Marine unmanned mobile platform route planning method based on multi-element environment field

Abstract

A marine unmanned mobile platform route planning method based on a multi-element environment field relates to the technical field of marine unmanned platform route planning, and comprises the following steps: calculating evaluation functions of adjacent nodes from a route starting point of an unmanned platform according to input marine environment field information data such as a wind field, a flow field and an illumination radiation flux field; and selecting the node with the optimal evaluation function value as the next node, recording the search result of each time at the same time, repeating the steps until the target point is searched, and planning the optimal path. According to the invention, through planning the sea unmanned platform route, the unmanned platform can make full use of favorable environments such as sea wind, wave, flow and illumination, and ultra-long endurance of the unmanned platform is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of route planning for unmanned platforms in the sea, and in particular to a route planning method for an unmanned marine mobile platform based on a multi-factor environmental field. Background Art

[0002] Marine unmanned platforms have strong mobility and superior autonomous navigation capabilities. In recent years, they have been widely used in maritime operations and marine management activities. The route planning of marine unmanned platforms is a range-scale optimization planning process based on prior environmental factors. High-quality route planning services can ensure the safety and economic benefits of ocean-going unmanned platforms. Existing unmanned platform route planning technologies mainly include Dijkstra method, artificial potential field method, dynamic window method, genetic algorithm, and various swarm intelligence algorithms. The above-mentioned algorithms have achieved good results, but they are all based on a relatively static environment. However, in the actual marine environment, the situation changes rapidly, and different environmental conditions will have various effects on the movement of unmanned platforms. Therefore, the research on route planning of unmanned platforms in the marine environment must fully consider the influence of environmental factors such as wind, waves and currents. Based on marine environmental field navigation, unmanned platforms can effectively utilize all marine conditions that are beneficial to navigation in the navigation area, such as monsoons and ocean currents, to obtain the safest and most economical routes for them. However, there are currently few studies on route planning algorithms that can make optimization decisions based on the marine environment. Summary of the invention

[0003] The present invention proposes a route planning method for an unmanned marine mobile platform based on a multi-factor environmental field, with the aim of solving the following problems in the prior art: existing unmanned platform route planning technologies are all based on a relatively static environment, however, in the actual marine environment, the situation changes rapidly, and different environmental conditions will have various effects on the movement of the unmanned platform. Currently, there are few studies on route planning algorithms that can make optimization decisions based on the marine environment.

[0004] To achieve the above object, the technical solution of the present invention is:

[0005] A method for marine unmanned mobile platform route planning based on a multi-factor environmental field comprises the following steps:

[0006] Step 1: Determine the starting point, end point coordinates and platform parameters;

[0007] Step 2: Load the marine environment field information data, including wind field distribution, wind speed and wind direction; flow field distribution, flow speed and flow direction; wave height; light radiation flux field distribution;

[0008] Step 3: Determine the evaluation function value of each node around the starting point, determine the optimal node by comparing the evaluation function results, and record it in the path node set;

[0009] Step 4: Determine whether the current path node is the end point. If the end point is reached, the method ends; if the end point is not reached, the current path node is used as the starting point, and steps 3 and 4 are repeated until the end point is reached;

[0010] Step 5: Summarize all path nodes and output the final path result.

[0011] Preferably, in the step three, the evaluation function value of each node around the starting point is calculated based on three aspects of the maneuverability, energy consumption and safety of the platform navigation.

