Ocean robot energy optimization path planning method considering space-time environment change, program, equipment and storage medium

Abstract

The invention belongs to the technical field of path planning, and particularly relates to an ocean robot energy optimization path planning method considering space-time environment change, a program, equipment and a storage medium. The invention aims to solve the problems of low path planning efficiency and poor cruising ability caused by dynamic change of a marine environment in a long-endurance task of a marine robot. According to the method, firstly, the energy evaluation function is constructed, then the improved Lazy Theta * algorithm is used for dynamically expanding and optimizing the marine environment map, it is ensured that the marine robot can maintain the relatively optimal energy utilization state in the complex environment, rapid and efficient path planning is achieved, and therefore the endurance performance and the working efficiency of the marine robot are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and specifically relates to a method, program, device and storage medium for energy optimization path planning of a marine robot taking into account changes in the temporal and spatial environment. Background Art

[0002] With the gradual maturity of ocean energy technology, marine robots have shown broad application prospects in long-duration missions such as resource exploration, environmental monitoring, and communication relay. However, due to the complexity and dynamics of the marine environment, especially the rapid changes in the spatiotemporal environment, traditional path planning methods are often difficult to adapt to these changes, resulting in low path planning efficiency and limited robot endurance, which seriously affects its mission execution ability. Therefore, in response to these problems encountered by marine robots in long-duration missions, it is urgent to develop a more efficient path planning method that adapts to dynamic environmental changes, so as to improve the endurance and energy efficiency of marine robots in complex environments.

[0003] At present, some path planning methods have achieved preliminary results in research. The paper "Energy optimal trajectory planning of an underwater robot using a genetic algorithm" uses BondGraph technology to model the marine robot, and iteratively optimizes the trajectory through a genetic algorithm, introducing complex factors in the underwater environment into the calculation model, ensuring the smoothness of the path while significantly reducing energy consumption. However, this method does not fully consider the temporal and spatial changes of the environment, and it is difficult to guarantee real-time performance in complex sea conditions.

[0004] The paper "Intelligent Path Planning of Underwater Robot Based on Reinforcement Learning" proposed an intelligent path planning method for underwater robots based on reinforcement learning, using the N-step Priority Double DQN (NPDDQN) algorithm for path planning, establishing a three-dimensional ocean environment model, and proposing an experience screening mechanism to improve the stability of the algorithm in a dynamic environment. However, this method has a relatively high computational complexity and a long learning process.

[0005] In addition, some other methods such as A*, Dijkstra and other classic path planning algorithms, although they can perform relatively accurate path planning in static environments, lack the ability to quickly adapt to environmental changes when dealing with complex ocean dynamic environments and cannot effectively update the path in real time. This results in their performance showing great limitations when it comes to scenarios involving the balance between ocean energy conversion and consumption. Summary of the invention

[0006] The purpose of the present invention is to provide a method, program, device and storage medium for energy optimization path planning of marine robots that considers changes in the spatiotemporal environment. The present invention fully combines the dynamic change characteristics of the marine environment with the real-time calculation of marine energy conversion and consumption, uses the improved Lazy Theta* algorithm to dynamically expand and optimize the marine environment map, and gradually constructs an energy evaluation function to ensure that in a complex environment, the marine robot can efficiently avoid obstacles and maintain the optimal energy utilization state, and finally achieve fast and safe path planning.

[0007] The energy optimization path planning method for marine robots considering the changes in the spatiotemporal environment includes the following steps:

[0008] Step 1: Obtain a priori nautical chart of the target sea area and divide the priori nautical chart into a grid map;

[0009] Step 2: The marine robot obtains its own position and state information at the current moment, and plans the path from the current position to the target point through the improved LazyTheta* algorithm;

[0010] Step 2.1: In the grid chart of the prior nautical chart, initialize the starting point and the target point, set the evaluation value of the starting point to 0; initialize the OPEN set and the CLOSED set to be empty sets, and add the starting point to the OPEN set;

[0011] Step 2.2: Determine whether the OPEN set is an empty set; if it is an empty set, determine that the search has failed and stop the calculation;

[0012] Step 2.3: Remove the point with the smallest corresponding evaluation value from the OPEN set and add it to the CLOSED set, and use it as the current node s i ; The evaluation value of the point is the energy consumption of the path from the starting point to the point;

[0013] Step 2.4: Determine the current node s i Is it the target point? If the current node s i If it is the target point, the calculation ends and the CLOSED set is output. Each node is connected in chronological order from the starting point to the target point to complete the path planning.

