generation method in operation interaction process - Google Patents

Abstract

The invention relates to a virtual fixture optimization generation method in an operation interaction process, which is characterized in that an optimization method is utilized to optimize and generate a virtual fixture, path points are selected firstly, state variable construction is carried out, and a state equation which the virtual fixture should meet is obtained; giving out a required functional function and a constraint condition; solving the required path track and the virtual clamp parameters; and generating the virtual clamp according to the generated track and the virtual clamp parameters. According to the scheme of the invention, different virtual clamp configuration parameters can be obtained according to the environment where the virtual clamp is located and the characteristics of the virtual clamp, and the method and the device are suitable for more complex task requirements.

Description

Virtual fixture optimization generation method in operation interaction process

Technical Field

The invention belongs to the field of operation interaction control, and relates to a virtual clamp optimization generation method in an operation interaction process, which can be used for assisting an operator in refined operation in a teleoperation process and completing an operation task with higher precision requirement.

Background

Teleoperation techniques and teleoperation systems have been greatly developed over the past few decades. Teleoperated systems can help operators perform a number of very challenging tasks such as disaster relief, unmanned mining, hazardous material handling, remote assistance, telemedicine, and the like.

In the teleoperation process, the tail end mechanism executes an instruction sent by an operator through the interactive system, corresponding operation is carried out, an operation result and the working state of the tail end are fed back, and the tail end state can be conveniently predicted. In the teleoperation process, the refined instruction is generated in a corresponding interaction mode basically depending on the operator, and is limited by the operator to a great extent. In the existing operation modes, a hand controller is used as a main interaction mode, for the control of the position of the tail end, the corresponding operation of the tail end needs to be carried out by depending on the observation of an operator, and for the consideration of human body functions, in the operation task with higher accuracy requirement, the control of the tail end by depending on the operator is not enough. To address this problem and ensure that the operator can complete the task more accurately at a given time, researchers have developed a concept of a virtual fixture.

The virtual clamp is used as a universal guiding mode, and by limiting the motion position of the tail end of the robot, abstract sensory information, force sense, touch sense and other information are generated from a virtual environment and fed back to a main-end operator, so that the auxiliary operator can complete fine operation.

The robot auxiliary system achieves corresponding auxiliary effects by performing constraint action on expected movement of the tail end of the robot. The existing virtual clamp is mainly realized by the following steps: simple function method, proxy point method, potential field method, non-energy-storage constraint method, constraint joint optimization method, reference direction clamp method, mechanical passive constraint implementation method and the like. The generated virtual clamp needs to feed back some sense information to the operator to assist the operator to complete some experiments. For teleoperation, the virtual fixture itself is the tool generated for the convenience of end control, and therefore, the feedback of the virtual fixture and its own influence on the teleoperation process need to be considered.

Disclosure of Invention

Technical problem to be solved

In order to avoid the defects of the prior art, the invention provides an optimal virtual clamp generation method in the operation interaction process, which is used for generating a more efficient virtual clamp for some tasks in the teleoperation process so as to assist an operator to complete the control of a tail end.

Technical scheme

A virtual clamp optimization generation method in an operation interaction process is characterized by comprising the following steps:

step 1: selecting a plurality of path points (x) according to the tasks to be completed by the virtual clamp in the established virtual environment by adopting an interactive tool_i,y_i,z_i)；

Step 2: for a plurality of selected path points (x)_i,y_i,z_i) Constructing the State variables (x)_i,y_i,z_i,k_i) Obtaining a state equation which the virtual clamp should meet;

wherein A (4X4) represents a system matrix, determined by the relationship between states, B (1X4) is an input matrix determining the influence of the input on the system state, C (4X1) is a system observation matrix representing the state to be output, X is the state variable of the whole system, U is the system input, Y is the system output, k is the system output_iCharacteristic parameters of the virtual clamp;

and step 3: according to the obtained state equation, the characteristic parameter k of the virtual clamp_iObtaining a required functional function and a required constraint condition;

the functional function is: target functional function:

hamiltonian: h [ x (t), u (t), lambda (t), t]＝L[x(t),u(t),t]+λ^T(t)f[x(t),u(t),t]

The constraint conditions are as follows: the adjoint equation:

the control equation:

the cross-section conditions are as follows:

wherein λ is a Lagrange multiplier vector;

and 4, step 4: solving the constraint conditions in the step 3 to obtain a path track and virtual fixture parameters;

and 5: generating and setting the virtual clamp according to the generated track and the virtual clamp parameters to complete the configuration of one path point; (ii) a

Step 6: and (5) repeating the steps 2 to 5, and performing virtual fixture setting on all the path points to generate a new virtual fixture.

Advantageous effects

The invention provides a virtual fixture optimization generation method in an operation interaction process, which is characterized in that an optimization method is used for optimizing and generating a virtual fixture, path points are selected firstly, state variable construction is carried out, and a state equation which the virtual fixture should meet is obtained; giving out a required functional function and a constraint condition; solving the required path track and the virtual clamp parameters; and generating the virtual clamp according to the generated track and the virtual clamp parameters.