[0012] Preferably, in step 3, the evaluation function calculation formula is as shown in formula (1), where E total is the comprehensive evaluation function, E mobility is the mobility evaluation function, E energy is the energy evaluation function, E safety is the safety assessment function, ω1, ω2, ω3 are weight coefficients:

[0013] E total =ω1·E mobility +ω2·E energy +ω3·E safety (1)

[0014] The constraints of the evaluation function are 4 points: the value of each sub-evaluation function cannot be negative; the path point search cannot exceed the range of the chart; the steering angle and frequency during the journey cannot be greater than the set upper limit; no obstacles can appear within the safety range. The mathematical expression is as follows:

[0015] E mobility ≥0&E energy ≥0&E safety ≥0 (2)

[0016] path∈Map (3)

[0017]

[0018] In formulas (2)-(5), path represents the final planned path, (x i ,y i ) and (x i+1 ,y i+1 ) are the two sub-path points that need to be turned, ε max is the maximum steering angle, T max is the maximum turning interval time;

[0019] In the process of unmanned platform path planning, if the result of any unilateral evaluation function in mobility, energy consumption and safety is non-positive, the route is not feasible; when all unilateral evaluation functions are positive, then by comparing the comprehensive evaluation function E total The size of the function E is used to determine the optimal path and the comprehensive evaluation function E total The larger the value, the higher the path selectivity.

[0020] Preferably, in step 3, the evaluation function for mobility is calculated as follows:

[0021] The mobility of unmanned platforms is affected by three factors: wind, waves and currents. The calculation formula for the mobility evaluation function based on the three factors of wind, waves and currents is as follows:

[0022] E mobility =F wind +F wave +F current (6)

[0023] The impact of environmental meteorological conditions on mobility is reflected in its effect on the speed of the unmanned platform. Among them, the evaluation function F of the impact of sea breeze on the mobility of the unmanned platform is wind The calculation method is:

[0024]

[0025] Δv=v wind ·cos(θ-α)-v (9)

[0026]

[0027] The degree of interference of waves on the maneuverability of the unmanned platform depends on the wave height h. The wave height is calculated by the wind speed. The evaluation function F of the impact of waves on the maneuverability of the unmanned platform is wave The calculation method is as shown in formula (12):

[0028] h=(-0.12·v wind +1.22)+0.082·v wind 1.55 (11)

[0029] F wave =a1·h·cos(θ-α) (12)

[0030] The effect of ocean current on the mobility of unmanned platforms is similar to the effect of sea breeze, and its evaluation function F current The calculation formula is as follows:

[0031]

[0032] In the above formula (7), f represents the air resistance of the unmanned platform, Caa Where is the air resistance coefficient; ρ is the air density. Under standard atmospheric pressure, the air density is affected by the temperature T. Its calculation formula is shown in (8); S wind is the windward area of ​​the cross-section of the unmanned platform; Δv represents the relative speed between the unmanned platform and the sea breeze, α represents the heading of the unmanned platform, θ represents the wind direction of the sea breeze, and v represents the speed of the unmanned platform itself; in formula (12), a1 represents the influence factor of the wave height on the speed of the unmanned platform, and formula (11) is the calculation formula of the wave height; in formula (13), a2 represents the influence factor of the flow velocity on the speed of the unmanned platform, Represents the direction of ocean current.

[0033] Preferably, in step 3, the evaluation function for energy consumption is calculated as follows:

[0034] Assume that the inherent storage energy of the unmanned platform is e total , assuming that the unmanned platform is moving at a constant power, if the power consumption per unit time during its forward movement is G a , wind power generation generates energy of G wind , the photovoltaic panel receives sunlight to generate energy G light , then the energy consumption evaluation function E during navigation is energy The calculation formulas for each part of energy are as follows:

[0035] E energy =e total -G a +G wind +G light (14)

[0036] G a = b·t (15)

[0037]

[0038] G light =Q s ·S light ·t·η2 (17)

[0039] In the above formulas (15) and (16), b is the energy consumption per unit time of the unmanned platform, t is the total navigation time, R is the fan blade radius of the wind turbine, and the fan energy conversion efficiency is η1; in formula (17), Q s is the light radiation flux, S is the effective light receiving area of ​​the photovoltaic panel, and the photovoltaic panel power generation conversion efficiency is η2.

[0040] Preferably, in step 3, the evaluation function for security is calculated as follows:

[0041] If the maximum wave height that the unmanned platform can withstand is H max, then the safety assessment function is calculated by the following formula:

[0042] E safety =H max -h (18).

