Underwater robot energy consumption optimization path planning method

Abstract

The invention discloses an energy consumption optimization path planning method for an underwater robot. According to the method, an ocean current model is established by using a Navier-Stokes equation, and complex water flow in the ocean is simulated; a mountain peak model is used for simulating complex submarine topography, and a sphere model is used for simulating random obstacles in the ocean; the outer layer of the path planner adopts a particle swarm optimization algorithm, the inner layer of the path planner adopts a sequential quadratic programming algorithm, and the optimal speed of each section of path can be obtained while path points are obtained; fitting path points by using a B-spline curve to generate a smooth path; and a special fitness function calculation mode is adopted, and a re-planning strategy is combined, so that the calculation efficiency of the algorithm is improved. According to the method, the optimal navigation speed of the robot in different environments can be calculated, the energy consumption of the planned path is reduced on the basis of ensuring the navigation time and safety of the robot, and the efficiency and robustness of path planning are effectively improved through a multi-algorithm combined double-layer path planner and a dynamic re-planning mechanism.

Description

Technical Field

[0001] The invention relates to an underwater robot energy consumption optimization path planning method, belonging to the technical field of robot path planning. Background Art

[0002] Since autonomous underwater vehicles (AUVs) have limited energy, planning low-energy paths can extend their operating time and range. AUV path planning is mainly divided into two categories: global path planning and local path planning. Common methods for global path planning include Dijkstra algorithm and heuristic search algorithm, but the former has high computational complexity, and the latter is suitable for static environments and does not consider dynamic factors such as ocean currents; local path planning methods such as RRT and artificial potential field method can cope with dynamic obstacles, but there are problems such as easy to fall into local minima and strong search blindness.

[0003] The main problems in existing research on AUV path planning include: insufficient modeling of complex ocean environments; difficulty in balancing real-time and robustness in path planning algorithms; and energy consumption optimization only considers path length and does not fully integrate the AUV kinematic model. The above problems limit the improvement of AUV's energy efficiency and stability in complex environments. Summary of the invention

[0004] The purpose of the present invention is to provide an underwater robot energy consumption optimization path planning method, aiming to solve the problems of insufficient robustness, imperfect complex environment modeling and incomplete energy consumption optimization in existing path planning algorithms.

[0005] In a first aspect, the present invention provides an underwater robot energy consumption optimization path planning method, comprising the following steps:

[0006] Step 1: Establish an ocean current model based on the Navier-Stokes equations, combine the mountain model to simulate the complex seabed topography, and use the sphere model to simulate randomly distributed obstacles in the ocean to form a complex ocean environment model.

[0007] Step 2: Clarify the kinematic constraints of the AUV and establish an energy consumption model based on the relationship between thruster power and speed to support energy efficiency optimization in path planning.

[0008] Step 3: Design a path planner and use the improved energy-optimal particle swarm optimization algorithm (EOPSO) to search for feasible paths in the three-dimensional solution space. Apply the sequential quadratic programming algorithm to the global path to solve the optimal navigation speed for each segment that meets the time constraint. The optimization goal is to minimize the total energy consumption of the propeller.

[0009] Step 4: Use B-spline curve to fit the generated path points to generate a smooth path, thereby reducing the increase in energy consumption caused by the robot's path deviation.

[0010] Furthermore, the three-dimensional seabed topography data is based on the ETOPO1 data set of the real sea area, and the seabed depth information matrix is ​​obtained through grid discretization. The AUV depth is restricted during path planning so that the AUV maintains a preset safe distance from the seabed.

[0011] Furthermore, the ocean current field in the ocean current model is constructed using the Lamb vortex model, and the vortex intensity, center position and vortex radius are superimposed and simulated based on historical ocean current data, ignoring the vertical component, and assuming that the ocean current exists stably during the AUV's mission.

[0012] Furthermore, the energy consumption model of the AUV is based on the cubic growth trend of power and speed measured by the thruster experiment, and the total energy consumption is calculated by performing piecewise integration calculation on the power distribution of different thrusters.

[0013] Furthermore, the improved particle swarm optimization algorithm dynamically adjusts the inertia weight coefficient by detecting whether the next iteration position of the particle exceeds the predefined search space, and triggers replanning when there is no improvement in the global optimal solution for multiple consecutive generations, so as to reinitialize the particle swarm.

