CN112035992A - Autonomous remote control underwater robot sharing control method based on multi-objective optimization - Google Patents
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Abstract
The invention relates to an autonomous remote control underwater robot sharing control method based on multi-objective optimization, which comprises the following steps: calculating an ARV heading angle control command and a forward speed control command which are manually operated according to the input signal of the operating rod; calculating an ARV heading angle control command of autonomous control; constructing a shared objective function, a constraint condition and an optimization solving method thereof, and outputting a shared control command of the heading angle according to the current position of the ARV, the heading angle, the distribution position of the obstacles in the local environment, the calculated manually-operated ARV heading angle control command and the automatically-controlled ARV heading angle control command; and the ARV avoids the barrier and executes the task according to the control command of the forward speed and the shared control command of the heading angle which are manually operated. The method combines the remote control and the autonomous control of the operator to improve the operation capacity, task performance and safety of the ARV in a complex, unknown and unstructured underwater environment and reduce the workload of the operator.
Description
Technical Field
The invention relates to the field of underwater robot control, in particular to an autonomous remote control underwater robot man-machine sharing control method based on multi-objective optimization.
Background
An Autonomous remote controlled Underwater robot (ARV) is a novel Underwater robot integrating the characteristics of an AUV and an ROV, has energy sources, uses optical fibers to communicate with a mother ship, has the functions of large-range Underwater search and detection of the AUV, and can be Operated and controlled by an operator in real time like the ROV to realize fixed-point observation and Underwater light operation. The ARV provides convenience for human exploration, research and development of oceans.
The current major control modes of an ARV include autonomous control and remote control. The autonomous control is generally used for executing relatively simple tasks such as pre-programmed search and observation, and the application scene is limited; when tasks are executed in a complex and unknown underwater environment, a remote control mode is generally adopted, an operator directly controls ARV observation or operation in the remote control mode, task performance of an ARV system depends on the technical level of the operator, safety of the ARV is low, and workload of the operator is heavy.
Disclosure of Invention
Aiming at the problems in the autonomous control and the remote control, the invention aims to provide the ARV man-machine shared control method based on the multi-objective optimization, which improves the operation capacity, task performance and safety of the ARV in a complex, unknown and unstructured underwater environment by combining the remote control and the autonomous control of an operator and simultaneously lightens the work load of the operator for remotely controlling the ARV.
The technical scheme adopted by the invention for solving the technical problems is as follows: an autonomous remote control underwater robot sharing control method based on multi-objective optimization comprises the following steps:
by the operator depending on the current position P of the ARV detected by the sensorARVHeading angle psiARVAnd the distribution position P of obstacles in the local environmentobsOperating the operating lever to calculate an artificially operated ARV heading angle control command psi based on the operating lever inputhAnd control command v for forward speedh；
According to PobsAnd PARVCalculating an ARV heading angle control command psi for autonomous controlapf；
Constructing a shared objective function, a constraint condition and an optimization solving method thereof according to the current position P of the ARVARVHeading angle psiARVAnd the distribution position P of obstacles in the local environmentobsAnd calculated manuallyARV heading angle control command psihAutonomously controlled ARV heading angle control command psiapfOutputting a heading angle sharing control command psishare；
ARV control command v according to forward speedhAnd sharing the control command psi of the heading angleshareAnd controlling the ARV to avoid the barrier and executing the task.
Calculating the ARV heading angle control command psi of the human operationhAnd control command v for forward speedhThe formula of (1) is as follows:
in the formula, t is time; j. the design is a squarexAnd JyFor operator control input in the x and y axes of the operating lever, where JxControlling the heading angle, J, of the ARVyControlling the forward speed of the ARV, and Jx∈[-1，1]、Jy∈[-1，1]；ψARVIs the current heading angle of the ARV; k is a radical ofψA gain factor for adjusting the heading angle increment; v. ofmaxMaximum forward speed of the ARV; j. the design is a squarexdNot less than 0 and JydAnd the value is more than or equal to 0, and the dead zone threshold values are respectively set near the x-axis 0 position and the y-axis 0 position of the operating rod.
