A method for generating energy-saving trajectories for underactuated AUVs

Abstract

This invention discloses an energy-saving trajectory generation method for underactuated AUVs, belonging to the field of autonomous underwater vehicle technology. Addressing the problem of generating three-dimensional energy-saving trajectories for AUVs under multiple constraints, including dynamic constraints, this invention designs an AUV energy-optimal trajectory generator based on the direct matching method. First, based on relevant theories and data obtained from open-water thruster experiments and CFD simulations, a complete analysis of the energy consumption mechanism of AUVs performing multipath point tracking tasks is conducted. Then, a novel energy evaluation function is established, incorporating the influence of time and attitude angles on energy consumption during the underactuated AUV's navigation. Finally, AUV dynamic constraints are added to prevent sharp corners or inflection points in the generated trajectory from causing untrackable situations. Combining system boundary constraints and path constraints, an executable optimal energy trajectory is generated through offline trajectory planning based on the direct matching method. Simulation experiments verify the effectiveness and superiority of this method.

Description

Technical Field

[0001] This invention belongs to the field of autonomous underwater vehicle technology, specifically relating to an energy-saving trajectory generation method for an underactuated AUV. Background Technology

[0002] The endurance of autonomous underwater vehicles (AUVs) is one of their most important performance indicators, as it directly determines the operating cost of the vehicle.

[0003] Due to the limited energy capacity of AUVs, 80% of the Earth's oceans remain unexplored, and improving their endurance during ocean exploration has been a pressing problem. Current common methods involve utilizing ocean currents or installing variable buoyancy systems (VBS) for long-duration exploration. However, this sacrifices AUV maneuverability, increases costs, and lacks portability. Therefore, reducing AUV energy consumption from a control strategy perspective is highly significant.

[0004] In recent years, experts and scholars have conducted extensive research on the problem of generating energy-saving trajectories for AUVs. Most research approaches involve designing an efficient and feasible energy cost function and minimizing it to solve for the optimal reference state of the AUV. However, many current studies assume that ocean currents are slowly time-varying and that marine environmental information is known a priori, which is difficult to satisfy in practical engineering. Furthermore, different choices of energy consumption evaluation functions can lead to differences in the final energy-saving effect; simply using the shortest travel time or the minimum actuator work as the evaluation index for minimum energy is not rigorous for AUVs. Therefore, generating smooth energy-saving trajectories for AUVs at the planning level, conforming to the constraints of time, space, and dynamic models, based on an efficient energy consumption evaluation function, is both challenging and significant. Summary of the Invention

[0005] The purpose of this invention is to provide an energy-efficient trajectory generation method for underactuated AUVs. Its feature is the optimal trajectory generation based on computational fluid dynamics simulation and direct matching method. Under the action of the energy-efficient trajectory generator designed in this invention, the generation of an energy-optimal three-dimensional trajectory that satisfies the dynamic constraints of the AUV is achieved.

[0006] This invention provides a method for generating energy-saving trajectories for an underdriven AUV, comprising the following steps:

[0007] S110: Constructing a five-degree-of-freedom mathematical model for the underactuated AUV energy-saving trajectory generation task;

[0008] S120: Analysis of energy consumption of underactuated AUVs during navigation;

[0009] S130: Constructing a drag and energy consumption model for underactuated AUVs;

[0010] S140: Combining the above steps and the specified waypoint information, list the optimal control problem that needs to be solved;

[0011] S150: Based on the direct combination method, the continuous-time infinite-dimensional optimal control problem is discretized into a finite-dimensional nonlinear programming problem, and the solver generates the optimal trajectory based on the original path point with the minimum energy consumption and the shortest time.

[0012] Furthermore, in S110, the five-degree-of-freedom model is:

[0013]

[0014] Where η = [x,y,z,θ,ψ] T This represents the position information of the underactuated AUV; ν = [u, v, w, q, r] T The speed information is for the underactuated AUV; α and γ are the angle of attack and drift angle of the underactuated AUV, respectively; m ii d ii , i∈[1,6] is the hydrodynamic coefficient; ρ is the density of seawater; g is the gravitational acceleration; This refers to the drainage volume; For longitudinal steady center height; control input τ = [τ u ,0,0,τ q ,τ r ] T .

