A method for evaluating the task completion of cross-domain landing algorithms for amphibious unmanned vehicles

Abstract

The present invention relates to a method for evaluating the task completion of an amphibious unmanned vehicle cross-domain landing algorithm, which belongs to the field of task evaluation technology and solves the problem of the lack of an evaluation method for the task completion of a cross-domain landing algorithm in the prior art. The method comprises: constructing multiple evaluation data packets, and randomly issuing the evaluation data packets to the algorithm to be evaluated; the evaluation data packets include environmental data and task data; the algorithm to be evaluated is an amphibious unmanned vehicle cross-domain landing algorithm; calculating the environmental complexity and task complexity according to the evaluation data packets of the algorithm to be evaluated; obtaining the attitude data, positioning data, and identified shoreline data returned by the algorithm to be evaluated when performing the landing task, and calculating the basic completion of the algorithm to be evaluated according to the attitude data, positioning data, and identified shoreline data; and obtaining the comprehensive task completion of the algorithm to be evaluated based on the environmental complexity, task complexity, and basic completion. An objective and comprehensive evaluation of the task completion of the cross-domain landing algorithm of an amphibious unmanned vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the field of task evaluation technology, and in particular to a method for evaluating the task completion of an amphibious unmanned vehicle cross-domain landing algorithm. Background Art

[0002] Amphibious vehicles are specialized vehicles capable of both land and water operations, performing tasks such as underwater exploration and offshore landings. They play an irreplaceable role in unique geographical environments. With the advancement of technologies such as machine learning, deep learning, and reinforcement learning, amphibious vehicles are moving towards lightweight and unmanned operation. Amphibious unmanned vehicles are equipped with control algorithms that use sensors to perform visual detection, scene recognition, path planning, and control the vehicle's operation. Cross-domain landing missions are a key task for unmanned amphibious vehicles and a must-perform operation. These operations generally encompass two major operations: surface navigation and landing. The surface navigation operation generally requires the following: first, normal surface navigation, which involves controlling the vehicle's movement at a specific speed and direction while maintaining real-time control over natural disturbances (such as waves and wind); second, obstacle avoidance, which involves the ability to identify and avoid other surface users and obstacles such as reefs. The unmanned algorithm installed in the unmanned amphibious vehicle controls the vehicle's operation in two operating modes and the switching between these modes. The landing process of an amphibious vehicle generally includes the following steps: first, shoreline identification and path planning, namely identifying the landing area and planning the landing path; then, determining the appropriate time to switch the operating mode and switching the vehicle's operating mode, namely switching from "water operating mode" to "water and land operating mode." This step will adjust the vehicle's suspension, clutch, sluice gate, etc., so that it can transition from a purely water-based operation to a landing state; finally, landing, namely controlling the vehicle to complete the landing in a certain direction and reach the designated location. Amphibious unmanned vehicles also need to complete some special tasks during the cross-domain landing process, which in practice include but are not limited to reconnaissance and patrol, information collection, disaster relief, personnel and material transportation, and fire support.

[0003] Currently, there are unmanned algorithms used to complete corresponding login tasks, but there is no evaluation method for the completion of unmanned algorithm tasks in cross-domain login scenarios, making it difficult to quantitatively evaluate the performance of different algorithms. Summary of the Invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a method for evaluating the task completion of the cross-domain landing algorithm of an amphibious unmanned vehicle, so as to solve the problem of the existing lack of an evaluation method for the task completion of the cross-domain landing algorithm of an amphibious unmanned vehicle.

[0005] On the one hand, an embodiment of the present invention provides a method for evaluating the task completion of an amphibious unmanned vehicle cross-domain landing algorithm, comprising the following steps:

[0006] Construct multiple evaluation data packets and randomly distribute them to the algorithm to be evaluated; the evaluation data packets include environmental data and task data; the algorithm to be evaluated is an amphibious unmanned vehicle cross-domain landing algorithm;

[0007] Calculate the environment complexity and task complexity based on the evaluation data package of the algorithm to be evaluated;

[0008] Obtaining the posture data, positioning data, and identified shoreline data returned by the algorithm to be evaluated when performing the landing task according to the task data, and calculating the basic completion degree of the algorithm to be evaluated based on the posture data, positioning data, and identified shoreline data;

[0009] The comprehensive task completion of the algorithm to be evaluated is obtained based on the environment complexity, task complexity and basic completion.

[0010] Based on the further improvement of the above method, the basic completion of the algorithm to be evaluated is calculated based on the posture data, positioning data and recognized shoreline data, including:

[0011] The number of mission points passed by the amphibious unmanned vehicle is calculated based on the real-time positioning data returned by the algorithm to be evaluated to obtain the mission point completion degree;

[0012] Based on the real-time positioning data returned by the algorithm to be evaluated, it is determined whether the amphibious unmanned vehicle has a collision risk and the traffic safety degree is obtained;

[0013] The shoreline identification integrity and shoreline identification continuity are calculated based on the shoreline data returned by the algorithm to be evaluated in each inspection frame to obtain the shoreline identification completion degree;

[0014] Based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point, the landing direction deviation and landing position deviation are calculated;

[0015] Calculate the navigation attitude safety of each inspection frame based on the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame to obtain the navigation attitude safety of the algorithm to be evaluated;

[0016] Obtain the traffic efficiency of the algorithm to be evaluated according to the test completion time of the algorithm to be evaluated;

[0017] The basic completion of the algorithm to be evaluated is obtained based on the completion of the task points, traffic safety, shoreline identification completion, landing direction deviation, landing position deviation, navigation posture safety and traffic efficiency.