Said according to PobsAnd PARVCalculating ARV heading angle control command psi of autonomous control systemapfThe method comprises the following steps:
calculating the ARV heading angle control command psi of the autonomous control system using the following equation if there is an obstacle in the environmentapf：
ψapf(t)＝atan(yv-yo，xv-xo)
In the formula, PARV＝[xv，yv]As the current location coordinates of the ARV, [ x ]o，yo]Is the position of the obstacle;
if a plurality of obstacles exist in the environment, the method comprises the following steps:
the magnitude r of the effect of the ith obstacle on ARV motion is calculated using the following equationi：
Wherein i is the number of the obstacle, dobs_iIs the Euclidean distance between the ARV and the i-th obstacle, dshare、dsafe、koTo design the parameters, dshare>0 is the maximum distance that the barrier has an influence on the navigation safety of the ARV, dsafe>0 is a distance threshold for ensuring safety between the ARV and the obstacle, dshare、dsafeDetermination of k from the radius of motion of the ARVo(1≥ko>0) The scale factor is used for adjusting the degree of the autonomous obstacle avoidance behavior for assisting other behaviors in avoiding the obstacle;
with riIs the size of the die, #apf_iDetermining a vector V of the impact of the ith obstacle on the ARV for the directionobs_iThe sum vector of the vectors affecting the ARV by all the obstacles is obtained by vector addition using the following formula, and the direction of the sum vector is used as the control command psi output by the autonomous control moduleapf：
In the formula, n is the number of obstacles influencing the navigation safety of the ARV in the environment.
The shared objective functions include a compliance function obedience (ψ), an autonomy function (ψ), and a stability function stability (ψ) for evaluating different values of the heading angle control command variable ψ, in the form as follows:
aim(ψ)＝exp(-χ|ψ-ψaim|)
in the formula, aim denotes an objective function, #aimAs an optimization target of the objective function, exp () is an exponential function with a natural constant as a base; x is more than or equal to 0 and is a shape coefficient(ii) a The value ranges of the objective function of the form are all in the interval (0, 1)]Internal; the magnitude of the function value of the objective function and the heading angle control command variable psi meet the corresponding target psiaimIs positively correlated with the degree of (i) in the symmetry axis psi ═ psiaimThe maximum value 1 is obtained and decreases towards two sides, the decreasing speed of the function value can be changed by adjusting the shape coefficient chi, the larger chi is, the faster the decreasing speed is, and particularly, when the chi is 0, the function value is always 1.
The obedience function obedience (psi) is used to evaluate compliance psiaimDegree of (c):
obedience(ψ)＝exp(-α|ψ-ψh|)
α is a shape coefficient of the compliance function, the value of which is determined by:
in the formula, alphamaxIs a constant greater than 0; dmminIs the minimum distance of the ARV to the obstacle; according to formula dmminThe value of alpha is adjusted to change the extent to which the operator affects the movement of the ARV.
The autonomy function ψ is used to evaluate ψ and ψ apfThe difference is as follows:
autonomy(ψ)＝exp(-γ|ψ-ψapf|)
γ is the shape coefficient of the autonomy function, whose value is determined by:
in the formula, gammamaxIs a constant greater than 0, according to the formulaminAdjusting the value of gamma to psiapfOnly at dmin＜dshareWhile keeping the ARV away from the obstacle.
The stability function stability (psi) is used to evaluate psi against psiARVDegree of change of (a):
stability(ψ)＝exp(-β|ψ-ψARV|)
where β is a shape factor of the stability function and is a normal number.
Constraint I of the shared objective functionsafeThe calculation is carried out in two steps according to a safety evaluation function:
firstly, determining a set I 'of value intervals of motion directions meeting the safety requirement'safe＝[I′1，I′2，...，I′j]J is the number of value intervals, and ARV is set to be I'safeA safe distance is kept from the obstacle in the moving direction in the interval of (1); using security (ψ) < λ (d)max-dshare) Is calculated to obtain I'safe；
Wherein the safety evaluation function is defined as a piecewise function, and the function value in each piecewise domain is determined according to the following formula:
in the formula, security (ψ) represents a security evaluation function; mu is a scale factor for changing the size of the function value and is set to be a constant larger than 0; dmaxA distance threshold for initiating a change in the security function value for the obstacle, and
d is the minimum distance of the ARV to the barrier grid within a single defined domain, when d>dmaxWhen d is equal to dmax；
Secondly, a safety interval I for ensuring ARV safety is setΔReduction of l'safeTo obtain a set I of feasible solution value intervalssafe＝[I1，I2，...，Im]M is the number of value intervals, and the ARV is ensured to be in IsafeAlso keep a relatively safe distance with the obstacles on both sides in the moving direction in the interval of (2).