[0043] The beneficial effects of the marine unmanned mobile platform route planning method based on a multi-factor environment field of the present invention are:

[0044] 1. The present invention plans the route of the unmanned ocean platform, so that the unmanned platform can make full use of favorable environments such as ocean wind, waves, currents and light to achieve ultra-long endurance of the unmanned platform.

[0045] 2. The route planning of the present invention can update the planned route in real time according to the environmental forecast results to ensure navigation safety.

[0046] 3. The present invention can realize three different task modes: capacity priority, balance priority, and speed priority, and plan paths that meet actual needs according to different task modes.

[0047] Instruction Manual

[0048] Figure 1 It is the relationship curve between the unmanned platform capacity and each variable;

[0049] Figure 2 Flow chart for implementing the method of the present invention;

[0050] Figure 3 For meteorological data and planning route map;

[0051] Figure 4 The path results obtained by planning the method under different task modes. DETAILED DESCRIPTION

[0052] The following description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0053] The following embodiments may be understood as individually expressing a part of a local structure or method of the present invention, or may be understood as a combination of the embodiments to explain the connotation of a larger structure or method of the present invention.

[0054] Example 1

[0055] A route planning method for an unmanned marine mobile platform based on a multi-factor environmental field, such as Figure 2 As shown, the following steps are included:

[0056] Step 1: Determine the starting point, end point coordinates and platform parameters;

[0057] Step 2: Load the marine environment field information data, including wind field distribution, wind speed and wind direction; flow field distribution, flow speed and flow direction; wave height; light radiation flux field distribution;

[0058] Step 3: Determine the evaluation function value of each node around the starting point, determine the optimal node by comparing the evaluation function results, and record it in the path node set;

[0059] Step 4: Determine whether the current path node is the end point. If the end point is reached, the method ends; if the end point is not reached, the current path node is used as the starting point, and steps 3 and 4 are repeated until the end point is reached;

[0060] Step 5: Summarize all path nodes and output the final path result.

[0061] Through this embodiment, it can be understood that the principle of the present invention is to start from the starting point of the route of the unmanned platform, calculate the evaluation functions of the adjacent nodes respectively, select the node with the best evaluation function value as the next node, and record the search results each time, repeat the cycle until the target point is searched and the optimal path is planned.

[0062] When planning a specific route for an unmanned platform, the values ​​of the influencing factors in the calculation process of the above evaluation function are related to the parameters of the platform itself. For details, see Table 1 below.

[0063] Table 1. Parameter values ​​of different unmanned platforms

[0064]

[0065] Note: ① USV is a wind-solar hybrid driven unmanned vessel, WG is a wave energy glider, and UG is an underwater glider;

[0066] ②The data in the table are the measured data of the wind-solar hybrid drive unmanned platform independently developed by the unit;

[0067] ③ The ratio between weight coefficients can be adjusted according to different task modes.

[0068] Example 2

[0069] like Figure 2 As shown, in the step three, the evaluation function value of each node around the starting point is calculated based on three aspects of the platform's navigation maneuverability, energy consumption and safety.

[0070] Example 3

[0071] In step 3, the evaluation function calculation formula is shown in the following formula (1), where E total is the comprehensive evaluation function, E mobility is the mobility evaluation function, Eenergy is the energy evaluation function, E safety is the safety assessment function, ω1, ω2, ω3 are weight coefficients:

[0072] E total =ω1·E mobility +ω2·E energy +ω3·E safety (1)

[0073] The constraints of the evaluation function are 4 points: the value of each sub-evaluation function cannot be negative; the path point search cannot exceed the range of the chart; the steering angle and frequency during the journey cannot be greater than the set upper limit; no obstacles can appear within the safety range. The mathematical expression is as follows:

[0074] E mobility ≥0&E energy ≥0&E safety ≥0 (2)

[0075] path∈Map (3)

[0076]

[0077] In formulas (2)-(5), path represents the final planned path, (x i ,y i ) and (x i+1 ,y i+1 ) are the two sub-path points that need to be turned, ε max is the maximum steering angle, T max is the maximum turning interval time;

[0078] In the process of unmanned platform path planning, if the result of any unilateral evaluation function in mobility, energy consumption and safety is non-positive, the route is not feasible; when all unilateral evaluation functions are positive, then by comparing the comprehensive evaluation function E total The size of the function E is used to determine the optimal path and the comprehensive evaluation function E total The larger the value, the higher the path selectivity.