[0018] Based on the further improvement of the above method, the following formula is used to calculate the shoreline recognition integrity and shoreline recognition continuity according to the shoreline data returned by the algorithm to be evaluated in each inspection frame, and the shoreline recognition completion degree is obtained:

[0019] M3=M 3-1 +M 3-2

[0020]

[0021] Among them, M3 represents the shoreline recognition completion of the algorithm to be evaluated, N IF Indicates the number of inspection frames, M 3-1 Indicates the completeness of shoreline identification, M 3-2 Indicates the continuity of shoreline identification, X i,max represents the maximum horizontal coordinate of the real coastline of the i-th survey frame, X i,min represents the minimum horizontal coordinate of the real coastline of the i-th survey frame, x i,max represents the maximum horizontal coordinate of the coastline of the i-th inspection frame identified by the algorithm to be evaluated, x i,min represents the minimum horizontal coordinate of the shoreline identified by the algorithm to be evaluated in the i-th survey frame, μ standard Represents the standard coastline point cloud spacing variance, μ i Represents the point cloud spacing variance of the coastline identified by the algorithm to be evaluated.

[0022] Based on the further improvement of the above method, the following formula is used to calculate the navigation attitude safety of each inspection frame according to the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame, and the navigation attitude safety of the algorithm to be evaluated is obtained:

[0023]

[0024] Among them, M6 represents the navigation attitude safety of the algorithm to be evaluated, N IF represents the number of inspection frames, σ i1 represents the pitch angle of the i-th survey frame, σ1 lim Indicates the maximum value of the pitch angle, σ i2 represents the roll angle of the i-th inspection frame, σ2 lim represents the roll angle limit, ω1 and ω2 represent weights, α i1 and α i2 is the intermediate parameter.

[0025] Based on the further improvement of the above method, the following formula is used to calculate the landing direction deviation and landing position deviation based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point:

[0026]

[0027] Among them, M4 represents the landing direction deviation, M5 represents the landing position deviation, (X0, Y0) represents the target landing point coordinates, (X self ,Y self) represents the coordinate position of the amphibious unmanned vehicle when it completes landing, δ represents the heading angle of the amphibious unmanned vehicle when it completes landing, θ represents the expected landing direction in the global coordinate system, R d Indicates the error radius.

[0028] Based on further improvements to the above method, the following method is used to determine whether the amphibious unmanned vehicle has a collision risk based on the real-time positioning data returned by the algorithm to be evaluated, and to obtain the traffic safety degree:

[0029] Based on the distance between the positioning data returned by the algorithm to be evaluated in each inspection frame and the center point of the obstacle, it is determined whether the amphibious unmanned vehicle has collided with the obstacle in the inspection frame;

[0030] The traffic safety is obtained according to the following formula:

[0031]

[0032] Among them, M2 represents the traffic safety, m 2i Indicates the communication security of the i-th inspection frame, N RBi represents the total number of risk obstacles in the i-th inspection frame, n RBi N represents the number of times the amphibious unmanned vehicle collides with risk obstacles in the i-th inspection frame, IF Indicates the number of inspection frames.

[0033] Based on the further improvement of the above method, the task complexity is calculated according to the evaluation data package of the algorithm to be evaluated, including:

[0034] Calculate the average proportion of semantic label types in each scene frame, and obtain the label type complexity based on the average proportion of semantic label types in each scene frame;

[0035] The complexity of the inspection frame number is obtained according to the number of inspection frames in the task data;

[0036] Obtaining the guidance point complexity according to the guidance point information in the task data;

[0037] The complexity of the landing area is obtained based on the area ratio of the landing area and the area ratio of the passable area in the mission data;

[0038] The complexity of the slope of the landing point is obtained according to the slope of the landing point;

[0039] The task complexity is obtained based on the complexity of label type, complexity of inspection frame number, complexity of guidance point, complexity of landing area and complexity of landing point slope.

[0040] Based on the further improvement of the above method, the guidance point complexity is obtained according to the guidance point information in the task data, including:

[0041] The dimension complexity of the guiding point is obtained according to the coordinate dimension of the guiding point;

[0042] The complexity of the number of guiding points is obtained according to the number of guiding points;

[0043] Calculating the curvature of each guiding point, and obtaining the curvature complexity of the guiding point according to the ratio of the number of guiding points whose curvature exceeds a first threshold to the total number of guiding points;

[0044] The guidance point complexity is obtained based on the guidance point dimension complexity, the guidance point dimensional complexity and the guidance point curvature complexity.

[0045] Based on the further improvement of the above method, the curvature of each guide point is calculated in the following way:

[0046] For each guide point, find the four nearest guide points, and perform data fitting on the guide point and its four nearest guide points to obtain a fitting function;

[0047] According to the formula Calculate the curvature of the guide point, where K represents the curvature of the guide point and y' represents the curvature of the fitting function at x h The second derivative value of the point, y' represents the fitting function at x h The first derivative value of the point, x h Indicates the X-axis coordinate value of the guide point.