[0015] Furthermore, in S120, the energy consumption of the underactuated AUV during navigation includes basic computational energy consumption, mission payload energy consumption, energy consumption generated by the work done by the underactuated AUV actuators during navigation, and energy consumption to overcome navigation resistance; the navigation resistance energy consumption includes the work done by the propellers to overcome the longitudinal resistance of the hull. And the increase in energy consumption W due to the increase in angle of attack. d .

[0016] Furthermore, in S130, the propeller energy consumption to overcome the longitudinal resistance of the hull is...

[0017]

[0018] Where β is the power conversion ratio; u is the longitudinal velocity; X uu t is the longitudinal drag coefficient of the underactuated AUV; t0 is the start time of flight; t f This refers to the time the ship stops.

[0019] The energy consumption increase W of the underactuated AUV due to the increased angle of attack d for:

[0020]

[0021] Where ΔX is the longitudinal drag of the underdriven AUV fitted by the least squares method; For underdriven AUV speed;

[0022] The total energy consumption W of the underactuated AUV during navigation auv :

[0023]

[0024] Among them, W R Energy consumed to overcome resistance during navigation; W e Other energy consumption.

[0025] Furthermore, in S140, the energy optimal control problem is expressed as:

[0026]

[0027] stχ=f(t,χ(t),τ(t))

[0028]

[0029] Where, χ=[η,ν] T It is the state of AUV; t∈[t0,t f [] represents the flight time of the AUV; stχ = f(t,χ(t),τ(t)) represents the dynamic constraints of the problem. For the boundary constraints of the problem, To control the upper limit of input This is the upper limit of the pitch angle. This is the maximum termination time. For the path constraints of the problem; r COA For the switching radius; x(t) j ),y(t j ),z(t j ) represents the real-time position coordinates of the AUV; WP = {WP i ∈R 3 i = 0, ..., N w} represents the coordinates of the specified path points. WP0 is the initial position.

[0030] Furthermore, step S150 specifically includes the following steps:

[0031] S150.1: Divide the time domain of trajectory optimization into N equal intervals. f Segment, obtain N f+1 time-based collating points, with the time domain between every two time points being [t]. k-1 ,t k ], k=0,...,N f ;

[0032] S150.2: Rewrite the optimal control problem proposed in S140 in discrete form:

[0033]

[0034] stη k+1 =f dt ·(t k ,η k ,v k ),η0=WP0

[0035]

[0036] Among them, f dt The discrete-time vehicle kinematics are obtained according to the time step Δt; WP0 is the initial position; η represents the upper bound of the AUV's velocity and angular velocity. mj The m-th discrete value j One state point; x mj ,y mj ,z mj N represents the real-time location of the AUV. w This represents the number of waypoint sequences.

[0037] S150.3: In [t0,t f The continuous time is divided into N segments. f The segment, each sub-interval is [t] k ,t k+1 ], k = 0, ..., N f -1; the matching point of each sub-interval

[0038]

[0039] Among them, h k =t k+1 -t k ;

[0040] S150.4: Using Simpson's formula for subintervals t∈[t k ,t k+1 State equations on Integrate to obtain the expected trajectory of the AUV in each sub-interval:

[0041]

[0042] Obtain AUV in t∈[t0,t f Optimal energy trajectory η d (t):

[0043]

[0044] Where, η d (t)=[x d ,y d ,z d ] T It is bounded, η i Let (t) be the trajectory of each segment, i = 0, 1, 2, ..., f-1, and have bounded first and second derivatives with respect to time. There exists a positive constant B0 that satisfies...

[0045] Beneficial effects of this invention:

[0046] This invention addresses the problem of generating three-dimensional energy-efficient trajectories for AUVs under multiple constraints, including dynamic constraints, and designs an AUV energy-optimal trajectory generator based on the direct matching method. First, based on relevant theories and data obtained from open-water thruster experiments and CFD simulations, a comprehensive analysis of the energy consumption mechanism of AUVs performing multipath point tracking tasks is conducted. Then, a novel energy evaluation function is established, incorporating the effects of time and attitude angles during underactuated AUV navigation. Finally, AUV dynamic constraints are added to prevent sharp corners or inflection points in the generated trajectory from causing untrackable situations. Combining system boundary constraints and path constraints, an executable optimal energy trajectory is generated through DC-based offline trajectory planning. Simulation experiments verify the effectiveness and superiority of the proposed method. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method for generating three-dimensional energy-saving trajectories of AUVs with dynamic constraints provided in the embodiments of the present invention;