[0079] The symbols of unmanned platform and marine environment parameters are listed in Table 2 below:

[0080] Table 2 Parameters and symbols of various variables

[0081]

[0082] Example 4

[0083] In step 3, the evaluation function for mobility is calculated as follows:

[0084] The mobility of unmanned platforms is affected by three factors: wind, waves and currents. The calculation formula for the mobility evaluation function based on the three factors of wind, waves and currents is as follows:

[0085] E mobility =F wind +F wave +F current (6)

[0086] The impact of environmental meteorological conditions on mobility is reflected in its effect on the speed of the unmanned platform. Among them, the evaluation function F of the impact of sea breeze on the mobility of the unmanned platform is wind The calculation method is:

[0087]

[0088] Δv=v wind ·cos(θ-α)-v (9)

[0089]

[0090] The degree of interference of waves on the maneuverability of the unmanned platform depends on the wave height h. The wave height is calculated by the wind speed. The evaluation function F of the impact of waves on the maneuverability of the unmanned platform is wave The calculation method is as shown in formula (12):

[0091] h=(-0.12·v wind +1.22)+0.082·v wind 1.55 (11)

[0092] F wave =a1·h·cos(θ-α) (12)

[0093] The effect of ocean current on the mobility of unmanned platforms is similar to the effect of sea breeze, and its evaluation function F current The calculation formula is as follows:

[0094]

[0095] In the above formula (7), f represents the air resistance of the unmanned platform, C aa Where is the air resistance coefficient; ρ is the air density. Under standard atmospheric pressure, the air density is affected by the temperature T. Its calculation formula is shown in (8); S windis the windward area of ​​the cross-section of the unmanned platform; Δv represents the relative speed between the unmanned platform and the sea breeze, α represents the heading of the unmanned platform, θ represents the wind direction of the sea breeze, and v represents the speed of the unmanned platform itself; in formula (12), a1 represents the influence factor of the wave height on the speed of the unmanned platform. Formula (11) is the calculation formula of the wave height, which is an empirical prediction formula derived and calculated by statistical methods and scientific experiments based on a large number of ship navigation; a2 represents the influence factor of the flow velocity on the speed of the unmanned platform, Represents the direction of ocean current movement.

[0096] Example 5

[0097] In step 3, the evaluation function for energy consumption is calculated as follows:

[0098] Assume that the inherent storage energy of the unmanned platform is e total , assuming that the unmanned platform is moving at a constant power, if the power consumption per unit time during its forward movement is G a , wind power generation generates energy of G wind , the photovoltaic panel receives sunlight to generate energy G light , then the energy consumption evaluation function E during navigation is energy The calculation formulas for each part of energy are as follows:

[0099] E energy =e total -G a +G wind +G light (14)

[0100] G a = b·t (15)

[0101]

[0102] G light =Q s ·S light ·t·η2 (17)

[0103] In the above formulas (15) and (16), b is the energy consumption per unit time of the unmanned platform, t is the total navigation time, R is the fan blade radius of the wind turbine, and the fan energy conversion efficiency is η1; in formula (17), Q s is the light radiation flux, S is the effective light receiving area of ​​the photovoltaic panel, and the photovoltaic panel power conversion efficiency is η2. For the unmanned platform, the wind turbine capacity G wind With wind speed v wind Relationship Figure 1 As shown in the middle left figure, the photovoltaic panel production capacity G light and the light radiation flux Q s The relationship is as Figure 1As shown in the middle right picture.