[0014] Step 2.5: Visit the current node s i All adjacent nodes of s are determined, and each adjacent node is determined whether it can be expanded, and whether each adjacent node is in the CLOSED set. For each adjacent node s that can be expanded and is not in the CLOSED set, i ' +1 , after executing steps 2.6 to 2.7, return to step 2.2;

[0015] Step 2.6: Determine the current node s i The parent node s i-1 Straight line to expandable adjacent nodes s i ' +1 Is the energy consumption of node s less than that of the current node s? i To expandable adjacent nodes s i ' +1 energy consumption;

[0016] If yes, then for node s i-1 With s i ' +1 Perform line of sight detection. If the detection passes, node s i-1 As nodes i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i ' +1 moment;

[0017] If it is no or the sight line detection fails, follow the node s i For node s i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i ' +1 moment;

[0018] Step 2.7: If node s i ' +1 If it is not in the OPEN set, then the node s i ' +1 Add to the OPEN set; if node s i ' +1 In the OPEN set, update node s according to the calculation result of step 2.6 i ' +1 The evaluation value and the distance from the starting point to the node s i ' +1 moment;

[0019] Step 3: After the marine robot moves for a preset time according to the path planning results, if it does not reach the target point, it returns to step 2.

[0020] Furthermore, the evaluation value of the point is the energy consumption of the path from the starting point to the point, specifically:

[0021]

[0022] Among them, j = 0, ..., k represents the distance from the starting point to point s i The path points between them, the 0th path point is the starting point, and the kth path point is point s i ;P Npr (j, j+1) represents the energy consumption power of the marine robot in the path from the jth path point to the j+1th path point, which is predicted based on the environmental information of the prior sea chart; disii(j, j+1) represents the path length from the jth path point to the j+1th path point; v e (j, j+1) represents the speed of the marine robot predicted based on the environmental information of the prior nautical chart in the path from the jth path point to the j+1th path point.

[0023] Furthermore, the energy consumption power of the marine robot is predicted based on the environmental information of the priori nautical chart, specifically:

[0024] P Npr (j,j+1)=P consume (v e (j,j+1))-P wind (j,j+1)-P sun (j,j+1)

[0025] Among them, P wind (j, j+1) represents the wind energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point; P sun (j, j+1) represents the solar energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point; P consume (v e (j,j+1)) represents the energy consumption power of the marine robot itself and the speed v e (j,j+1) is related.

[0026] Furthermore, the path length disii(j,j+1) from the jth path point to the j+1th path point is specifically:

[0027]

[0028] Among them, R e represents the radius of the earth; lat(·) represents the latitude of the point; lon(·) represents the longitude of the point.

[0029] Furthermore, in the path from the jth path point to the j+1th path point, the speed v of the marine robot predicted according to the environmental information of the prior nautical chart is e(j,j+1), specifically:

[0030]

[0031] Among them, v w (j,j+1), C(j,j+1), current1(j,j+1), current2(j,j+1) are the water speed, ground heading, latitudinal speed of the ocean current, and longitudinal speed of the ocean current of the ocean robot in the process of moving from the jth path point to the j+1th path point, respectively. They are all obtained from the prior nautical chart and are known items. e (j, j+1) and θ(j, j+1) are the ground speed and heading angle of the marine robot in the process of moving from the jth path point to the j+1th path point, respectively. They are unknown terms, and the unique solution is obtained by the above two equations.