[0048] Figure 2 This is a schematic diagram of an open-water test of an AUV propulsion unit provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the AUV "thrust-power" curve fitting provided in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of computational fluid dynamics simulation of an AUV at zero angle of attack provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the "angle of attack - increase in drag" curve fitting of the AUV provided in the embodiment of the present invention;

[0052] Figure 6 This is a speed comparison diagram of the AUV energy-saving trajectory generator provided in the embodiments of the present invention;

[0053] Figure 7 This is a comparison diagram of the position and energy consumption of the AUV energy-saving trajectory generator provided in this embodiment of the invention;

[0054] Figure 8 This is a comparison chart of position and energy consumption when the AUV energy-saving trajectory generator provided in this embodiment of the invention performs multi-path point tracking tasks. Detailed Implementation

[0055] The embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, this invention discloses an energy-saving trajectory generation method for an underactuated AUV, the specific steps of which are as follows:

[0057] S110: Establish a five-degree-of-freedom mathematical model for generating the trajectory of an underactuated AUV; since AUVs have good symmetry, the influence of the roll angle is ignored, and a five-degree-of-freedom kinematic model is established.

[0058] S120: Analysis of the energy consumption generation mechanism of AUV.

[0059] The energy consumption of an AUV during navigation mainly comes from the following sources:

[0060] (1) Basic computing energy consumption: generated by industrial control computer and basic circuits;

[0061] (2) Task payload energy consumption: generated during the operation of each sensor;

[0062] (3) Energy consumption generated by the work done by the AUV actuators during navigation and energy consumption to overcome navigation resistance;

[0063] It should be noted that the first two types of energy consumption are usually fixed or have little energy-saving potential under a specified task. Therefore, this patent sets the power of the first two types of energy consumption to a fixed value, mainly to optimize the energy consumption generated during AUV navigation.

[0064] S130: Design of an AUV's drag energy consumption model;

[0065] The specific method for constructing the AUV's flight drag energy consumption model is as follows:

[0066] First, we break down the resistance during AUV navigation into the longitudinal drag along the hull and the increase in drag caused by the angle of attack α. Overcoming the longitudinal drag is the effective thrust, denoted as X. s =X |u∣u u 2 Furthermore, an empirical model of thruster energy consumption P can be used. T =β(X) s ) 1.5 Energy consumption is calculated by integration.

[0067] The energy consumption caused by different angles of attack α during AUV navigation is also different. This value can be measured by computational fluid dynamics simulation software, and the increase in AUV's navigation resistance ΔX at different angles of attack can be calculated by CFD numerical simulation, which provides a basis for the subsequent generation of energy-saving trajectories.

[0068] The least squares method shows that, given m sets of observation data (α) i ΔX i (i = 1, 2, ..., m), set the fitting model as ΔX = k q α 3 +k r α 2 The parameter k is estimated by minimizing the following objective function. q k r :

[0069]

[0070] in, u and w are the longitudinal and vertical velocities of the AUV, respectively; β is the power conversion ratio of the thruster, which depends on the capability of each thruster and can be measured through relevant experiments; ΔX i These are observed values. S is the fitted value, and S(a) is the sum of squared residuals.

[0071] S140: Description of the optimal control problem; The specific construction method for the optimal control problem that AUV needs to solve is as follows:

[0072] Based on the mathematical model of the AUV and the analysis and energy consumption model constructed in S110, S120, and S130, a multi-waypoint underwater exploration mission is considered. It has N w A sequence of waypoints, WP = {WP i ∈R 3 i = 0, ..., N w}, given by the planning algorithm, where The x, y, and z positions of the i-th waypoint are given, with WP0 ​​corresponding to the vehicle's initial position. Assume the vehicle is initialized with zero relative velocity and facing WP1. The energy-optimal control problem is formulated as follows:

[0073]

[0074] stχ=f(t,χ(t),τ(t))

[0075]

[0076] Where, χ=[η,v] T It is the state of AUV; t∈[t0,t f [] represents the flight time of the AUV; stχ = f(t,χ(t),τ(t)) represents the dynamic constraints of the problem. For the boundary constraints of the problem, To control the upper limit of input This is the upper limit of the pitch angle. This is the maximum termination time. For the path constraints of the problem, r COA For the switching radius; x(t) j ),y(t j ),z(t j ) represents the real-time position coordinates of the AUV; WP = {WP i ∈R 3 i = 0, ..., N w} represents the coordinates of the specified path points. WP0 is the initial position.