[0104] Example 6

[0105] In step 3, the evaluation function for security is calculated as follows:

[0106] During the navigation of the unmanned platform, waves are the most threatening environmental condition to its safety. If the maximum wave height that the unmanned platform can withstand is H max , then the safety evaluation function can be calculated by the following formula:

[0107] E safety =H max -h (18).

[0108] Example 7

[0109] The following takes the wind-solar hybrid drive unmanned boat as an example, uses the measured marine environmental field data in a certain sea area in the South my country Sea in June 2022, and takes the unmanned boat platform as an example to verify the proposed method. According to the platform's own parameters and environmental field data, the evaluation function is calculated according to the given formula in the method, and a reasonable and feasible path can be determined. Figure 3 The first three figures are the distribution of light radiation flux, flow field distribution (flow velocity and direction), and wind field distribution (wind velocity and direction). In this environment, 109.5269°E, 11.0163°N is taken as the starting point of the unmanned boat, and 120.3381°E, 18.7051°N is taken as the end point to verify the feasibility of the method. The final planned path is as follows Figure 4 shown.

[0110] It can be seen from the above results that the path planned by the present invention can be well combined with environmental information and make full use of known meteorological information. In order to ensure the endurance of the unmanned ship, the selected route has good light intensity and appropriate wind intensity, which is conducive to the power generation of ship-borne photovoltaic panels and wind turbines. The navigation route is chosen to go in the direction of the ocean current as much as possible to reduce the distance of upstream travel, thereby shortening the navigation time.

[0111] At the same time, by adjusting the weights of the factors affecting different evaluation functions, the optimal paths under different modes can be planned. The output results of different modes are as follows: Figure 4 shown.

[0112] Table 3 Experimental data records in different modes

[0113]

[0114]

[0115] It can be seen from the experimental results shown in Table 3 that the present invention can effectively plan a safe and feasible path for the unmanned platform, and can realize different task modes by changing the weight ratio. Analysis of the above experimental data shows that in the capacity priority mode, in order to meet the light intensity and wind speed as much as possible, the selected path length and navigation time are relatively long, but the final capacity effect is considerable. In the speed priority mode, the maximum navigation speed and the shortest navigation time can be obtained.

Claims

1. A method for route planning of an unmanned marine mobile platform based on a multi-factor environment field, characterized by comprising the following steps: Step 1: Determine the starting point, end point coordinates and platform parameters; Step 2: Load the marine environment field information data, including wind field distribution, wind speed and wind direction; flow field distribution, flow speed and flow direction; wave height; light radiation flux field distribution; Step 3: Determine the evaluation function value of each node around the starting point, determine the optimal node by comparing the evaluation function results, and record it in the path node set; Step 4: Determine whether the current path node is the end point. If the end point is reached, the method ends; if the end point is not reached, the current path node is used as the starting point, and steps 3 and 4 are repeated until the end point is reached; Step 5: Summarize all path nodes and output the final path result.

2. A method for marine unmanned mobile platform route planning based on a multi-factor environment field as claimed in claim 1, Its characteristics are: In the step three, the evaluation function values ​​of the nodes around the starting point are calculated based on the three aspects of the platform's navigation maneuverability, energy consumption and safety.

3. A method for marine unmanned mobile platform route planning based on a multi-factor environment field as claimed in claim 2, Its characteristics are: In step 3, the evaluation function calculation formula is shown in the following formula (1), where E total is the comprehensive evaluation function, E mobility is the mobility evaluation function, E energy is the energy evaluation function, E safety is the security assessment function, ω 1 ,ω 2 ,ω 3 is the weight coefficient: E total =ω 1 ·E mobility +oh 2 ·E energy +oh 3 ·E safety (1) The evaluation function has four constraints: the value of each sub-evaluation function cannot be negative; the path point search cannot exceed the range of the chart; the steering angle and frequency during the journey cannot be greater than the set upper limit; There should be no obstacles within the safety range, and the mathematical expression is as follows: AND mobility ≥0 & E energy ≥0 & E safety ≥0 (2) path∈Map (3) In formulas (2)-(5), path represents the final planned path, (x i ,y i ) and (x i+1 ,y i+1 ) are the two sub-path points that need to be turned, ε max is the maximum steering angle, T max is the maximum turning interval time; In the process of unmanned platform path planning, if the result of any unilateral evaluation function in mobility, energy consumption and safety is non-positive, the route is not feasible; when all unilateral evaluation functions are positive, then by comparing the comprehensive evaluation function E total The size of the function E is used to determine the optimal path and the comprehensive evaluation function E total The larger the value, the higher the path selectivity.