[0032] Furthermore, the prediction of the wind energy conversion power of the marine robot based on the environmental information of the priori sea chart is specifically as follows:

[0033]

[0034] Among them, η wb , C p , R are the charging loss coefficient of the marine robot, the wind energy utilization coefficient and the radius of the wind turbine blade, respectively, and are all fixed values; ρ air (j, j+1) and U0(j, j+1) are the air density and air flow speed of the grid where the path from the jth path point to the j+1th path point is located, respectively, and are obtained from the prior nautical chart;

[0035] The solar energy conversion power of the marine robot is predicted based on the environmental information of the priori sea chart, specifically:

[0036] P sun (j,j+1)=G θ (j,j+1)·η sp ·A sp

[0037] Among them, A sp , η sp are the photovoltaic panel area of ​​the marine robot and the light energy conversion coefficient considering the battery charging loss, both of which are fixed values; G θ (j, j+1) is the solar radiation intensity of the grid where the path from the jth path point to the j+1th path point is located, which is obtained from the prior nautical chart.

[0038] Furthermore, the energy consumption power P of the marine robot itself consume (v e(j, j+1)) includes the power consumption of the intelligent control module, navigation and communication module, information fusion module, planning algorithm module, energy capture module, environment perception module and power execution module carried by the vehicle;

[0039] The intelligent control module is used to input task information and support data calculation of all functions of the marine robot;

[0040] The navigation and communication module is used to obtain the precise positioning of the marine robot according to the Beidou navigation system and is responsible for the real-time data transmission of other modules;

[0041] The information fusion module is used to obtain input information from the energy capture module, the environment perception module and the navigation and communication module, organize and form a priori nautical chart of the target sea area, divide the priori nautical chart into grid maps, and mark the environmental data of each grid;

[0042] The planning algorithm module is used to execute the improved LazyTheta* algorithm to plan the path of the marine robot from the current position to the target point;

[0043] The energy capture module is used to capture solar energy, wind energy and ocean energy, and convert them into electrical energy and power required for the normal operation of the marine robot;

[0044] The environmental perception module is used to obtain meteorological information of the sea area where the marine robot is located;

[0045] The power execution module is used to drive the movement and operation of the marine robot, ensure that the task is performed according to the planned path, and can adjust the power output in real time to adapt to changes in the environment.

[0046] A computer device / equipment / system includes a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned method for energy optimization path planning of a marine robot taking into account changes in the spatiotemporal environment.

[0047] A computer-readable storage medium stores a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for energy-optimized path planning of a marine robot taking into account changes in the spatiotemporal environment.

[0048] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for energy optimization path planning of a marine robot taking into account changes in the spatiotemporal environment.

[0049] The beneficial effects of the present invention are:

[0050] The present invention uses an improved Lazy Theta* algorithm to dynamically expand and optimize the marine environment map, gradually construct an energy evaluation function, and ensure that the marine robot can efficiently avoid obstacles and maintain the optimal energy utilization state in complex environments, and finally achieve fast and safe path planning. The present invention enables the marine robot to effectively adapt to the spatiotemporal changes in different marine areas, especially to cope with sudden changes in sea conditions during mission execution, and improve its navigation safety and mission completion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the path that line of sight detection passes through.

[0052] Figure 2 Schematic diagram of the path that fails the line of sight detection.

[0053] Figure 3 This is a flowchart of planning a path from the current position to the target point by using the improved Lazy Theta* algorithm in the present invention.

[0054] Figure 4 It is a schematic diagram of the system structure of the marine robot in the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described below in conjunction with the accompanying drawings.