[0048] Based on the further improvement of the above method, the following formula is used to calculate the average semantic label type ratio of each scene frame:

[0049]

[0050] Among them, N F Indicates the number of scene frames, N tagi Indicates the number of label types for the i-th scene frame.

[0051] Compared with the prior art, the present invention constructs multiple evaluation data packets and randomly sends evaluation data packets to the algorithm to be evaluated, thereby evaluating multiple algorithms to be evaluated at the same time. The basic completion degree of the algorithm to be evaluated is calculated based on the posture data, positioning data and identified shoreline data returned by the algorithm to be evaluated, and the environmental complexity and task complexity of the algorithm to be evaluated for performing the landing task are calculated based on the evaluation data packet of the algorithm to be evaluated. The comprehensive task completion degree of the algorithm to be evaluated is obtained based on the environmental complexity, task complexity and basic completion degree, thereby not only examining the performance of the algorithm to be evaluated in completing the landing task, but also considering the environment and task complexity of its execution of the landing task, thereby comprehensively and objectively quantitatively evaluating the performance of the algorithm to be evaluated.

[0052] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols denote the same components.

[0054] Figure 1 This is a flowchart of a method for evaluating the task completion of an amphibious unmanned vehicle's cross-domain landing algorithm according to an embodiment of the present invention;

[0055] Figure 2 This is a diagram of the composition of cross-domain login task completion evaluation indicators in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0057] Environmental factors like wind, waves, and currents can affect the amphibious unmanned vehicle's heading, attitude, and power control. The complexity of the landing mission affects the amphibious unmanned vehicle's algorithm's scene understanding and path planning, which in turn affects its landing mission completion. Therefore, assessing landing mission completion not only examines whether the algorithm can safely control the amphibious unmanned vehicle to the designated landing point, but also examines the environmental factors involved in the mission execution and the complexity of the mission itself.

[0058] A specific embodiment of the present invention discloses a method for evaluating the task completion of an amphibious unmanned vehicle cross-domain landing algorithm. Figure 1 As shown, the following steps are included:

[0059] S1. Construct multiple evaluation data packages and randomly distribute them to the algorithm to be evaluated; the evaluation data packages include environmental data and task data; the algorithm to be evaluated is an amphibious unmanned vehicle cross-domain landing algorithm;

[0060] S2. Calculate the environment complexity and task complexity based on the evaluation data package of the algorithm to be evaluated;

[0061] S3. Obtaining the posture data, positioning data, and identified shoreline data returned by the algorithm to be evaluated when performing the landing task according to the task data, and calculating the basic completion degree of the algorithm to be evaluated based on the posture data, positioning data, and identified shoreline data;

[0062] S4. Obtain the comprehensive task completion of the algorithm to be evaluated based on the environment complexity, task complexity and basic completion.

[0063] It should be noted that the algorithm to be evaluated completes the evaluation task based on the received evaluation data packet, and returns posture data, positioning data and identified shoreline data in accordance with the evaluation task requirements during the task completion process. The evaluation end evaluates the task completion degree of the algorithm to be evaluated based on the data returned by the algorithm to be evaluated.

[0064] Compared with the existing technology, the task completion evaluation method for the cross-domain landing algorithm of an amphibious unmanned vehicle provided in this embodiment constructs multiple evaluation data packets and randomly sends evaluation data packets to the algorithm to be evaluated, thereby simultaneously evaluating multiple algorithms to be evaluated. The basic completion of the algorithm to be evaluated is calculated based on the posture data, positioning data and identified shoreline data returned by the algorithm to be evaluated, and the environmental complexity and task complexity of the landing task performed by the algorithm to be evaluated are calculated based on the evaluation data packet of the algorithm to be evaluated. The comprehensive task completion of the algorithm to be evaluated is obtained based on the environmental complexity, task complexity and basic completion. Therefore, not only the performance of the algorithm to be evaluated in completing the landing task is examined, but also the environment and task complexity of its landing task are considered, thereby comprehensively and objectively quantitatively evaluating the performance of the algorithm to be evaluated.

[0065] During implementation, the evaluation data package includes environmental data and login task data. The environmental data and login task data of different evaluation data packages may be different. The evaluation algorithm can be evaluated through individual evaluation data packages, or the algorithm to be evaluated can be made to perform the evaluation tasks of multiple evaluation data packages. The evaluation results of multiple evaluation data packages can be combined to obtain the final evaluation result of the algorithm to be evaluated.

[0066] During implementation, the environmental data in the assessment data package include water surface status, water surface environmental characteristic level, shoreline change degree level, shoreline terrain category, weather type and lighting condition level.

[0067] When an unmanned amphibious vehicle is sailing on the sea, the state of the water surface, including the height of the waves and the water flow rate, will have a great impact on the heading, attitude, and power control of the unmanned vehicle, and may even cause the vehicle to capsize. Therefore, the water surface state is an aspect that reflects the complexity of the environment. The complexity corresponding to the sea state level is shown in Table 1:

[0068] Table 1 Water surface state complexity standard

[0069]

[0070] The water surface conditions corresponding to each sea state level are as follows:

[0071] Level 0: Wave height 0 meters. The sea is calm, the water surface is as smooth as a mirror, or there are only swells, and the boat is stationary.