[0014] Furthermore, a fitness function is used to evaluate the pros and cons of each path, and the fitness function simultaneously considers the AUV energy consumption, obstacle safety distance, and seabed terrain safety distance.

[0015] Furthermore, the sequential quadratic programming algorithm uses the maximum navigation time as a constraint to perform nonlinear optimization on the water speed of the AUV on each path, and calculates the actual energy consumption of each path according to the AUV's ground speed, thruster power and ocean current direction component.

[0016] Furthermore, it also includes the introduction of dynamic programming strategies to dynamically update the path to ensure the safety and efficiency of AUV navigation when the path is affected by dynamic obstacles or sudden changes in the environment.

[0017] In a second aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy consumption optimization path planning method for an underwater robot when executing the program.

[0018] In a third aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the underwater robot energy consumption optimization path planning method.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] (1) The present invention establishes an ocean current model based on the Navier-Stokes equations and combines the mountain model and the sphere model to simulate complex seabed terrain and obstacles, which can more realistically reflect the complexity of the underwater environment and improve the applicability of the path planning algorithm.

[0021] (2) The present invention adopts a two-level planning strategy that combines an improved energy-optimal particle swarm optimization algorithm (EOPSO) with a sequential quadratic programming algorithm (SQP), which can not only ensure the global optimality of the path, but also dynamically respond to complex environmental changes, thereby improving the robustness of the planner.

[0022] (3) The present invention establishes an accurate energy consumption model, taking into account the kinematic and dynamic characteristics of the underwater robot in different environments, and can calculate the optimal navigation speed, achieve global optimization of path energy consumption, and improve energy utilization efficiency.

[0023] (4) By fitting the path points with a B-spline curve, the present invention generates a smooth navigation path, thereby avoiding increased energy consumption and navigation instability caused by excessive path turning angles.

[0024] (5) The present invention designs a dynamic planning strategy that can update the path in real time when the path is affected by dynamic obstacles or environmental changes, thereby ensuring the safety of the underwater robot and the real-time performance of path planning.

[0025] (6) By designing a special fitness function and a multi-algorithm combination strategy, the present invention greatly improves the computational efficiency of the path planning algorithm and is suitable for large-scale and long-term underwater operation tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the appearance and thruster distribution of the AUV of the present invention.

[0027] Figure 2 It is an overall flow chart of the present invention from environment modeling to path planning algorithm to output of the energy-optimal path.

[0028] Figure 3 It is a schematic diagram of the three-dimensional ocean model and obstacles of the present invention.

[0029] Figure 4 It is a schematic diagram of the re-planning mechanism of the PSO algorithm of the present invention.

[0030] Figure 5 It is a comparison chart of simulation results between the algorithm of the present invention and the traditional algorithm. DETAILED DESCRIPTION

[0031] like Figure 1As shown, the AUV involved in the embodiment of the present application has six thrusters, including a first forward main thruster 1, a second forward main thruster 2, a first vertical thruster 3, a second vertical thruster 5 and a third vertical thruster 6 and a lateral thruster 4; wherein the driving shafts of the first forward main thruster 1, the second forward main thruster 2 and the lateral thruster 4 are parallel to the horizontal plane, which can realize the robot's independent movements of three degrees of freedom: horizontal straight navigation, translation and bow shaking, and can perform combined movements of three degrees of freedom; the driving shafts of the first vertical thruster 3, the second vertical thruster 5 and the third vertical thruster 6 are perpendicular to the horizontal plane, which can control the independent movements of the AUV's three degrees of freedom: vertical heave, roll, rotation and pitch.

[0032] For the above-mentioned AUV, this application provides an energy-optimized path planning method. The core innovations of this application include: the establishment of a complex ocean environment model, a path planner combining an improved energy-optimal particle swarm optimization algorithm (EOPSO) and a sequential quadratic programming algorithm, and a dynamic planning strategy based on energy optimization. While ensuring the accuracy and safety of path planning, this application significantly reduces the energy consumption of underwater robots and improves the robustness and adaptability of path planning.

[0033] In one embodiment, the method of the present application comprises the following steps:

[0034] Step 1: Establish an ocean current model based on the Navier-Stokes equations, combine the mountain model to simulate the complex seabed topography, and use the sphere model to simulate randomly distributed obstacles in the ocean to form a complex ocean environment model.