[0056] The purpose of the present invention is to solve the problems of low efficiency and poor endurance of path planning of marine robots in long-duration missions due to the dynamic changes of the marine environment. Traditional path planning methods cannot fully consider the dynamic changes of the environment in time and space and the capture requirements of various natural energy sources (such as sea breeze, waves, solar energy, ocean currents, etc.). In order to meet the planning needs of marine robots in long-duration missions, the present invention proposes a marine robot energy optimization path planning method that considers changes in the time and space environment. This method fully combines the dynamic change characteristics of the marine environment with the real-time calculation of marine energy conversion and consumption, and uses the improved Lazy Theta* algorithm to dynamically expand and optimize the marine environment map, and gradually construct an energy evaluation function to ensure that in complex environments, the marine robot can efficiently avoid obstacles and maintain the optimal energy utilization state, and finally achieve fast and safe path planning.

[0057] like Figure 4 As shown, the marine robot is equipped with an intelligent control module, an energy conversion module, a navigation and communication module, an information fusion module, a planning algorithm module, an environmental perception module and a power execution module.

[0058] The intelligent control module is responsible for supporting data operations for all functions of the marine robot; the energy conversion module is responsible for capturing ocean energy and converting it into electrical energy and power required for the normal operation of the marine robot; the navigation and communication module is responsible for achieving precise navigation and positioning of the marine robot and real-time data transmission with other modules, ensuring the acquisition of location information and environmental updates, and supporting the effectiveness and accuracy of path planning; the information fusion module is responsible for organizing and analyzing input information and normalizing it; the planning algorithm module is responsible for designing the optimal energy evaluation function based on the principle of Lazy Theta* algorithm, starting from the initial grid, continuously calculating the evaluation function value in the expansion direction, adding the grid with the minimum evaluation value to the path until it reaches the target grid and completes the planning task; the power execution module is responsible for driving the movement and operation of the robot, ensuring that the task is executed according to the planned path, and can adjust the power output in real time to adapt to environmental changes; the environmental perception module is responsible for obtaining real-time meteorological information in the sea area where the marine robot is located, and providing necessary data support, so that the path planning algorithm can help the marine robot dynamically adapt to environmental changes.

[0059] Furthermore, the environmental perception module of the marine robot mainly includes a weather station, a current meter, a wave sensor, and a light radiation sensor; the weather station can monitor and collect sea breeze information in real time, including wind speed and wind direction information; the current meter can monitor and collect sea wave information in real time, including flow speed and flow direction information; the wave sensor can monitor and collect ocean current information in real time, including significant wave height, average wave period, and wave direction; the radiation sensor can monitor and collect solar radiation intensity information in real time. The energy conversion module of the marine robot mainly includes a wind turbine, a hydrofoil, and a photovoltaic panel; the wind turbine can convert wind energy into the electrical energy required for the operation of the marine robot; the photovoltaic panel can convert solar energy into the electrical energy required for the operation of the marine robot; and the hydrofoil can convert wave energy into thrust for the marine robot to move.

[0060] First, obtain the prior nautical chart of the target sea area and divide the prior nautical chart into grid maps; Figure 1 and Figure 2 As shown, black represents land and white represents navigable areas. When the black coverage area of ​​the grid is less than 10%, the value is assigned to 0, indicating that the area is navigable. Figure 1 If the value is 1, it means that the area is not navigable. Figure 2 As shown, you need to navigate on the edge of the black grid;

[0061] The marine robot obtains its own position and status information at the current moment, and plans the path from the current position to the target point through the improved Lazy Theta* algorithm, such as Figure 3 As shown, the specific steps include:

[0062] Step 1: In the grid map of the prior chart, initialize the starting point and the target point, set the evaluation value of the starting point to 0; initialize the OPEN set and the CLOSED set to be empty sets, and add the starting point to the OPEN set;

[0063] Step 2: Determine whether the OPEN set is an empty set; if it is an empty set, determine that the search has failed and stop the calculation;

[0064] Step 3: Remove the point with the smallest corresponding evaluation value from the OPEN set and add it to the CLOSED set, and use it as the current node s i ;

[0065] Nodes i The evaluation value G(s i ) is the energy consumption of the path from the starting point to this point, and the calculation method is:

[0066]

[0067] Among them, j = 0, ..., k represents the distance from the starting point to point s i The path points between them, the 0th path point is the starting point, and the kth path point is point s i ;