The optimization solving method of the shared objective function adopts a minimum and maximum method, and the heading angle control command psi generated by the shared control method can be obtained by solvingshareOf the formulaShown in the figure:
in the formula, min { } means that each segment in the definition domain takes the minimum value of all the objective functions.
The invention has the following beneficial effects and advantages:
1. the method improves the task performance of the ARV under the complex, unknown and unstructured environments by combining the shared control with the environmental awareness and decision-making capability of operators and the accurate control capability of an autonomous control system.
2. The method of the invention uses the control signal generated by the autonomous control system to assist the operation of the operator, thereby reducing the operation complexity of the operator in the task and lightening the workload.
3. The method designs a method for processing the local environment information acquired by the ARV sensor, and adjusts the control signal of the ARV according to the processed information, thereby ensuring the safety of the ARV.
4. The method can give consideration to various factors such as task requirements, operator requirements and the like when integrating the remote control of operators and the autonomous control of the autonomous control system, is suitable for various tasks, and has wide application scenes.
5. The method adopts a modularized design idea, has a simple structure and strong expandability, and is convenient for adjusting the design of the algorithm according to task requirements.
6. The method is designed based on the existing multi-objective optimization theory, can be compatible with a large number of mature objective function forms and optimization algorithms in the multi-objective optimization theory, and can meet wide actual requirements.
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FIG. 1 is a system block diagram of the method of the present invention;
FIG. 2 is a block diagram of the process of the present invention;
FIG. 3 is a schematic illustration of processing of local environmental information acquired by an ARV sensor in the method of the present invention;
FIG. 4 is a schematic illustration of the solution in the method of the present invention using the minimum-maximum method;
fig. 5 is a flow chart of the method steps of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The method has the basic idea that based on the theory of shared control, the control command of the ARV heading angle is used as a decision variable, a group of corresponding objective functions are designed according to factors such as control intentions of operators, autonomous control, task requirements, requirements on the performance of the ARV task and the like, constraint conditions of the ARV in the task are determined, the shared control of the ARV is converted into a multi-objective optimization problem by using the objective functions and the constraint conditions, and an appropriate optimization algorithm is selected in the multi-objective optimization method to solve the problem to obtain an optimal solution (namely a Pareto optimal solution in the multi-objective optimization) as the heading angle control command generated by the ARV shared control. The method can give consideration to various factors in the shared control of the ARV, improve the operation capability and the safety of the ARV in a complex, unknown and unstructured underwater environment, and simultaneously reduce the workload of operators.
Based on the method, the embodiment aims at the problem that an ARV can not preset a moving target and a moving path of the ARV in the execution environment exploration task design in the underwater environment with unknown global environment information and unstructured global environment information, an operator needs to search, decide and plan according to real-time local environment information acquired by acoustic, optical and other sensors carried by the ARV, the safety is poor, the task performance is limited by the technical level of the operator, the workload of the operator is heavy and the like, and the multi-objective optimization based sharing control method designed by the embodiment can be adopted to well solve the problems.
P in FIG. 1obs＝[Pobs_1，Pobs_2，...，Pobs_n](n is the number of obstacles) is the position information of the obstacle distribution in the local environment, PARVFor the current position of the ARV,. psiARVIs the current heading angle, psi, of the ARVhARV heading angle control command v output for the operatorhControl command for forward speed of ARV output by operator,. psiapfControl command of the ARV heading angle, psi, output for an autonomous control systemshareThe control command is shared for the heading angle generated by using a multi-objective optimization-based shared control method. In fig. 3, the small squares generated by intersecting the dotted lines are grids, where a black grid represents an obstacle grid, a white grid represents a free grid, Δ represents a grid granularity, Δ is determined according to the accuracy of local obstacle information and the performance of a computer, and the smaller Δ is, the higher the requirements on the performance of the computer and the map accuracy are; the large square marked by the solid line is a movable window, w is the side length of the movable window, and the value of w is determined according to the detection distance of the sensor; the ellipse represents an ARV, a grid whose center of gravity is located at the center of the active window; theta is an angle used for dividing the active window, theta is an included angle, and the active window is divided into
In each area, the size of theta is determined according to the performance of a computer and the precision of a local map, the smaller theta is, the higher the requirements on the performance of the computer and the precision of the map are, and the more accurate the description of the obtained safety evaluation function on the environment is; d is the minimum distance of the ARV to the barrier grid in a single area, when no barrier grid exists in the area
I′1A value range, I, of psi for meeting the safety requirement, i.e. the safety evaluation function value requirement1Is a constraint condition IsafeA value interval of the middle psi; i isΔFor a safety interval, IΔIs determined according to the requirements on the security of the ARV in the task, IΔThe greater the safety of the ARV, the less the range of motion of the ARV near the obstacle. In FIG. 4, I1、I2And I3Are respectively IsafeThe value range of the medium heading angle control command variable psi, obedience (psi) is a obedience function in the objective function, autonomy (psi) is an autonomy function in the objective function, and stability (psi) is an objective functionThe stability function in number, the black bold line, is a single-target optimization function min { origin (ψ), autonomy (ψ), stability (ψ) } satisfying the constraint condition obtained when calculation is performed using the minimum-maximum method.