[0035] This implementation is based on real terrain data and uses the seabed depth information in the ETOPO1 data set of real sea areas. The acquired seabed terrain data is gridded to form a discretized matrix corresponding to two-dimensional coordinates and depths, and the AUV depth is restricted during path planning so that the AUV maintains a preset safe distance from the seabed.

[0036] Furthermore, a mountain model generation algorithm is used to construct the three-dimensional seabed topography:

[0037]

[0038] in Indicates the points with corresponding coordinates in the map. are the randomly generated peak center coordinates, represents the number of mountain models, is the sequence number of the mountain. is the coefficient controlling the peak height, In order to determine the coefficient of the mountain slope, typical terrains including trenches and seamounts are generated by adjusting parameters.

[0039] Based on the simplification of the Navier-Stokes equations and the idea of ​​vortex superposition, the ocean current field is constructed, and the viscous Lamb vortex is introduced to simulate the local ocean current. Based on the historical ocean current data, the vortex intensity, center position, vortex radius and other parameters are superimposed and simulated. The vertical component is ignored, and it is considered that the ocean current exists stably during the AUV mission:

[0040]

[0041]

[0042]

[0043] in, are the eddy current velocity components in the horizontal, longitudinal and vertical directions respectively, , , are the coordinates of the eddy intensity, eddy radius, and eddy center position respectively. Represent the east-west and north-south components respectively, represents the distance from the vortex center to a certain point, represents the coordinates of the vortex center. If is greater than 0, the vortex is counterclockwise; If it is less than 0, the vortex is in clockwise direction.

[0044] In order to enhance the complexity and authenticity of the simulation environment, several spherical obstacles are randomly placed in the three-dimensional environment:

[0045]

[0046] in, Indicates The center position of the obstacle, Representing three-dimensional space The area in Indicates its radius. Position and radius They are randomly drawn from a given probability distribution to reflect the uncertainty and randomness of the real ocean environment. Figure 3 shown.

[0047] Step 2: Clarify the kinematic constraints of the AUV and establish an energy consumption model based on the relationship between thruster power and speed to support energy efficiency optimization in path planning.

[0048] This embodiment uses Figure 1 Take the fully-drive Stingray II AUV shown as an example. It uses six thrusters to achieve full-degree-of-freedom control of forward and backward movement, steering, lateral translation, heave and sink, and attitude adjustment.

[0049] AUV ground speed The speed of the AUV body against water and ocean current speed It is obtained by superposition, which is specifically expressed as the sum of the components in the AUV navigation direction (in the horizontal plane) and the vertical direction. If the local ocean current speed exceeds a certain range, the area is regarded as a strong current obstacle area that cannot be entered or needs to be avoided.

[0050] The AUV positioning system uses a Kalman wave detector framework. When the AUV water speed is 0.3 to 2 m / s, the positioning error of the basic positioning mode does not exceed 5‰. .

[0051] In addition, seafloor topography and obstacle restrictions must be considered:

[0052]

[0053]

[0054] in, Represents a path segment. represents the distance between the AUV and the obstacle center, is the radius of the obstacle, is the safe distance from obstacles. Indicates the depth of a point on the path. Indicates the height of the AUV relative to the seabed. The safety height is set.

[0055] In order to accurately evaluate the energy consumption of the full-drive AUV under different speeds and ocean current conditions, this embodiment first conducts power experiments on the six thrusters of the AUV (including the main thruster, lateral thruster and vertical thruster) in a still water environment, and measures their energy consumption at different speeds.

[0056] After comparing the measured data with the theoretical resistance model, it was found that the AUV thruster Power It increases cubically with the speed, which can be used describe.

[0057] in is the efficiency of the propulsion system, is the drag coefficient, which depends on the shape and surface properties of the AUV, is the AUV cross-sectional area, is the AUV’s water speed, is the fluid density.

[0058] The six thrusters of the AUV are divided into three groups, including two main thrusters (the first forward main thruster 1 and the second forward main thruster 2) that provide forward and steering power, with a total power of , the power of the lateral thruster (lateral thruster 4) to resist the lateral ocean current is and three vertical thrusters (first vertical thruster 3, second vertical thruster 5 and third vertical thruster 6) for controlling the heave motion, with a total power of The global total energy consumption is defined as follows:

[0059]

[0060] in Indicates the time of beginning, Indicates the moment of ending, Indicates time The instantaneous power.