[0068] P Npr (j, j+1) represents the energy consumption power of the marine robot in the path from the jth path point to the j+1th path point, which is predicted based on the environmental information of the prior sea chart. The specific calculation method is:

[0069] P Npr (j,j+1)=P consume (v e (j,j+1))-P wind (j,j+1)-P sun (j,j+1)

[0070] Among them, P consume (v e (j,j+1)) represents the energy consumption power of the marine robot itself and the speed v e (j,j+1) is related; P wind (j, j+1) represents the wind energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point; P sun (j, j+1) represents the solar energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point;

[0071]

[0072] Among them, η wb , C p , R are the charging loss coefficient of the marine robot, the wind energy utilization coefficient and the radius of the wind turbine blade, respectively, and are all fixed values; ρ air (j, j+1) and U0(j, j+1) are the air density and air flow speed of the grid where the path from the jth path point to the j+1th path point is located, respectively, and are obtained from the prior nautical chart;

[0073] P sun (j,j+1)=G θ (j,j+1)·η sp ·A sp

[0074] Among them, A sp , η sp are the photovoltaic panel area of ​​the marine robot and the light energy conversion coefficient considering the battery charging loss, both of which are fixed values; G θ (j, j+1) is the solar radiation intensity of the grid where the path from the jth path point to the j+1th path point is located, which is obtained from the prior nautical chart;

[0075] disii(j,j+1) represents the path length from the jth path point to the j+1th path point, and is calculated as follows;

[0076]

[0077] Among them, R e represents the radius of the earth; lat(·) represents the latitude of the point; lon(·) represents the longitude of the point;

[0078] v e (j, j+1) represents the speed of the marine robot in the path from the jth path point to the j+1th path point, which is predicted based on the environmental information of the prior nautical chart. The calculation method is:

[0079]

[0080] Among them, v w (j,j+1), C(j,j+1), current1(j,j+1), current2(j,j+1) are the water speed, ground heading, latitudinal speed of the ocean current, and longitudinal speed of the ocean current of the ocean robot in the process of moving from the jth path point to the j+1th path point, respectively. They are all obtained from the prior nautical chart and are known items. e (j, j+1) and θ(j, j+1) are the ground speed and heading angle of the marine robot when moving from the jth path point to the j+1th path point, respectively. They are unknown terms, and the unique solution is obtained through the above two equations;

[0081] Step 4: Determine the current node s i Is it the target point? If the current node s i If it is the target point, the calculation ends and the CLOSED set is output. Each node is connected in chronological order from the starting point to the target point to complete the path planning.

[0082] Step 5: Visit the current node s i All adjacent nodes (up, down, left, right, upper left, lower left, upper right, lower right 8 directions), determine whether each adjacent node can be expanded, and at the same time determine whether each adjacent node is in the CLOSED set; there are two specific situations where a node direction cannot be expanded. One is from node s i To Nodes i ' +1 The speed v of the ocean robot over the ground obtained by the above formula is e (s i ,s i ' +1 ) has no solution, that is, due to the limited weather conditions and the high speed of the current, the heading of the marine robot cannot be adjusted to the corresponding requirements; the other is to obtain the ground speed v of the marine robot e (s i ,s i ' +1 ) is zero, which means that there are obstacles in the search direction and the path of the marine robot is unreachable;

[0083] For each adjacent node s that is expandable and not in the CLOSED set i ' +1 , perform the following judgment:

[0084] (1) Determine the current node s i The parent node s i-1 Straight line to expandable adjacent nodes s i ' +1 Is the energy consumption of node s less than that of the current node s? i To expandable adjacent nodes s i ' +1 energy consumption;

[0085] If yes, then for node s i-1 With s i ' +1 Perform line of sight detection. If the detection passes, node s i-1 As nodes i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i '+1 moment;

[0086] If it is no or the sight line detection fails, follow the node s i For node s i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i ' +1 moment;

[0087] (2) If node s i ' +1 If it is not in the OPEN set, then the node s i ' +1 Add to the OPEN set; if node s i ' +1 In the OPEN set, the node s is updated according to the calculation result of (1). i ' +1 The evaluation value and the distance from the starting point to the node s i ' +1 moment;

[0088] Current nodes i All adjacent nodes s that are expandable and not in the CLOSED set i ' +1 After executing (1) and (2), return to step 2.