As shown in fig. 1, in the present embodiment, a modular system structure control ARV is designed, and is divided into four modules, namely a sensing system module, an operator remote control module, an autonomous control module, and a shared control module.
The sensing system module is used for acquiring and outputting information related to environment and ARV motion state, including distribution P of obstacles in local environmentobs＝[Pobs_1，Pobs_2，...，Pobs_n](n is the number of obstacles), the current position P of the ARVARVAnd heading angle psiARV. The sensors employed are capable of detecting environmental information, motion status and position information, including: acoustic, optical, etc. sensors, such as forward looking sonar, collision avoidance sonar, cameras, etc.
The function of the remote control module of the operator is that the operator outputs P according to the sensing system moduleARV、ψARVAnd PobsRespectively outputting ARV heading angle control command psi of operator through x and y axes of remote control operation rodhAnd control command v for forward speedh，ψhAnd vhRespectively calculated according to the following formula:
in the formula, t is the time calculated by using a formula; j. the design is a squarexAnd JyFor operator control input in the x and y axes of the operating lever, where JxControlling the heading angle, J, of the ARVyControlling the forward speed of the ARV, and Jx∈[-1，1]、Jy∈[-1，1]；ψARVIs the current heading angle of the ARV; k is a radical ofψTo adjust the gain factor of the heading angle increment,determining according to the control capability of the ARV motion controller and the steering capability of the ARV; v. ofmaxMaximum forward speed of the ARV; j. the design is a squarexdNot less than 0 and JydThe dead zone threshold value set near the x-axis and y-axis 0 positions of the operating rod is not less than 0, so that an operator can conveniently input a control command for keeping the current movement direction and the forward speed of the ARV at 0, and the control command is determined according to the technical level of the operator and the requirement of a task on the control command of the operator.
The autonomous control module outputs P according to the sensing system moduleobsAnd PARVCalculating the ARV heading angle control command psi for the autonomous control system using the following equationapf：
ψapf(t)＝atan(yv-yo，xv-xo)
In the formula, PARV＝[xv，yv]As the current location coordinates of the ARV, [ x ]o，yo]Atan (-) is the arctan function for the location of the obstacle.
If a plurality of obstacles exist in the environment, the magnitude r of the influence of the ith obstacle on the ARV motion is calculated by using the following formulai(i is the obstacle number):
in the formula (d)obs_iIs the Euclidean distance between the ARV and the i-th obstacle, dshare、dsafe、koTo design the parameters, dshareGreater than 0 is the maximum distance that the barrier has an influence on the navigation safety of the ARV, dsafeGreater than 0 is a distance threshold for ensuring safety between the ARV and the obstacle, dshare、dsafeDetermination of k from the radius of motion of the ARVo(1≥ko> 0) is a proportionality coefficient used for adjusting the degree of the autonomous obstacle avoidance behavior assisting other behaviors in avoiding obstacles, and is determined according to the technical level of operators and the requirement on safety, the higher the requirement on safety is, k isoThe larger. With riIs the size of the die, #apf_iDetermining a vector V of the impact of the ith obstacle on the ARV for the directionobs_iThe sum vector of the vectors affecting the ARV by all the obstacles is obtained by vector addition using the following formula, and the direction of the sum vector is used as the control command psi output by the autonomous control moduleapf：
In the formula, n is the number of obstacles influencing the navigation safety of the ARV in the environment.
The shared control module outputs psi according to the sensing system moduleARV、PARVAnd PobsPsi output by the operator remote control modulehPsi output from autonomous control moduleapfShared control command psi for generating heading angle using a shared control method based on multiobjective optimizationshare。
In the configuration shown in fig. 1, the forward speed of the ARV is controlled by a control command v output by the operatorhDirect control, shared control command psi with heading angle generated by shared control module using a multi-objective optimization-based shared control methodshareAnd (5) controlling.