[0077] S150: Trajectory generation is performed using the direct matching method, and its superiority is verified through simulation examples. The specific construction method of the AUV energy-saving trajectory generation scheme based on the direct matching method is as follows:

[0078] In S150, we use offline trajectory planning based on the direct collocation method to solve the energy-optimal control problem proposed in S140 and generate energy- and time-optimal trajectories.

[0079] First, based on the idea of ​​the direct collocation method, the optimal control problem proposed in S140 is discretized, transforming the original continuous-time infinite-dimensional optimal control problem (OCP) into a finite-dimensional nonlinear programming problem (NLP). The time domain of trajectory optimization is divided into N equal intervals. f Segment, obtain N f +1 time-based pairing points (the time domain between every two time points is [t]). k-1 ,t k], k=0,...,N f This allows us to represent the system's state and control variables using the values ​​at specific pairing points. The following discrete sequence of variable nodes is obtained:

[0080] t→t0…t k …t f ,

[0081]

[0082]

[0083] Then, the continuous optimal control problem proposed above is rewritten in discrete form. In this way, solving the infinite-dimensional optimal control problem becomes solving a finite number of nonlinear programming problems, greatly reducing the difficulty of the solution.

[0084] Furthermore, this optimal control problem is solved using open-source solvers (such as IPOPT), yielding a series of discrete states and control points. These results are then expressed as state-space equations using Hermite polynomials for interpolation. Finally, Simpson's formula is applied to each subinterval t∈[t... k ,t k+1 State equations on Integrating, we obtain the optimal energy expectation trajectory of the AUV.

[0085] Furthermore, simulation experiments have also demonstrated the superior energy-saving properties of this trajectory.

[0086] Example 1

[0087] This invention discloses an energy-saving trajectory generation method for an underdriven AUV, comprising the following steps:

[0088] S110: Establish a five-degree-of-freedom mathematical model for underactuated AUV trajectory generation; due to the good symmetry of AUVs, the influence of roll angle is ignored, and its five-degree-of-freedom kinematic model is as follows:

[0089]

[0090] The dynamic equation is:

[0091]

[0092] Where η = [x,y,z,θ,ψ] T Let ν represent the position vector of the AUV in the Earth's fixed inertial frame, ν = [u, v, w, q, r] T Let α represent the velocity vector of the AUV in the fixed inertial frame of Earth, and let α and γ represent the angle of attack and drift angle of the AUV, respectively. ii ,dii ,i∈[1,6] are measurable hydrodynamic coefficients, ρ is the density of seawater, and g is the acceleration due to gravity. It is the drainage volume. It is the longitudinal center of gravity height, and the control input τ = [τ u ,0,0,τ q , τ r ] T .

[0093] S120, Analysis of the energy consumption mechanism during AUV navigation.

[0094] The energy consumption of an AUV during navigation mainly comes from the following sources:

[0095] (1) Basic computing energy consumption: generated by industrial control computer and basic circuits;

[0096] (2) Task payload energy consumption: generated during the operation of each sensor;

[0097] (3) Energy consumption generated by the work done by the AUV actuators during navigation and energy consumption to overcome navigation resistance;

[0098] It should be noted that the first three types of energy consumption are usually fixed or have little potential for energy saving under a given operational task. Therefore, we consider reducing the navigation energy consumption of AUVs during operational tasks at the control strategy level.

[0099] Please see Figure 2 As shown, a thrust-power curve fitting scheme for the S130 AUV is performed.

[0100] This paper uses a DC motor-driven duct thruster for simulation analysis. Its power conversion ratio β = 0.4507, which was obtained from the thrust-power experimental data measured in an open-water test in a circulating water tank. The simulation analysis was based on the empirical model P. T =β(X) s ) 1.5 It is derived from fitting, where X s =X |u∣u u 2 ,like Figure 3 As shown.

[0101] Furthermore, the energy consumption caused by different angles of attack α during AUV navigation is also different; this invention will provide an example with a real AUV type, such as... Figure 4 , 5 As shown, the drag of the AUV under different flight attitudes in this embodiment was calculated by CFD numerical simulation, providing a basis for energy-saving control.