[0072] Level 1: Wave height 0-0.1 meters. Ripples or swells and ripples exist simultaneously, with small waves resembling fish scales, and no spray.

[0073] Level 2: Wave height 0.1-0.5 meters. The waves are small and the crests begin to break, but the waves are not white but glassy in color.

[0074] Level 3: Wave height 0.5-1.25 meters. The waves are not large, but very eye-catching, with breaking crests, shiny foam, and sometimes whitecaps.

[0075] Level 4: Wave height 1.25-2.5 meters. The waves have a distinct shape and whitecaps form everywhere.

[0076] Level 5: Waves 2.5-4 meters high. Large crests appear, with spray covering a large area of ​​the crests. The wind begins to shave off the spray. The sea is extremely turbulent, making navigation difficult.

[0077] Level 6 and above: Waves are greater than 4 meters high. The sea is rough, the air is filled with white spray, water droplets, and spray, visibility is reduced, and navigation is extremely dangerous.

[0078] Water surface environmental features include both natural and artificial features. Natural features include natural objects on the sea surface, such as reefs and ice floes, as well as hydrological information. Artificial features include ships and buoys. These features can affect the navigation of amphibious unmanned vehicles. For example, reefs are static obstacles, while ships are dynamic obstacles that need to be avoided. Therefore, water surface environmental features are classified and their complexity is given, as shown in Table 2:

[0079] Table 2 Water surface feature complexity standards

[0080]

[0081] The corresponding status of each category is as follows:

[0082] Artificial features are obvious: various buoys and large and small ships are clearly visible on the sea surface;

[0083] Obvious natural features: natural features such as reefs, pumice, and ice floes on the sea surface are clear, and ocean hydrological information is obvious;

[0084] No features: None of the above information is obvious.

[0085] Due to tidal effects, the position of the shoreline changes over time, especially during high and low tides, when the shoreline position fluctuates significantly. This dynamic shoreline change can affect the amphibious unmanned vehicle's shoreline recognition, thus affecting the timing of operational transitions. Therefore, the complexity of the shoreline boundary is analyzed and calculated. The shoreline is classified by the degree of positional change and the complexity is given in Table 3:

[0086] Table 3 Coastline boundary complexity standards

[0087]

[0088] The quantitative basis for classifying shorelines is as follows:

[0089] No-change shoreline boundary: The horizontal position change of the shoreline within 24 hours is less than 10m;

[0090] Slightly changing shoreline boundary: The horizontal position of the shoreline changes between 10m and 100m within 24 hours;

[0091] Significantly changing shoreline boundary: The horizontal position of the shoreline changes by more than 100m within 24 hours.

[0092] The impact of shoreline topography on landing missions primarily lies in the following two aspects: First, the slope of the shoreline determines whether an amphibious vehicle can successfully land; a steeper slope can cause the vehicle to become bogged down. Second, the firmness of the ground also affects the landing process; softer mud and sand can cause the vehicle to become bogged down, while artificially paved slopes facilitate landing. Therefore, shorelines are categorized into the following typical types based on topography, and their complexity is assigned based on the ease of landing, as shown in the following table:

[0093] Table 4 Coastline terrain complexity standards

[0094]

[0095] The classification characteristics of various types of coastlines are as follows:

[0096] Artificially paved slope: mostly made of concrete, with a straight road surface and a gentle slope;

[0097] Gravel banks: These are mainly composed of gravel and sand, forming landforms such as sandbanks and sandbars. These banks are made of coarse grains, washed and transported by the sea to form sandy beaches that slope gently toward the ocean.

[0098] Muddy coast: mainly composed of silt and fine sand, with a wide coastal zone, a small slope and a straight coastline;

[0099] Earthen banks: mainly composed of mud, often covered with vegetation, and are often found in rivers, lakes and other water bodies. The angle between the land and the water surface is not large, and the height difference between the land and the water surface is small;

[0100] Bedrock coast: A hard coast with more than 75% rock as its base, including rocky coastal islands and sea cliffs. This type of coastline is often steep, with a large angle between land and sea.

[0101] Weather conditions affect both the perception and control of amphibious unmanned vehicles. For example, rain and snow can affect camera images to a certain extent, solid particulates can significantly affect lidar recognition accuracy, and wind speed can directly or indirectly affect the vehicle's heading and speed. This article analyzes weather conditions, classifies weather factors that affect unmanned vehicle navigation, and assigns a complexity level, as shown in Table 5:

[0102] Table 5 Weather state complexity standards

[0103]

[0104] The classification of each weather element is based on the following:

[0105] No wind: wind speed is less than 3m / s;

[0106] Windy: wind speed is between 3m / s and 8m / s;

[0107] Storm: wind speed greater than 8m / s;

[0108] Light rain: rainfall less than 10 mm in 24 hours;

[0109] Moderate rain: 24-hour rainfall is between 10 mm and 50 mm;

[0110] Heavy rain: rainfall in 24 hours is between 50mm and 100mm;

[0111] Heavy rain: rainfall greater than 100 mm in 24 hours;

[0112] Light snow: precipitation in 24 hours is less than 2.4 mm;

[0113] Moderate snow: 24-hour precipitation between 2.4 mm and 4.9 mm;

[0114] Heavy snow: 24-hour precipitation between 4.9 mm and 9.9 mm;

[0115] Blizzard: precipitation greater than 9.9 mm in 24 hours;

[0116] Light fog: horizontal visibility is greater than or equal to 400m;

[0117] Dense fog: horizontal visibility is less than 400m.