As shown in FIG. 2, the autonomous control module of FIG. 1 is used to generate the heading angle shared control command ψshareThe sharing control method based on the multi-objective optimization is composed of an objective function, a constraint condition and an optimization method, wherein the constraint condition is determined by a safety evaluation function. The objective function and the constraint condition convert the shared control of the ARV heading angle into a command variable psi (psi belongs to (-pi, pi) controlled by the heading angle]) A multi-objective optimization problem as a decision variable, which is solved by an optimization method to obtain an optimal solution of psi, i.e. a shared control command psi of the ARV heading angleshare. The objective function, constraints and optimization method are described in detail below.
In the embodiment, with the target of complying with the control intention of an operator, improving the safety of the ARV, reducing the operation complexity of the operator, optimizing the motion path of the ARV and the like, three target functions, namely a compliance function obedience (ψ), an autonomy function autonomy (ψ) and a stability function stability (ψ), are respectively designed to evaluate different values of ψ. The objective functions are all designed to be in a negative exponential function form with a natural constant as a base number, and are shown as the following formula:
aim(ψ)＝exp(-χ|ψ-ψaim|)
in the formula, aim denotes an objective function, #aimAs an optimization target of the objective function, exp () is an exponential function with a natural constant as a base; x is more than or equal to 0 and is a shape coefficient; the value ranges of the objective function of the form are all in the interval (0, 1)]And the function values of different objective functions can be conveniently compared. The magnitude of the function value of the objective function and psi satisfy the corresponding objective psiaimIs positively correlated with the degree of symmetry axis psiaimThe maximum value 1 is obtained and is decreased towards two sides, the decreasing speed of the function value can be changed by adjusting the shape coefficient x, the larger the x is, the faster the decreasing speed is, and particularly, when the x is zero, the function value is always 1; in the embodiment, the minimum and maximum method is used as the optimization method to solve the multi-objective optimization problem, and the optimization result is determined by the objective function with the minimum function value, so that the larger the shape coefficient of the objective function is, the larger the influence on the optimization result is, and when the shape coefficient is 0, the optimization result is not influenced, and the effect of each objective function in the multi-objective optimization problem solving is conveniently adjusted by adjusting the shape coefficient. The design of the three objective functions is described below.
In an environmental exploration task, the movement of an ARV first needs to comply with the control intent of the operator to meet the operator's observed needs. The embodiment adopts psihThe compliance function obedience (psi) shown in the following formula is designed to evaluate compliance psihDegree of (c):
obedience(ψ)＝exp(-α|ψ-ψh|)
where α is a shape factor of the compliance function, and its value is determined by:
in the formula, alphamaxIs constant greater than 0, according to the operatorThe skill level of the staff is set, the higher the skill level, alphamaxThe larger the ARV is, the more obeying the control command of an operator in the sharing control based on the multi-objective optimization; dminIs the minimum distance of the ARV to the obstacle; according to formula dminAdjusting the value of alpha to vary the extent to which the operator influences the movement of the ARV, dminThe smaller the form factor of the obedience function is, the smaller the influence of the obedience function on the optimization result is, the control weight of the ARV is reduced in the sharing control of the ARV by remote control of an operator, and more ARV obey the control command of the autonomous control system to ensure the safety of the ARV.
The ARV should also obey the control command psi of the autonomous control system in the environment exploration taskapfTo assist the operator's operation and improve the safety of the ARV, the present embodiment designs an autonomy function ψ shown below for evaluating ψ and ψapfThe difference in (a).
autonomy(ψ)＝exp(-γ|ψ-ψapf|)
Where γ is a shape coefficient of the autonomy function, and its value is determined by:
in the formula, gammamaxIs a constant greater than 0. According to formula dmminAdjusting the value of gamma to psiapfOnly at dmin＜dshareWhen the ARV is far away from the obstacle, the influence of the shape coefficient on the objective function in the multi-objective optimization can be determined, and when the ARV is far away from the obstacle, the autonomous control system has no influence on the movement of the ARV.
An appropriate reduction in abrupt changes in control commands relative to the current direction of motion may make the state of motion of the ARV more stable, thereby reducing operator complexity and making the path of motion of the ARV smoother. Therefore, the present embodiment designs a stability function stability (ψ) shown below for evaluating ψ against ψARVDegree of change of (a):
stability(ψ)＝exp(-β|ψ-ψARV|)
in the formula, beta>0 is the shape coefficient of the stability function, the larger beta is, the larger influence of the stability function on the optimization result is, and psi generated by optimizationshareRelative to psiARVThe smaller the variation of (c). Too large a β makes it difficult for the ARV to change the direction of motion, so β is set to a smaller normal number.