[0102] Solving using the nonlinear least squares method yields curves showing the increase in drag at different angles of attack relative to a 0° angle of attack, as well as an approximate relationship between the angle of attack α and the increase in longitudinal drag ΔX:

[0103] ΔX = -0.002315α 3 +0.05841α 2

[0104] In the formula, u and w are the longitudinal and vertical velocities of the AUV, respectively;

[0105] The data obtained from CFD simulation clearly shows that the angle of attack of an AUV is positively correlated with its drag curve. Therefore, if we want to reduce the energy consumption caused by drag, we need to make the angle of attack of the AUV as small as possible during navigation.

[0106] In this paper, the power of sensors and basic circuits other than the thruster is set to a fixed value P. e This portion of energy consumption can be obtained by integrating over the flight time. Therefore, the overall energy consumption of an AUV during flight can be calculated as follows:

[0107]

[0108] In the formula, W auv W represents the total energy consumption of the AUV system when performing a task. R W represents the energy consumed due to drag during navigation. e Other energy consumption, t0, t f These are the start and end times of the voyage. This represents the speed of the AUV.

[0109] S140, the specific construction method for the optimal control problem that the AUV needs to solve is as follows:

[0110] Based on the mathematical model of the AUV and the analysis and energy consumption model constructed in S110, S120, and S130, a multi-waypoint underwater exploration mission is considered. It has N w A sequence of waypoints, WP = {WP i ∈R 3 i = 0, ..., N w}, given by the planning algorithm, where The x, y, and z positions of the i-th waypoint are given, with WP0 ​​corresponding to the vehicle's initial position. Assume the vehicle is initialized with zero relative velocity and facing WP1. The energy-optimal control problem is formulated as follows:

[0111]

[0112] In the formula, χ=[η,ν]T It is in AUV mode. These are the upper limits for the pitch angle and termination time, while the remaining formulas represent the AUV dynamic constraints, boundary constraints, and path constraints, respectively.

[0113] In section S150, we use offline trajectory planning based on the direct collocation method to solve the energy-optimal control problem proposed in S140 and generate energy- and time-optimal trajectories.

[0114] First, based on the idea of ​​the direct collocation method, the optimal control problem proposed in S140 is discretized, transforming the original continuous-time infinite-dimensional optimal control problem (OCP) into a finite-dimensional nonlinear programming problem (NLP). The time domain of trajectory optimization is divided into N equal intervals. f Segment, obtain N f +1 time-based pairing points (the time domain between every two time points is [t]). k-1 ,t k ], k=0,...,N f This allows us to represent the system's state and control variables using the values ​​at specific pairing points. The following discrete sequence of variable nodes is obtained:

[0115] t→t0…t k …t f ,

[0116]

[0117]

[0118] Then, considering the kinematic model constraints of the AUV system, with the terminal state fixed and the terminal time t... f The variable boundary constraints and path constraints passing through fixed path points rewrite the aforementioned optimal control problem in discrete form, as shown in the following equation:

[0119]

[0120] stη k+1 =f dt ·(t k ,η k ,v k ),η0=WP0

[0121]

[0122] In the formula, f dt • This is the discrete-time vehicle kinematics obtained according to the time step Δt.

[0123] Furthermore, by solving this optimal control problem using open-source solvers (such as IPOPT), the optimal control variables and state variables of the AUV performing the target task under various constraints can be obtained.

[0124] Then, in [t0,t f The continuous time is divided into N. f The segment, each sub-interval is [t] k ,t k+1 ], k = 0, 1, ..., N f -1. Let h k =t k+1 -t k , Then s∈[0,1]. Within this subinterval, each state variable can be represented by a cubic Hermite polynomial:

[0125] η(t)=c k,0 +c k,1 s+c k,2 s 2 +c k,3 s 3

[0126] Among them, c k,i denoted as the coefficient to be determined.

[0127] From the boundary condition η k =η(0),η k+1` =η(1), It can be concluded that:

[0128]

[0129] By selecting the midpoint of each sub-interval as the matching point, we can obtain:

[0130]

[0131] Using Simpson's formula for subintervals t∈[t k ,t k+1 State equations on Integrating, we obtain the expected trajectory of the AUV in each sub-interval as follows:

[0132]

[0133] In summary, we can obtain that AUV in t∈[t0,t f Optimal energy trajectory η d (t):

[0134]

[0135] Where, ηd (t)=[x d ,y d ,z d ] T It is bounded, η i (t), where i = 0, 1, 2, ..., f-1, represents the trajectory of each segment. It also possesses bounded first and second derivatives with respect to time, and there exists a positive constant B0 satisfying...