[0118] Lighting conditions affect data acquisition by the autonomous vehicle's image sensors. Poor lighting conditions can significantly impact the vehicle's environmental perception, hindering planning and control, and even leading to misjudgments. Lighting conditions are categorized by their complexity, as shown in Table 6:

[0119] Table 6 Complexity standards of lighting conditions

[0120]

[0121] During implementation, the complexity results of each environmental data are weighted and summed to obtain the final environmental complexity.

[0122] Task data includes:

[0123] (1) A set of scene frame sequences for testing, the environmental semantic labels of each scene frame, and the radar point cloud data corresponding to each scene frame. Each scene frame is identified by a unique timestamp.

[0124] (2) Guidance point information: Guidance points are points that the amphibious unmanned vehicle needs to pass through during the planning process. Their function is to provide path information and guide the algorithm to be evaluated to perform path planning.

[0125] (3) Mission point information, including the positioning information of each mission point. The last mission point is the landing point, and the landing point information includes positioning information and slope information. The amphibious unmanned vehicle should pass through every mission point as much as possible.

[0126] (4) Examination frame timestamp: The evaluation task uses certain scene frames as examination frames. When the algorithm to be evaluated executes the scene frame with the corresponding timestamp, it needs to return the information required by the evaluation task, such as posture data and recognized shoreline data.

[0127] The algorithm to be evaluated performs scene understanding on each scene frame in timestamp order, identifies shoreline information based on radar point cloud data, plans paths based on environmental semantic labels, guide point information, and mission point information in the scene frames, and controls the amphibious unmanned vehicle to perform cross-domain landing tasks.

[0128] It should be noted that the algorithm to be evaluated returns positioning data in real time when performing the login task.

[0129] The complexity of the task will also affect the evaluation results of the evaluation algorithm, so it is necessary to calculate the task complexity. Specifically, the task complexity is calculated based on the evaluation data package of the algorithm to be evaluated, including:

[0130] S21. Calculate the average proportion of semantic label types in each scene frame, and obtain the label type complexity based on the average proportion of semantic label types in each scene frame;

[0131] The ratio of the number of semantic labels contained in each scene frame to the total semantic labels for the shoreline indicates the scene richness of the target shoreline for the landing mission scenario. The greater the number of semantic labels, the more complex the shoreline scene and the greater the difficulty of understanding the scene. The total semantic labels for shoreline landing missions include the following eight categories: water area, shoreline, easy landing, difficult landing, impossible landing, risky obstacle, impassable obstacle, and pedestrian.

[0132] The following formula is used to calculate the average semantic label type ratio of each scene frame:

[0133]

[0134] Among them, N F Indicates the number of scene frames, N tagi Indicates the number of label types for the i-th scene frame.

[0135] According to the average proportion of semantic label types in each scene frame, that is, the proportion of the number of semantic labels in a single shoreline frame to the total number of labels, the label type complexity results are obtained according to Table 7.

[0136] Table 7. Complexity of semantic labels for a single frame of the coastline

[0137]

[0138] S22, obtaining the complexity of the number of inspection frames according to the number of inspection frames in the task data;

[0139] The more frames examined, the more scenes the landing mission examines, and the higher the difficulty of scene understanding. The complexity corresponding to the number of examination frames is shown in Table 8:

[0140] Table 8 Coastline survey frame complexity standard

[0141]

[0142] S23, obtaining the guidance point complexity according to the guidance point information in the task data;

[0143] The guidance point is the point that the amphibious unmanned vehicle needs to pass through during the planning process. Its function is to provide path information and guide the amphibious unmanned vehicle to plan the path. The information it provides will have a great impact on the difficulty of the unmanned vehicle's path planning.

[0144] Furthermore, when navigating on water, amphibious vehicles rely on sluice gates and rudders for heading control, and in principle, can achieve on-the-spot steering. However, considering the influence of water flow and wind speed, actually completing large-angle steering is extremely challenging for the unmanned vehicle's control precision. Therefore, the curvature of the guide point is used as a key indicator affecting control complexity.

[0145] Therefore, the guidance point complexity is obtained according to the guidance point information in the task data, including:

[0146] The dimension complexity of the guiding point is obtained according to the coordinate dimension of the guiding point;

[0147] The complexity of the number of guiding points is obtained according to the number of guiding points;

[0148] Calculating the curvature of each guiding point, and obtaining the curvature complexity of the guiding point according to the ratio of the number of guiding points whose curvature exceeds a first threshold to the total number of guiding points;

[0149] The guidance point complexity is obtained based on the guidance point dimension complexity, the guidance point dimensional complexity and the guidance point curvature complexity.