[0061] If there is an ocean current on this path segment, the AUV's ground speed is the superposition of the AUV's own water speed and the speed component of the ocean current in the AUV's navigation direction. The lateral and vertical thrusters are used to resist the lateral component of the ocean current and realize the AUV's vertical movement, respectively. Therefore, the final energy consumption can be regarded as the sum of the integrals of each thruster power over the corresponding time. The actual speed of the AUV is expressed as , represents the AUV ground speed, which is calculated as follows:

[0062]

[0063] in represents the AUV’s speed over water, represents the speed of the ocean current, A vector representing a path segment, with a direction given by point to ,The component of the ocean current speed in the AUV speed direction affects the actual navigation speed of the AUV.

[0064] By combining the above experimental curves and speed decomposition relationships, the total energy consumption of the AUV in complex flow fields and multi-dimensional motion modes can be accurately estimated, thereby providing a basis for energy consumption optimization for subsequent path planning.

[0065] Step 3: Design a path planner and use the improved energy-optimal particle swarm optimization algorithm (EOPSO) to search for feasible paths in the three-dimensional solution space. Apply the sequential quadratic programming algorithm to the global path to solve the optimal navigation speed for each segment that meets the time constraint. The optimization goal is to minimize the total energy consumption of the propeller.

[0066] In the traditional particle swarm optimization algorithm (PSO), each particle represents a potential solution. Through continuous iteration, the particle continuously searches in the solution space. Particles have two main parameters: position and and speed In the iterative process, the particle passes through the best position in its personal experience (the individual cognitive part ) and the best position in the group (social cognition part ) to adjust its speed and position, which can be described by the following formula:

[0067]

[0068]

[0069] Where: is the velocity of the particle at a certain moment, is the position of the particle at a certain moment, is the inertia weight coefficient, which describes the degree of inheritance of the particle velocity at the previous moment. is the learning factor, is the individual optimal position and the global optimal position at the tth iteration, Is a random number.

[0070] For each particle, the safe distance between the AUV and obstacles, the seabed terrain constraints, and the preliminary energy consumption evaluation are comprehensively considered during the update and iteration process, and the fitness function is used to evaluate the pros and cons of each path.

[0071] The improved energy-optimal particle swarm optimization algorithm (EOPSO) takes the energy consumption of AUV as the main part of the fitness function based on the particle swarm optimization algorithm. The fitness function is defined as follows:

[0072]

[0073] in, For a specific particle solution, is the total energy consumption of AUV, Represents the penalty function corresponding to the minimum safe distance between the path point and the obstacle, is the weight coefficient of the safety distance function, The penalty function corresponding to the minimum safe distance between the path point and the seabed terrain, is its weight coefficient, which controls the influence of obstacle safety distance in the fitness function.

[0074] For a specific particle solution , the penalty function of the obstacle threat to the AUV The form is as follows:

[0075]

[0076]

[0077] Penalty function of seabed terrain on AUV threat The form is as follows:

[0078]

[0079]

[0080] in Represents a path segment. is the number of path segments, represents an infinitesimal positive number, is the number of intermediate path points, represents the distance between the path and the obstacle, Represents the particle solution No. Waypoints, Indicates the depth of the point. Indicates the depth of the seabed at that point.

[0081] like Figure 2 The figure shows the overall flow chart of this implementation. By evolving the particle swarm for multiple generations, several feasible candidate paths with low energy consumption are obtained. If it is detected during the iteration that the particle is about to exceed the predefined search space, the adaptive adjustment of the inertia weight coefficient is triggered. If it is trapped in the local optimum for a long time, that is, there is no improvement in the global optimal solution for multiple generations, the re-planning mechanism is triggered to ensure the global search capability. Figure 4 FIG. 4 is a block diagram of a re-planning mechanism of this embodiment.

[0082] On the basis of obtaining the corresponding optimal or approximately optimal path, in order to further reduce energy consumption and meet the maximum navigation time limit, the sequential quadratic programming (SQP) algorithm is used to perform secondary optimization on the navigation speed of each section on the path. represents the path segment between two adjacent points on the discrete path, and the AUV moves at the water speed Through this path, the distribution of this path is a steady flow field, and the speed of the ocean current is , the angle between the speed of AUV and the speed of ocean current is At this time, the AUV's ground speed is and the lateral speed to be resisted They are:

[0083]

[0084]

[0085] The following nonlinear optimization problem is constructed:

[0086]

[0087]

[0088] in is the energy consumption in the i-th segment, represents the length of the path segment, is the number of path segments, is the average speed of the AUV in the path segment, The maximum flight time is limited. are the minimum and maximum navigation speeds of the AUV, respectively.