[0136] The feedforward trajectory generated by the direct matching method described above is under ideal conditions (known and fixed model parameters, no external disturbances, and actuator saturation). This method generates an open-loop feedforward trajectory, which does not consider the complex and variable marine environment. Therefore, the resulting control input cannot be directly applied to the AUV system. However, the generated energy-saving trajectory η... d (t) The desired trajectory for subsequent controller design can still reduce energy consumption during AUV mission execution.

[0137] To verify the advancement of the proposed energy consumption model, we consider the multi-waypoint tracking task problem proposed in S150. By considering only two waypoints at a time for trajectory generation, we can more intuitively compare the performance of different energy consumption evaluation functions. We conduct three sets of comparative experiments: minimizing thrust, minimizing time, and the proposed method.

[0138] #1)

[0139] #2) minJ2=t f -t0

[0140] #3)

[0141] The power conversion ratio of the propulsion system, measured in the previous pool experiment, is β = 0.4507. The remaining energy consumption P generated during navigation... e =0.5W.

[0142] We can assume that the waypoints are known prior to us, and select the following waypoints: WP1 = (0,0,0), WP2 = (50,10,30), WP3 = (0,30,0). Set the initial position and attitude of the AUV as η0 = [x,y,z,ψ,θ]. T =[0,0,0,0,0] T The initial velocity is v0 = [u, v, w, q, r] T =[0,0,0,0,0] T The constraint parameters are selected as follows: r COA =2m. Number of matching points N f 200 were selected.

[0143] The energy consumption and corresponding AUV trajectories generated by trajectory generation under the same constraints using different evaluation functions are as follows: Figure 7 As shown, the corresponding speed is from Figure 6 As can be seen from the results, all three sets of energy consumption evaluation functions can reach the waypoints in a satisfactory state under the same constraints. Based on this, compared to the energy consumption evaluation functions used in #1 and #2, the method proposed in this paper achieves better results in terms of thruster energy consumption W. thrust Sailing time t f and average angle of attack It has better overall performance in terms of change rate, improving performance by nearly 48.13% compared to energy consumption evaluation function #1.

[0144] Furthermore, based on this, considering the problem of multipath tracking tasks, we should consider setting path points:

[0145] WP1=(0,0,0),WP2=(50,0,30),WP3=(50,10,30),

[0146] WP4=(0,10,0),WP5=(0,20,0),WP6=(50,20,30),WP7=(50,30,30),WP8=(0,30,0)

[0147] Assume that a steady ocean current U exists in the environment. c =1.2m / s, ψ c =0.5rad,θ c =0, setting the maximum speed V that the AUV can achieve. max =2m / s, the switching radius r is set to 2m. The energy-saving trajectory generator proposed in this invention is compared with other similar energy-saving methods combined with the same feedback controller. The comparison results are as follows: Figure 8 As shown, it is clear that the proposed solution improves energy efficiency by 5.7% compared to methods that optimize airspeed, and by 32.9% compared to traditional LOS guidance methods. This demonstrates that the trajectory generator proposed in this invention has a higher energy-saving effect, meaning it can better handle the energy-saving trajectory generation problem of underactuated AUVs under multi-objective constraints.

[0148] The foregoing has described one embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent variations and improvements made within the scope of the present invention should fall within the patent coverage of the present invention.