[0150] The dimensions of the guide point include the position coordinates of the guide point in the global coordinate system in the x, y, and z directions, as well as the vehicle heading information at the guide point. The dimensional complexity of the guide point indicates the comprehensiveness of the guide point information provided by the evaluation task. The more dimensions of the guide points provided in the evaluation data package, that is, the more comprehensive the guide point information, the lower the path planning difficulty. The specific complexity can be seen in Table 9:

[0151] Table 9 Guidance point dimensional complexity standards

[0152]

[0153]

[0154] The number of guide points reflects the overall path planning difficulty provided by the assessment. The more guide points there are, the lower the path planning difficulty. The number of guide points can be represented by the average interval between guide points in the task data, calculated according to the following formula:

[0155]

[0156] Among them, N GP is the total number of guiding points, L i is the distance between every two guiding points (the i-th guiding point and the i+1-th guiding point). The specific complexity of the number of guiding points is shown in Table 10:

[0157] Table 10: Complexity of number of guiding points

[0158]

[0159] Specifically, the curvature of each guide point is calculated in the following way:

[0160] For each guide point, find the four nearest guide points, and perform data fitting on the guide point and its four nearest guide points to obtain a fitting function;

[0161] According to the formula Calculate the curvature of the guide point, where K represents the curvature of the guide point and y' represents the curvature of the fitting function at x h The second derivative value of the point, y' represents the fitting function at x h The first derivative value of the point, x h Indicates the X-axis coordinate value of the guide point.

[0162] When implementing, for the guide point (x h ,y h ) and the four nearest neighboring guide points of the guide point are fitted by the least square method to obtain a fitting function. During implementation, the fitting function is determined based on the distribution of the five points, for example, it can be a logarithmic function.

[0163] Find the fitting function at x = x h The first-order derivative value y' and the second-order derivative value y" at the time can be used to obtain the curvature of the guiding point according to the above curvature calculation formula.

[0164] During implementation, the guidance points whose curvature exceeds the first threshold are difficulty points. The first threshold can be set to 0.05. According to the ratio of the number of difficulty points to the total number of guidance points, the curvature complexity of the guidance points is obtained by referring to Table 13.

[0165] Table 11 Guidance point curvature complexity standard

[0166]

[0167] The guiding point complexity is obtained by adding the guiding point dimensional complexity, the guiding point dimension complexity and the guiding point curvature complexity.

[0168] S24. Obtaining the complexity of the landable area based on the area ratio of the landable area and the area ratio of the passable area in the task data;

[0169] In most coastal environments, only a portion of the coast is suitable for landing missions. Therefore, when planning a route, the complexity of the landing area directly affects the difficulty of the mission. The complexity of the landing area is composed of two aspects: the proportion of the landing area and the continuity of the landing area.

[0170] The landable area ratio represents the ratio of the landable area to the entire target shore area, which directly determines the area available for path planning and the richness of the path planning. This can be quantified by the ratio R1 of the number of grids labeled "shoreline," "easy landing," and "difficult landing" to the total number of grids in all scene frames in the mission data.

[0171] The proportion of landable areas directly affects the difficulty of path planning during the landing task. A larger proportion of landable areas indicates lower complexity and easier path planning. Complexity is calculated linearly based on the proportion, with specific criteria listed in Table 12.

[0172] Table 12 Complexity standard of the proportion of accessible areas

[0173]

[0174] Landing area continuity refers to whether the target beach's shoreline is continuous and unified, that is, whether it is blocked by obstacles or impassable areas. The higher the shoreline continuity, the easier it is to plan the landing path. Continuity complexity can be assessed by the R² ratio (R²) of the total number of grid cells labeled "risk obstacles" and "impassable obstacles" within the shoreline to the number of grid cells labeled "shoreline." Specific indicators and evaluation criteria are shown in Table 13.

[0175] Table 13 Continuity standards for landing areas

[0176]

[0177] S25. Obtaining the complexity of the slope of the landing point according to the slope of the landing point;

[0178] When landing on a beach, the slope of the landing point tests the autonomous vehicle's ability to control the throttle, clutch, and other functions. The slope values ​​of the landing point are quantified, and the specific complexity is shown in Table 14.

[0179] Table 14 Landing point slope complexity standards

[0180]

[0181] S26. Obtain the task complexity according to the complexity of the tag type, the complexity of the number of inspection frames, the complexity of the guidance point, the complexity of the landing area, and the complexity of the landing point slope.

[0182] The task complexity is obtained by adding the complexity of label type, complexity of inspection frame number, complexity of guidance point, complexity of landing area and complexity of landing point slope.

[0183] Calculate the basic completion of the algorithm to be evaluated based on the posture data, positioning data, and recognized shoreline data returned by the algorithm to be evaluated, including:

[0184] S31. Calculate the number of mission points passed by the amphibious unmanned vehicle based on the real-time positioning data returned by the algorithm to be evaluated to obtain the mission point completion degree;

[0185] During the landing process, the amphibious unmanned vehicle needs to complete the designated mission at the designated mission point. The amphibious unmanned vehicle should be able to pass through every mission point. Therefore, the positioning data returned by the algorithm to be evaluated in real time is used to determine whether the amphibious unmanned vehicle has passed through the mission point. The completion degree of the mission point of the algorithm to be evaluated is obtained based on the ratio of the number of mission points passed to the total number of mission points. N finishis the number of completed mission points, N mission The total number of mission points.