The virtual fixture optimization generation scheme provided by the invention can obtain different virtual fixture configuration parameters aiming at the environment where the virtual fixture is located and the characteristics of the virtual fixture, and is suitable for more complex task requirements, and compared with the prior art, the virtual fixture optimization generation scheme has the following beneficial effects:

1) the virtual clamp is optimized in consideration of the influence of an operator on the virtual clamp;

2) optimizing the virtual fixture in consideration of the influence of the tail end environment on the virtual fixture;

3) the track of the virtual clamp is optimized by using an optimization method, so that the operation efficiency is improved.

Detailed Description

The invention will now be further described with reference to the examples:

the invention provides a virtual fixture optimization generation strategy based on an optimization method, which aims at solving the problem that how to generate a more efficient virtual fixture to assist an operator to complete a task in different task environments in a space teleoperation task. The method is realized by the following technical scheme:

step 1: selecting waypoints (x) in an established virtual environment using interactive tools_i,y_i,z_i) And storing the obtained data;

step 2: selecting a Path Point (x)_i,y_i,z_i) Constructing a state variable (x)_i,y_i,z_i,k_i) Obtaining a state equation of the system;

wherein A (4X4) represents a system matrix, determined by the relationship between states, B (1X4) is an input matrix determining the influence of the input on the system state, C (4X1) is a system observation matrix representing the state to be output, X is the state variable of the whole system, U is the system input, Y is the system output, k is the system output_iIs a characteristic parameter of the virtual clamp.

And step 3: writing functional functions and constraint conditions of the system according to the system state equation obtained in the step 2 and the model requirement; ensuring the smoothness of the virtual fixture path, ensuring the continuity of the path point track slope, ensuring the direction of the terminal direction vector to be the same as the current initial direction vector, and ensuring k_iRelated to the current position and the virtual clamp itself;

the system state equation:

target functional function:

hamiltonian: h [ x (t), u (t), lambda (t), t]＝L[x(t),u(t),t]+λ^T(t)f[x(t),u(t),t](4)

The adjoint equation:

the control equation:

the cross-section conditions are as follows:

where λ is the lagrange multiplier vector.

And 4, step 4: solving unknown variables in the system state equation according to the equation conditions written in the step 3, and noting that the whole path point is an optimization problem under the end point constraint, therefore, the cross-section condition can be rewritten into

Wherein, t₀Only for the starting time under the segment of the path, not the starting condition under the overall condition, t_fAlso corresponding to the end time of the phase. And solving a stage state function of the stage virtual fixture according to the formula.

And 5: according to the obtained configuration parameters of the virtual clamp, path planning setting is carried out, and meanwhile, virtual force setting can be carried out according to the formula 9;

wherein, K_GVFIs a virtual force parameter, and k_iIn connection with, can be obtained from the state variables of the system, P_dAs actual trajectory path points, P_rThe theoretical track path points are obtained through calculation.

Step 6: and repeating the steps 2-5, and calculating and configuring the virtual fixture of the complete path track to obtain a complete virtual fixture.

Claims (1)

1. A virtual clamp optimization generation method in an operation interaction process is characterized by comprising the following steps:

step 1: selecting a plurality of path points (x) according to the tasks to be completed by the virtual clamp in the established virtual environment by adopting an interactive tool_i,y_i,z_i)；

Step 2: for a plurality of selected path points (x)_i,y_i,z_i) Constructing the State variables (x)_i,y_i,z_i,k_i) Obtaining a state equation which the virtual clamp should meet;

wherein A (4X4) represents a system matrix, determined by the relationship between states, B (1X4) is an input matrix determining the influence of the input on the system state, C (4X1) is a system observation matrix representing the state to be output, X is the state variable of the whole system, U is the system input, Y is the system output, k is the system output_iCharacteristic parameters of the virtual clamp;

and step 3: according to the obtained state equation, the characteristic parameter k of the virtual clamp_iObtaining a required functional function and a required constraint condition;

the functional function is: target functional function:

hamiltonian: h [ x (t), u (t), lambda (t), t]＝L[x(t),u(t),t]+λ^T(t)f[x(t),u(t),t]

The constraint conditions are as follows: the adjoint equation:

the control equation:

the cross-section conditions are as follows:

wherein λ is a Lagrange multiplier vector;

and 4, step 4: solving the constraint conditions in the step 3 to obtain a path track and virtual fixture parameters;

and 5: according to the obtained virtual clamp configuration parameters, path planning setting is carried out, and meanwhile, virtual force setting can be carried out according to the following formula;

wherein, K_GVFIs a virtual force parameter, and k_iIn connection with, can be obtained from the state variables of the system, P_dAs actual trajectory path points, P_rCalculating the obtained theoretical track path points;

step 6: and (5) repeating the steps 2 to 5, and performing virtual fixture setting on all the path points to generate a new virtual fixture.