4. A method for marine unmanned mobile platform route planning based on a multi-factor environment field as claimed in claim 3, Its characteristics are: In step 3, the evaluation function for mobility is calculated as follows: The mobility of unmanned platforms is affected by three factors: wind, waves and currents. The calculation formula for the mobility evaluation function based on the three factors of wind, waves and currents is as follows: E mobility =F wind +F wave +F current (6) The impact of environmental meteorological conditions on mobility is reflected in its effect on the speed of the unmanned platform. Among them, the evaluation function F of the impact of sea breeze on the mobility of the unmanned platform is wind The calculation method is: Δv=v wind ·cos(θ-α)-v (9) The degree of interference of waves on the maneuverability of the unmanned platform depends on the wave height h. The wave height is calculated by the wind speed. The evaluation function F of the impact of waves on the maneuverability of the unmanned platform is wave The calculation method is as shown in formula (12): h=(-0.12·v wind +1.22)+0.082·v wind 1.55 (11) F wave =a 1 ·h·cos(θ-α) (12) The effect of ocean current on the mobility of unmanned platforms is similar to the effect of sea breeze, and its evaluation function F current The calculation formula is as follows: In the above formula (7), f represents the air resistance of the unmanned platform, C aa Where is the air resistance coefficient; ρ is the air density. Under standard atmospheric pressure, the air density is affected by the temperature T. Its calculation formula is shown in (8); S wind is the windward area of ​​the cross-section of the unmanned platform; Δv represents the relative speed between the unmanned platform and the sea breeze, α represents the heading of the unmanned platform, θ represents the wind direction of the sea breeze, and v represents the speed of the unmanned platform itself; in formula (12), a 1 represents the influence factor of wave height on the speed of unmanned platform. Formula (11) is the calculation formula of wave height. In formula (13), a 2 Indicates the influence factor of flow velocity on the movement speed of the unmanned platform, Represents the direction of ocean current.

5. A method for marine unmanned mobile platform route planning based on a multi-factor environment field as claimed in claim 4, Its characteristics are: In step 3, the evaluation function for energy consumption is calculated as follows: Assume that the inherent storage energy of the unmanned platform is e total , assuming that the unmanned platform is moving at a constant power, if the power consumption per unit time during its forward movement is G a , wind power generation generates energy of G wind , the photovoltaic panel receives sunlight to generate energy G light , then the energy consumption evaluation function E during navigation is energy The calculation formulas for each part of energy are as follows: E energy =e total -G a +G wind +G light (14) G a =b·t (15) G light =Q s ·S light ·t·η 2 (17) In the above formulas (15) and (16), b is the energy consumption per unit time of the unmanned platform, t is the total navigation time, R is the fan blade radius of the wind turbine, and the fan energy conversion efficiency is η 1 ; In formula (17), Q s is the light radiation flux, S light is the effective light receiving area of ​​the photovoltaic panel, and the photovoltaic panel power conversion efficiency is η 2 .

6. A method for marine unmanned mobile platform route planning based on a multi-factor environment field as claimed in claim 5, Its characteristics are: In step 3, the evaluation function for security is calculated as follows: If the maximum wave height that the unmanned platform can withstand is H max , then the safety assessment function is calculated by the following formula: E safety =H max -h (18)。

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