[0186] During implementation, based on the positioning data returned by the algorithm to be evaluated, if the distance difference between the positioning returned by the algorithm to be evaluated and the positioning of a certain task point is less than a preset threshold, such as 2m, then it is considered that the algorithm to be evaluated has passed the task point.

[0187] S32. Determine whether the amphibious unmanned vehicle has a collision risk based on the real-time positioning data returned by the algorithm to be evaluated, and obtain a traffic safety degree.

[0188] Vehicle safety metrics assess the safety of the vehicle itself and its surroundings during mission completion, and are crucial for actual landing missions. During implementation, obstacles that affect the amphibious unmanned vehicle in each scene frame primarily include risk obstacles and impassable obstacles.

[0189] The following method is used to determine whether the amphibious unmanned vehicle has a collision risk based on the real-time positioning data returned by the algorithm to be evaluated, and to obtain the traffic safety degree:

[0190] Based on the distance between the positioning data returned by the algorithm to be evaluated in each inspection frame and the center point of the obstacle, it is determined whether the amphibious unmanned vehicle has collided with the obstacle in the inspection frame;

[0191] The traffic safety is obtained according to the following formula:

[0192]

[0193] Among them, m 2i Indicates the communication security of the i-th inspection frame, N RBi represents the total number of risk obstacles in the i-th inspection frame, n RBi N represents the number of times the amphibious unmanned vehicle collides with risk obstacles in the i-th inspection frame, IF It represents the number of inspection frames, and M2 represents the degree of traffic safety.

[0194] During implementation, if the distance difference between the location returned by the algorithm being evaluated and the center point of an obstacle is less than a preset threshold, the amphibious unmanned vehicle is considered to have collided with the obstacle. The threshold may vary for different types of obstacles.

[0195] S33, calculating shoreline identification integrity and shoreline identification continuity based on the identified shoreline data returned by the algorithm to be evaluated in each inspection frame, to obtain shoreline identification completion;

[0196] The completeness of shoreline identification plays a crucial role in landing path planning and vehicle control during the landing of an amphibious unmanned vehicle. This test primarily examines the integrity and continuity of shoreline identification. Integrity primarily examines whether the identified shoreline boundaries are correct, while continuity primarily examines whether there are any breakpoints in the shoreline identification.

[0197] Specifically, the shoreline recognition integrity and shoreline recognition continuity are calculated based on the shoreline data returned by the algorithm to be evaluated in each inspection frame to obtain the shoreline recognition completion degree using the following formula:

[0198] M3=M 3-1 +M 3-2

[0199]

[0200] Among them, M3 represents the shoreline recognition completion of the algorithm to be evaluated, N IF Indicates the number of inspection frames, M 3-1 Indicates the completeness of shoreline identification, M 3-2 Indicates the continuity of shoreline identification, X i,max represents the maximum horizontal coordinate of the real coastline of the i-th survey frame, X i,min represents the minimum horizontal coordinate of the real coastline of the i-th survey frame, x i,max represents the maximum horizontal coordinate of the coastline of the i-th inspection frame identified by the algorithm to be evaluated, x i,min represents the minimum horizontal coordinate of the shoreline identified by the algorithm to be evaluated in the i-th survey frame, μ standard Represents the standard coastline point cloud spacing variance, μ i Represents the point cloud spacing variance of the coastline identified by the algorithm to be evaluated.

[0201] S34. Calculate the landing direction deviation and landing position deviation based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point;

[0202] In a landing mission, in order to facilitate the next step planning after landing, an expected landing direction is usually set. The greater the deviation from the expected landing direction, the lower the completion rate.

[0203] The last mission point of the landing mission is the final target point of the landing, that is, the landing point. The amphibious unmanned vehicle must stop at the target point after landing. The greater the position deviation, the lower the completion rate.

[0204] Specifically, the landing direction deviation and landing position deviation are calculated using the following formula based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point:

[0205]

[0206] Among them, M4 represents the landing direction deviation, M5 represents the landing position deviation, (X0, Y0) represents the target landing point coordinates, (X self ,Y self ) represents the coordinate position of the amphibious unmanned vehicle when it completes landing, δ represents the heading angle of the amphibious unmanned vehicle when it completes landing, θ represents the expected landing direction in the global coordinate system, R d Indicates the error radius.

[0207] S35. Calculate the navigation attitude safety of each inspection frame based on the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame to obtain the navigation attitude safety of the algorithm to be evaluated;

[0208] When traveling on the water, the navigation attitude of an amphibious vehicle has a significant impact on the vehicle's safety. Therefore, the control and evaluation of the navigation attitude has a profound impact on the safety of landing cross-domain missions. The navigation attitude safety mainly examines the pitch angle and roll angle.