[0089] The present invention enables the marine robot to effectively adapt to the temporal and spatial changes in different ocean areas, especially to cope with sudden changes in sea conditions during mission execution, thereby improving its navigation safety and mission completion efficiency.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy-optimized path planning method for marine robots considering spatiotemporal environmental changes, characterized in that: The following steps are involved: Step 1: Obtain a priori nautical chart of the target sea area and divide the priori nautical chart into a grid map; Step 2: The marine robot obtains its own position and state information at the current moment, and plans the path from the current position to the target point through the improved LazyTheta* algorithm; Step 2.1: In the grid chart of the prior nautical chart, initialize the starting point and the target point, set the evaluation value of the starting point to 0; initialize the OPEN set and the CLOSED set to be empty sets, and add the starting point to the OPEN set; Step 2.2: Determine whether the OPEN set is an empty set; if it is an empty set, determine that the search has failed and stop the calculation; Step 2.3: Remove the point with the smallest corresponding evaluation value from the OPEN set and add it to the CLOSED set, and use it as the current node s i ; The evaluation value of the point is the energy consumption of the path from the starting point to the point; Step 2.4: Determine the current node s i Is it the target point? If the current node s i If it is the target point, the calculation ends and the CLOSED set is output. Each node is connected in chronological order from the starting point to the target point to complete the path planning. Step 2.5: Visit the current node s i All adjacent nodes of s are determined, and each adjacent node is determined whether it can be expanded, and whether each adjacent node is in the CLOSED set. For each adjacent node s that can be expanded and is not in the CLOSED set, i ' +1 , after executing steps 2.6 to 2.7, return to step 2.2; Step 2.6: Determine the current node s i The parent node s i-1 Straight line to expandable adjacent nodes s i ' +1 Is the energy consumption of node s less than that of the current node s? i To expandable adjacent nodes s i ' +1 energy consumption; If yes, then for node s i-1 With s i ' +1 Perform line of sight detection. If the detection passes, node s i-1 As nodes i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i ' +1 moment; If it is no or the sight line detection fails, follow the node s i For node s i ' +1 The parent node of node s is used to calculate i ' +1 The evaluation value of , and calculate the distance from the starting point to the node s i ' +1 moment; Step 2.7: If node s i ' +1 If it is not in the OPEN set, then the node s i ' +1 Add to the OPEN set; if node s i ' +1 In the OPEN set, update node s according to the calculation result of step 2.6 i ' +1 The evaluation value and the distance from the starting point to the node s i ' +1 moment; Step 3: After the marine robot moves for a preset time according to the path planning results, if it does not reach the target point, it returns to step 2.

2. The method for energy-optimized path planning of a marine robot considering spatiotemporal environmental changes according to claim 1 is characterized in that: The evaluation value of the point is the energy consumption of the path from the starting point to the point, specifically: Among them, j = 0, ..., k represents the distance from the starting point to point s i The path points between them, the 0th path point is the starting point, and the kth path point is point s i ;P Npr (j, j+1) represents the energy consumption power of the marine robot in the path from the jth path point to the j+1th path point, which is predicted based on the environmental information of the prior sea chart; disii(j, j+1) represents the path length from the jth path point to the j+1th path point; v e (j, j+1) represents the speed of the marine robot predicted based on the environmental information of the prior nautical chart in the path from the jth path point to the j+1th path point.