Claims

1. A method for generating an energy-saving trajectory for an underactuated AUV, characterized in that: Includes the following steps: S110: Constructing a five-degree-of-freedom mathematical model for the underactuated AUV energy-saving trajectory generation task; S120: Analysis of energy consumption of underactuated AUVs during navigation; S130: Constructing a drag and energy consumption model for underactuated AUVs; S140: Combining the above steps and the specified path point information, list the energy optimal control problem that needs to be solved; The energy optimal control problem is expressed as follows: stχ=f(t,χ(t),τ(t)) Where, χ=[η,ν] T It is the state of AUV; t∈[t0,t f [This refers to the AUV's flight time;] This represents the increase in energy consumption of an underactuated AUV due to an increase in angle of attack. The propulsion energy consumption is used to overcome the longitudinal drag of the hull; Other energy consumption; stχ=f(t,χ(t),τ(t)) is the dynamic constraint of the problem; For the boundary constraints of the problem, To control the upper limit of input, This is the upper limit of the pitch angle. This is the maximum termination time. For the path constraints of the problem; r COA For the switching radius; x(t) j ),y(t j ),z(t j ) represents the real-time position coordinates of the AUV; WP = {WP i ∈R 3 i = 0, ..., N w } specifies the coordinates of the path point, WP i =(WP i x ,WP i y ,WP i z ), WP0 is the initial position; S150: Based on the direct combination method, the continuous-time infinite-dimensional energy optimal control problem is discretized into a finite-dimensional nonlinear programming problem, and the optimal trajectory with the minimum energy consumption and shortest time based on the original path point is generated by the solver.

2. The method for generating an underactuated AUV energy-saving trajectory according to claim 1, characterized in that: In S110, the five-degree-of-freedom model is: Where η = [x,y,z,θ,ψ] T This represents the position information of the underactuated AUV; ν = [u, v, w, q, r] T The speed information is for the underactuated AUV; α and γ are the angle of attack and drift angle of the underactuated AUV, respectively; m ii d ii , i∈[1,6] is the hydrodynamic coefficient; ρ is the density of seawater; g is the gravitational acceleration; This refers to the drainage volume; For longitudinal steady center height; control input τ = [τ u ,0,0,τ q ,τ r ] T .

3. The energy-saving trajectory generation method for an underactuated AUV according to claim 2, characterized in that: In S120, the energy consumption of the underactuated AUV during navigation includes basic calculation energy consumption, mission payload energy consumption, energy consumption generated by the work done by the underactuated AUV actuators during navigation, and energy consumption to overcome navigation resistance; the navigation resistance energy consumption includes the work done by the propellers to overcome the longitudinal resistance of the hull. And the increase in energy consumption W due to the increase in angle of attack. d .

4. The energy-saving trajectory generation method for an underactuated AUV according to claim 3, characterized in that: In S130, the propeller energy consumption to overcome the longitudinal resistance of the hull Where β is the power conversion ratio; u is the longitudinal velocity; X |u|u t is the longitudinal drag coefficient of the underactuated AUV; t0 is the start time of flight; t f This refers to the time the ship stops. The energy consumption increase W of the underactuated AUV due to the increased angle of attack d for: Where ΔX is the longitudinal drag of the underdriven AUV fitted by the least squares method; For underdriven AUV speed; The total energy consumption W of the underactuated AUV during navigation auv : Among them, W R Energy consumed to overcome resistance during navigation; W e Other energy consumption.

5. The energy-saving trajectory generation method for an underactuated AUV according to claim 4, characterized in that: S150 specifically includes the following steps: S150.1: Divide the time domain of trajectory optimization into N equal intervals. f Segment, obtain N f +1 time-based collating points, with the time domain between every two time points being [t]. k-1 ,t k ], k=0,...,N f ; S150.2: Rewrite the optimal control problem proposed in S140 in discrete form; st k+1 =f dt ·(t k ,or k ,v k ),η0=WP0 Among them, f dt The discrete-time vehicle kinematics are obtained according to the time step Δt; WP0 is the initial position; η represents the upper bound of the AUV's velocity and angular velocity. mj The m-th discrete value j One state point; x mj ,y mj ,z mj N represents the real-time location of the AUV. w This represents the number of waypoint sequences. S150.3: In [t0,t f The continuous time is divided into N segments. f The segment, each sub-interval is [t] k ,t k+1 ], k = 0, ..., N f -1; the matching point of each sub-interval Among them, h k =t k+1 -t k ; S150.4: Using Simpson's formula for subintervals t∈[t k ,t k+1 State equations on Integrate to obtain the expected trajectory of the AUV in each sub-interval: Obtain AUV in t∈[t0,t f Optimal energy trajectory η d (t): Where, η d (t)=[x d ,y d ,z d ] T It is bounded, η i Let (t) be the trajectory of each segment, i = 0, 1, 2, ..., f-1, and have bounded first and second derivatives with respect to time. There exists a positive constant B0 that satisfies...

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