[0089] According to the cubic resistance model combined with the sailing time By integrating and summing, the total energy consumption of AUV can be approximated as:

[0090]

[0091] in and are the power calculation coefficients of the main thruster, lateral thruster and vertical thruster, respectively. Indicates the vertical height change of the path segment.

[0092] The above nonlinear optimization problem is iterated into a series of quadratic programming (QP) sub-problems through SQP. The algorithm flow mainly includes initialization , construct the Lagrangian function and obtain its gradient or Hessian matrix approximation, and repeatedly update the velocity vector Until the convergence condition is met or the maximum number of iterations is reached, the speed of each segment is finally output To achieve comprehensive optimization of energy consumption and time constraints.

[0093] In summary, a feasible and initially low-energy three-dimensional trajectory can be obtained through EOPSO, and the optimal speed distribution of each path segment can be refined through SQP optimization, thereby significantly reducing the total energy consumption of the AUV in complex ocean environments. If the feasibility of individual path segments is found to be restricted by external conditions (such as exceeding the vertical speed safety range) during SQP solution, the speed of the segment is constrained and corrected and re-iterated. The final path and speed settings can ensure that the AUV can reach the maximum speed within a given time. Complete underwater tasks with the lowest possible energy consumption.

[0094] Step 4: Use B-spline curve to fit the generated path points to generate a smooth path, thereby reducing the increase in energy consumption caused by the robot's path deviation.

[0095] In the path planning process, multiple path points are obtained through the improved energy-optimal particle swarm optimization algorithm (EOPSO), which represent the key positions that the AUV needs to pass through in three-dimensional space.

[0096] Discretize the waypoints to ensure that they are reasonably distributed in space and can effectively reflect the real trajectory of the AUV in the marine environment. Adjust the density of the waypoints according to the actual situation of the path, such as the motion restrictions of the AUV and the characteristics of the environment.

[0097] The obtained path points are used as control points of the B-spline curve, and the path fitting is performed using the B-spline interpolation method. The basic formula of the B-spline curve is as follows:

[0098]

[0099] in, is the smooth curve fitted, It is basis functions, is the order of the spline curve, is the normalization parameter of the curve parameter space.

[0100] The resulting smooth path is optimized to ensure that the curvature of the path changes as gently as possible. The turning angle on the path should be minimized to avoid unnecessary energy consumption. The energy consumption of the AUV when turning is further reduced by optimizing the smoothness and curvature of the path. The curvature of the path It can be calculated by the following formula:

[0101]

[0102] in, and are the first and second derivatives of the path, respectively. The larger the value, the sharper the turn of the path, and the greater the energy consumption. Therefore, when optimizing, make sure the curvature is as small as possible.

[0103] Finally, the path after B-spline curve fitting and optimization will be output as the actual navigation path of the AUV. This smooth path can be directly input into the subsequent control system or further path optimization stage to ensure that the AUV can navigate efficiently and safely in complex marine environments.

[0104] Step 5: Introduce a dynamic programming strategy. When the path is affected by dynamic obstacles or sudden changes in the environment, the path is dynamically updated to ensure the safety and efficiency of the underwater robot's navigation.

[0105] During the navigation process of the AUV, sensors (sonar, inertial navigation, Doppler velocimeter, etc.) are used to detect changes in the surrounding environment in real time, including dynamic obstacles, changes in ocean currents, changes in seabed topography, etc. The environmental information is fed back to the path planning system in real time to ensure that the system can respond when sudden changes in the environment occur.

[0106] During the path planning process, the detection threshold of dynamic obstacles is set. If the current path of the AUV is affected by new obstacles or environmental changes, dynamic planning is performed.

[0107] In order to improve the efficiency of dynamic programming, a multi-level strategy is adopted. First, the particle swarm optimization algorithm is used to quickly adjust the path in the local range; if the environment changes greatly, the energy consumption optimal particle swarm optimization algorithm (EOPSO) is used for global path planning.

[0108] After the new path is generated, it is verified to ensure that it meets the kinematic and energy constraints of the AUV. After verification, the path control system immediately executes the new path planning.

[0109] Similarly, based on the concept of the method embodiment, the embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements an energy-optimized path planning method for an underwater robot when executing the program.

[0110] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function.