As shown in FIG. 3, the constraint condition is a set I of value intervals of the heading angle control command variable ψsafe＝[I1，I2，...，Im](m is the number of value intervals), and is determined by a safety evaluation function; the safety evaluation function is calculated according to the distribution of obstacles in the local environment to ensure the safety of the ARV in the task. In the environment exploration task, the embodiment determines the safety evaluation function according to the principle that the larger the distance between the ARV and the obstacle, the higher the safety, and the direction and distance information of the distribution of the surrounding obstacle obtained by the acoustic, optical and other sensors carried by the ARV. The grid map within the active window of fig. 3 is used to compute the security assessment function.
In this embodiment, the safety evaluation function is designed as a piecewise function, the argument of which is ψ (i.e. the domain is the interval (- π, π ]). in a polar coordinate system, the domain is segmented according to the region division with the included angle θ in fig. 3, the function value in each segment of the domain is constant, the magnitude of the function value is determined according to d in the corresponding region in fig. 3, the larger d, the smaller the function value, the safer the value of ψ in the segment of the domain is, and the function value in each segment of the domain is calculated using the following formula:
in the formula, security (ψ) represents a security evaluation function; mu is a scale factor for changing the size of the function value and is set to be a constant larger than 0; dmaxA distance threshold for initiating a change in the security function value for the obstacle, and
d is the minimum ARV to barrier grid within a single definition domainDistance, when no obstacle grid exists in the corresponding area
Constraint I in the present embodimentsafeAccording to the safety evaluation function, calculation is carried out in two steps, and firstly, a set I 'of value intervals of the motion direction meeting the safety requirement is determined'safe＝[I′1，I′2，...，I′j](j is the number of value intervals), and ARV is set to be I'safeA distance to an obstacle in a moving direction within the interval of (1): using dshareAs the distance threshold value for ensuring the safety of the ARV, the function value threshold value of the corresponding safety evaluation function is λ (d)max-dshare) Since the larger the distance is, the smaller the function value is, and the safer the value of ψ is to be taken in the calculation of the function value of the safety evaluation function, security (ψ) < λ (d) is usedmax-dshare) Is calculated to obtain I'safe. Then, I 'is reduced'safeTo obtain IsafeTo ensure ARV is in IsafeAlso keep a relatively safe distance with the obstacles on both sides in the moving direction in the interval of (1): as shown in FIG. 3, a safety interval I for ensuring ARV safety is setΔAt l'safeAll value intervals of (in l'1Example) respectively reduce I at both endsΔTo obtain Isafe(in I)1For example).
Using the above described design of objective functions and constraints, the shared control of the ARV heading angle can be translated into a maximized multi-objective optimization problem as shown by:
where max represents ψ corresponding to the maximum value of the objective function value to be preferentially taken in the multi-objective optimization.
The optimization method is used for solving the multi-objective optimization problem to obtain the optimal solution psishare. In environmental exploration tasks, ARVs are typically usedThe method works in an underwater environment with unknown global environment information, the requirement on reliability is high, and an optimization algorithm is required to be solved in each control cycle to obtain a stable and reliable result; furthermore, since the operator needs to control the ARV in real time, the optimization algorithm chosen should have less time complexity to reduce the amount of computation. Therefore, the present embodiment uses the min-max method as an optimization method to seek the best optimization result in the worst case. The minimum and maximum method needs less calculation amount, meanwhile, the optimization result is stable, and the real-time performance and the reliability of the ARV sharing control method in the task can be ensured. The minimum and maximum method converts the maximized multi-target optimization problem into a single-target optimization problem, and the heading angle control command psi generated by the shared control method can be obtained by solvingshareAs shown in the following formula:
in the formula, min { } means that each segment in the definition domain takes the minimum value of all the objective functions.
As shown in FIG. 4, the minimum maximum method is used to calculate ψshareIn the process, the function images of the single-target optimization functions min { essential (ψ), autonomy (ψ), and stability (ψ) } in the value range satisfying the constraint condition are shown by black bold lines in fig. 4, and the angle corresponding to the maximum value of the function value thereof is taken as the optimal solution of the multi-target optimization problem, that is, the control command ψ of the ARV heading angle generated by the shared control method based on the multi-target optimization in this embodimentshare. Psi obtained using the minimum-maximum methodshareThe objective function with the minimum function value can always obtain the maximum value in the constraint condition, the objective with the minimum function value can obtain the best result, and meanwhile, the influence of the objective function on the optimization result can be conveniently changed by adjusting the shape coefficient of the objective function.