3. The method for energy-optimized path planning of a marine robot considering spatiotemporal environmental changes according to claim 2 is characterized in that: The energy consumption power of the marine robot is predicted based on the environmental information of the priori nautical chart, specifically: P Npr (j,j+1)=P consume (v e (j,j+1))-P wind (j,j+1)-P sun (j,j+1) Among them, P wind (j, j+1) represents the wind energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point; P sun (j, j+1) represents the solar energy conversion power of the marine robot predicted based on the environmental information of the prior sea chart in the path from the jth path point to the j+1th path point; P consume (v e (j,j+1)) represents the energy consumption power of the marine robot itself and the speed v e (j,j+1) is related.

4. The method for energy-optimized path planning of a marine robot considering spatiotemporal environmental changes according to claim 2, characterized in that: The path length disii(j,j+1) from the jth path point to the j+1th path point is specifically: Among them, R e represents the radius of the earth; lat(·) represents the latitude of the point; lon(·) represents the longitude of the point.

5. The method for energy-optimized path planning of a marine robot considering changes in the spatiotemporal environment according to claim 2, characterized in that: In the path from the jth path point to the j+1th path point, the speed v of the marine robot predicted according to the environmental information of the prior nautical chart is e (j,j+1), specifically: Among them, v w (j,j+1), C(j,j+1), current1(j,j+1), current2(j,j+1) are the water speed, ground heading, latitudinal speed of the ocean current, and longitudinal speed of the ocean current of the ocean robot in the process of moving from the jth path point to the j+1th path point, respectively. They are all obtained from the prior nautical chart and are known items. e (j, j+1) and θ(j, j+1) are the ground speed and heading angle of the marine robot in the process of moving from the jth path point to the j+1th path point, respectively. They are unknown terms, and the unique solution is obtained by the above two equations.

6. The method for energy-optimized path planning of a marine robot considering changes in the spatiotemporal environment according to claim 3 is characterized in that: The prediction of the wind energy conversion power of the marine robot based on the environmental information of the priori sea chart is specifically as follows: Among them, η wb , C p , R are the charging loss coefficient of the marine robot, the wind energy utilization coefficient and the radius of the wind turbine blade, respectively, and are all fixed values; ρ air (j, j+1) and U0(j, j+1) are the air density and air flow speed of the grid where the path from the jth path point to the j+1th path point is located, respectively, both of which are obtained from the prior nautical chart; The solar energy conversion power of the marine robot is predicted based on the environmental information of the priori sea chart, specifically: P sun (j,j+1)=G θ (j,j+1)·η sp ·YOUR sp Among them, A sp , η sp are the photovoltaic panel area of ​​the marine robot and the light energy conversion coefficient considering the battery charging loss, both of which are fixed values; G θ (j, j+1) is the solar radiation intensity of the grid where the path from the jth path point to the j+1th path point is located, which is obtained from the prior nautical chart.

7. The method for energy-optimized path planning of a marine robot considering spatiotemporal environmental changes according to claim 3, characterized in that: The energy consumption power P of the marine robot itself consume (v e (j, j+1)) includes the power consumption of the intelligent control module, navigation and communication module, information fusion module, planning algorithm module, energy capture module, environment perception module and power execution module carried by the vehicle; The intelligent control module is used to input task information and support data calculation of all functions of the marine robot; The navigation and communication module is used to obtain the precise positioning of the marine robot according to the Beidou navigation system and is responsible for the real-time data transmission of other modules; The information fusion module is used to obtain input information from the energy capture module, the environment perception module and the navigation and communication module, organize and form a priori nautical chart of the target sea area, divide the priori nautical chart into grid maps, and mark the environmental data of each grid; The planning algorithm module is used to execute the improved LazyTheta* algorithm to plan the path of the marine robot from the current position to the target point; The energy capture module is used to capture solar energy, wind energy and ocean energy, and convert them into electrical energy and power required for the normal operation of the marine robot; The environmental perception module is used to obtain meteorological information of the sea area where the marine robot is located; The power execution module is used to drive the movement and operation of the marine robot, ensure that the task is performed according to the planned path, and can adjust the power output in real time to adapt to changes in the environment.

8. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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