[0111] Similarly, based on the concept of the method embodiment, the embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, a method for optimizing the path planning of an underwater robot for energy consumption is implemented.

[0112] It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the active distribution network state estimation method based on the fusion of GCN and Transformer in the above embodiment.

[0113] Simulation Example

[0114] Figure 5 This is a comparison chart of the results of different path planning algorithms in the simulation embodiment of the present invention. It can be seen from the figure that for the paths planned by the GA and PSO algorithms, the ocean current has little effect on the navigation speed of the AUV, but the AUV is greatly affected by the lateral component of the ocean current, while the paths planned by the EOPSO algorithm mostly sail in the downstream direction, and the trajectory mostly coincides with the direction of the ocean current, thereby improving the water speed and saving energy consumption.

[0115] In summary, the present invention can generate a smooth path with the lowest energy consumption for an underwater robot in a complex marine environment, calculate the optimal navigation speed in different environments, and improve the efficiency and robustness of path planning. Through simulation verification, energy consumption is reduced by 15%-30% in different scenarios.

[0116] The present invention establishes a more realistic marine environment model by introducing complex environmental factors such as ocean currents, seabed topography, and obstacles; and combines a multi-algorithm optimization path planner to enable it to quickly generate low-energy paths in complex environments. The present invention is suitable for underwater robots that require efficient and robust path planning, especially in tasks that require energy saving and real-time planning, such as offshore operations and large-scale observations.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the path planning of an underwater robot for energy consumption, characterized in that: The method comprises the following steps: 1) Based on the Navier-Stokes equations, an ocean current model is established, the mountain model is combined to simulate the complex seabed topography, and the sphere model is used to simulate randomly distributed obstacles in the ocean to form a complex ocean environment model; 2) Clarify the kinematic constraints of the AUV and establish an energy consumption model based on the relationship between propeller power and speed to support energy efficiency optimization in path planning; 3) Design a path planner, use an improved energy-optimal particle swarm optimization algorithm to search for feasible paths in the three-dimensional solution space, apply a sequential quadratic programming algorithm to the global path, and solve the optimal navigation speed for each segment that meets the time constraint. The optimization goal is to minimize the total energy consumption of the propeller; 4) Use B-spline curve to fit the generated path points to generate a smooth path, thereby reducing the increase in energy consumption caused by the robot's path deviation.

2. The method according to claim 1, characterized in that The three-dimensional seabed topography data is based on the ETOPO1 data set of the real sea area. The seabed depth information matrix is ​​obtained through grid discretization, and the AUV depth is restricted during path planning so that the AUV maintains a preset safe distance from the seabed.

3. The method according to claim 1 or 2, characterized in that: The ocean current field in the ocean current model is constructed using the Lamb vortex model. The vortex intensity, center position and vortex radius are superimposed and simulated based on historical ocean current data. The vertical component is ignored, and it is assumed that the ocean current exists stably during the AUV's mission.

4. The method according to claim 1, characterized in that The energy consumption model of the AUV is based on the cubic growth trend of power and speed measured by the thruster experiment, and the total energy consumption is obtained by performing piecewise integral calculation on the power distribution of different thrusters.

5. The method according to claim 1 or 4, characterized in that: The improved energy-optimal particle swarm optimization algorithm dynamically adjusts the inertia weight coefficient by detecting whether the next iteration position of the particle exceeds the predefined search space, and triggers replanning when there is no improvement in the global optimal solution for multiple consecutive generations, so as to reinitialize the particle swarm.

6. The method according to claim 5, characterized in that A fitness function is used to evaluate the pros and cons of each path, and the fitness function simultaneously considers AUV energy consumption, obstacle safety distance, and seabed terrain safety distance.

7. The method according to claim 1, characterized in that The sequential quadratic programming algorithm uses the maximum navigation time as a constraint to perform nonlinear optimization on the water speed of the AUV on each path, and calculates the actual energy consumption of each path according to the AUV's ground speed, thruster power and ocean current direction component.

8. The method according to claim 1, characterized in that It also includes the introduction of dynamic programming strategies to dynamically update the path to ensure the safety and efficiency of AUV navigation when the path is affected by dynamic obstacles or sudden changes in the environment.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the energy consumption optimization path planning method for the underwater robot according to any one of claims 1 to 8 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy consumption optimization path planning method for an underwater robot according to any one of claims 1 to 8 is implemented.

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