The flow of the working steps of the method of the invention is shown in fig. 5 and is divided into the following steps.
(1) Designing appropriate objective functions and constraint conditions according to task requirements, performance requirements and the like, modeling shared control of the ARV into a multi-objective optimization problem, and selecting an appropriate optimization method;
(2) determining each objective function according to the information of the movement state of the ARV, the operation of an operator, the environmental condition and the like in the current control period;
(3) determining the constraint condition of the shared control command according to the environmental information in the current control period, the requirement on the ARV movement and other information;
(4) calculating a shared control command of the heading angle of the ARV generated by the shared control method based on multi-objective optimization according to a preselected optimization method;
(5) transmitting the shared control command to an execution mechanism of the ARV;
(6) and (4) judging whether the operation is finished or not, if so, exiting, and otherwise, re-executing the step (2).
In addition to the above embodiments, the method of the present invention may also have other embodiments such as the movement of the end of the ARV mechanical arm, and any technical solution formed by adopting equivalent substitution or equivalent transformation falls within the protection scope of the method of the present invention.
Claims (9)
1. An autonomous remote control underwater robot sharing control method based on multi-objective optimization is characterized by comprising the following steps:
by the operator depending on the current position P of the ARV detected by the sensorARVHeading angle psiARVAnd the distribution position P of obstacles in the local environmentobsOperating the operating lever to calculate an artificially operated ARV heading angle control command psi based on the operating lever inputhAnd control command v for forward speedh；
According to PobsAnd PARVCalculating an ARV heading angle control command psi for autonomous controlapf；
Constructing a shared objective function, a constraint condition and an optimization solving method thereof according to the current position P of the ARVARVHeading angle psiARVAnd the distribution position P of obstacles in the local environmentobsAnd a calculated manually operated ARV heading angle control command psihAutonomously controlled ARV heading angle control command psiapfOutput heading angleShared control command psishare；
ARV control command v according to forward speedhAnd sharing the control command psi of the heading angleshareAnd controlling the ARV to avoid the barrier and executing the task.
2. The multi-objective optimization-based autonomous remote-control underwater robot shared control method according to claim 1, characterized in that the manually operated ARV heading angle control command ψ is calculatedhAnd control command v for forward speedhThe formula of (1) is as follows:
in the formula, t is time; j. the design is a squarexAnd JyFor operator control input in the x and y axes of the operating lever, where JxControlling the heading angle, J, of the ARVyControlling the forward speed of the ARV, and Jx∈[-1，1]、Jy∈[-1，1]；ψARVIs the current heading angle of the ARV; k is a radical ofψA gain factor for adjusting the heading angle increment; v. ofmaxMaximum forward speed of the ARV; j. the design is a squarexdNot less than 0 and JydAnd the value is more than or equal to 0, and the dead zone threshold values are respectively set near the x-axis 0 position and the y-axis 0 position of the operating rod.
3. The multi-objective optimization-based autonomous remote control underwater robot shared control method according to claim 1, characterized in that the method is based on PobsAnd PARVCalculating ARV heading angle control command psi of autonomous control systemapfThe method comprises the following steps:
calculating the ARV heading angle control command psi of the autonomous control system using the following equation if there is an obstacle in the environmentapf：
ψapf(t)＝atan(yv-yo，xv-xo)
In the formula, PARV＝[xv，yv]As the current location coordinates of the ARV, [ x ]o，yo]Is the position of the obstacle;
if a plurality of obstacles exist in the environment, the method comprises the following steps:
the magnitude r of the effect of the ith obstacle on ARV motion is calculated using the following equationi：
Wherein i is the number of the obstacle, dobs_iIs the Euclidean distance between the ARV and the i-th obstacle, dshare、dsafe、koTo design the parameters, dshare>0 is the maximum distance that the barrier has an influence on the navigation safety of the ARV, dsafe>0 is a distance threshold for ensuring safety between the ARV and the obstacle, dshare、dsafeDetermination of k from the radius of motion of the ARVo(1≥ko>0) The scale factor is used for adjusting the degree of the autonomous obstacle avoidance behavior for assisting other behaviors in avoiding the obstacle;
with riIs the size of the die, #apf_iDetermining a vector V of the impact of the ith obstacle on the ARV for the directionobs_iThe sum vector of the vectors affecting the ARV by all the obstacles is obtained by vector addition using the following formula, and the direction of the sum vector is used as the control command psi output by the autonomous control moduleapf：
In the formula, n is the number of obstacles influencing the navigation safety of the ARV in the environment.
4. The sharing control method for the autonomous remote-controlled underwater robot based on the multi-objective optimization of claim 1, characterized in that the shared objective function comprises a obedience function obedience (ψ), an autonomy function autonomy (ψ) and a stability function stability (ψ) for evaluating different values of a heading angle control command variable ψ, in the following form:
aim(ψ)＝exp(-χ|ψ-ψaim|)
in the formula, aim denotes an objective function, #aimAs an optimization target of the objective function, exp () is an exponential function with a natural constant as a base; x is more than or equal to 0 and is a shape coefficient; the value ranges of the objective function of the form are all in the interval (0, 1)]Internal; the magnitude of the function value of the objective function and the heading angle control command variable psi meet the corresponding target psiaimIs positively correlated with the degree of (i) in the symmetry axis psi ═ psiaimThe maximum value 1 is obtained and decreases towards two sides, the decreasing speed of the function value can be changed by adjusting the shape coefficient chi, the larger chi is, the faster the decreasing speed is, and particularly, when the chi is 0, the function value is always 1.
5. The multi-objective optimization-based autonomous remote control underwater robot shared control method as claimed in claim 4, characterized in that the compliance function obedience (psi) is used for evaluating psi compliance psiaimDegree of (c):
obedience(ψ)＝exp(-α|ψ-ψh|)
α is a shape coefficient of the compliance function, the value of which is determined by:
in the formula, alphamaxIs a constant greater than 0; dminIs the minimum distance of the ARV to the obstacle; according to formula dminThe value of alpha is adjusted to change the extent to which the operator affects the movement of the ARV.
6. The multi-objective optimization-based autonomous remote-control underwater robot shared control method of claim 4, characterized in that the autonomy function ψ is used for evaluating ψ and ψapfThe difference is as follows:
autonomy(ψ)＝exp(-γ|ψ-ψapf|)
γ is the shape coefficient of the autonomy function, whose value is determined by:
in the formula, gammamaxIs a constant greater than 0, according to the formulaminAdjusting the value of gamma to psiapfOnly at dmin＜dshareWhile keeping the ARV away from the obstacle.
7. The multi-objective optimization-based autonomous remote-control underwater robot shared control method according to claim 4, characterized in that the stability function stability (ψ) is used to evaluate ψ versus ψARVDegree of change of (a):
stability(ψ)＝exp(-β|ψ-ψARV|)
where β is a shape factor of the stability function and is a normal number.
8. The multi-objective optimization-based autonomous remote control underwater robot shared control method according to claim 1, characterized in that the constraint condition I of the shared objective functionsafeThe calculation is carried out in two steps according to a safety evaluation function:
firstly, determining a set I 'of value intervals of motion directions meeting the safety requirement'safe＝[I′1，I′2，...，I′j]J is the number of value intervals, and ARV is set to be I'safeA safe distance is kept from the obstacle in the moving direction in the interval of (1); using security (ψ) < λ (d)max-dshare) Is calculated to obtain I'safe；
Wherein the safety evaluation function is defined as a piecewise function, and the function value in each piecewise domain is determined according to the following formula:
in the formula, security (ψ) represents a security evaluation function; mu is a scale factor for changing the size of the function value and is set to be a constant larger than 0; dmaxA distance threshold for initiating a change in the security function value for the obstacle, and
d is the minimum distance of the ARV to the barrier grid within a single defined domain, when d>dmaxWhen d is equal to dmax；
Secondly, a safety interval I for ensuring ARV safety is setΔReduction of l'safeTo obtain a set I of feasible solution value intervalssafe＝[I1，I2，...，Im]M is the number of value intervals, and the ARV is ensured to be in IsafeAlso keep a relatively safe distance with the obstacles on both sides in the moving direction in the interval of (2).
9. The multi-objective optimization-based autonomous remote control underwater robot shared control method according to claim 4, characterized in that the optimization solving method of the shared objective function adopts a minimum and maximum method, and the heading angle control command psi generated by the shared control method can be obtained by solvingshareAs shown in the following formula:
in the formula, min { } means that each segment in the definition domain takes the minimum value of all the objective functions.
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