[0209] Specifically, the following formula is used to calculate the navigation attitude safety of each inspection frame based on the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame, and the navigation attitude safety of the algorithm to be evaluated is obtained:

[0210]

[0211] Among them, M6 represents the navigation attitude safety of the algorithm to be evaluated, N IF represents the number of inspection frames, σ i1 represents the pitch angle of the i-th survey frame, σ1 lim Indicates the maximum value of the pitch angle, σ i2 represents the roll angle of the i-th inspection frame, σ2 lim represents the roll angle limit, ω1 and ω2 represent weights, α i1 and α i2 is the intermediate parameter.

[0212] During implementation, for example, the pitch angle limit value is set to 20°, and the roll angle limit value is set to 15°.

[0213] S36. Obtaining the traffic efficiency of the algorithm to be evaluated based on the test completion time of the algorithm to be evaluated;

[0214] The speed of an amphibious unmanned vehicle significantly impacts planning and control difficulty. The faster the vehicle, the shorter its reaction time to the scene, placing higher demands on sensor sensitivity, information transmission efficiency, and actuator responsiveness. Environmental factors also impact the vehicle's speed. To more comprehensively evaluate the effectiveness of landing missions and avoid sacrificing speed to improve other metrics, the vehicle's traffic efficiency was examined.

[0215] The traffic efficiency M7 is calculated using the following formula:

[0216]

[0217] Where T is the manual driving time, and t is the test completion time of the algorithm to be evaluated.

[0218] S37. Based on the task point completion, passage safety, shoreline identification completion, landing direction deviation, landing position deviation, navigation posture safety and passage efficiency, the basic completion of the algorithm to be evaluated is obtained.

[0219] The basic completion degree is obtained by adding up the mission point completion degree, passage safety degree, shoreline identification completion degree, landing direction deviation degree, landing position deviation degree, navigation posture safety degree and passage efficiency.

[0220] For each algorithm to be evaluated, its environmental complexity and task complexity are used as weights and multiplied by the basic completion degree to obtain the comprehensive task completion degree of the algorithm to be evaluated. In this way, the landing task completion degree of the amphibious unmanned vehicle is comprehensively and objectively evaluated by considering the environmental complexity and task complexity, and the evaluation results are obtained.

[0221] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0222] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for evaluating the task completion of an amphibious unmanned vehicle's cross-domain landing algorithm, characterized in that: The following steps are involved: Construct multiple evaluation data packets and randomly distribute the evaluation data packets to the algorithm to be evaluated; the evaluation data packets include environmental data and task data; the algorithm to be evaluated is an amphibious unmanned vehicle cross-domain landing algorithm; the environmental data include water surface status, water surface environmental feature level, shoreline change degree level, shoreline terrain type, weather type and lighting condition level; the task data includes a set of scene frame sequences for testing, environmental semantic labels for each scene frame, radar point cloud data corresponding to each scene frame, guide point information, task point information, inspection frame timestamp, and information required to be returned by the algorithm to be evaluated when executing the inspection frame; Calculate the environment complexity and task complexity based on the evaluation data package of the algorithm to be evaluated; Obtaining the posture data, positioning data, and identified shoreline data returned by the algorithm to be evaluated when performing the landing task according to the task data, and calculating the basic completion degree of the algorithm to be evaluated based on the posture data, positioning data, and identified shoreline data; Obtain the comprehensive task completion of the algorithm to be evaluated based on the environment complexity, task complexity and basic completion; Calculate the basic completion of the algorithm to be evaluated based on the posture data, positioning data, and recognized shoreline data, including: The number of mission points passed by the amphibious unmanned vehicle is calculated based on the real-time positioning data returned by the algorithm to be evaluated to obtain the mission point completion degree; Based on the real-time positioning data returned by the algorithm to be evaluated, it is determined whether the amphibious unmanned vehicle has a collision risk and the traffic safety degree is obtained; The shoreline identification integrity and shoreline identification continuity are calculated based on the shoreline data returned by the algorithm to be evaluated in each inspection frame to obtain the shoreline identification completion degree; Based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point, the landing direction deviation and landing position deviation are calculated; Calculate the navigation attitude safety of each inspection frame based on the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame to obtain the navigation attitude safety of the algorithm to be evaluated; Obtain the traffic efficiency of the algorithm to be evaluated according to the test completion time of the algorithm to be evaluated; The basic completion of the algorithm to be evaluated is obtained based on the completion of the task points, traffic safety, shoreline identification completion, landing direction deviation, landing position deviation, navigation posture safety and traffic efficiency.

2. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: The shoreline recognition integrity and shoreline recognition continuity are calculated based on the shoreline data returned by the algorithm to be evaluated in each inspection frame using the following formula to obtain the shoreline recognition completion degree: M3=M 3-1 +M 3-2 Among them, M3 represents the shoreline recognition completion of the algorithm to be evaluated, N IF Indicates the number of inspection frames, M 3-1 Indicates the completeness of shoreline identification, M 3-2 Indicates the continuity of shoreline identification, X i,max represents the maximum horizontal coordinate of the real coastline of the i-th survey frame, X i,min represents the minimum horizontal coordinate of the real coastline of the i-th survey frame, x i,max represents the maximum horizontal coordinate of the coastline of the i-th inspection frame identified by the algorithm to be evaluated, x i,min represents the minimum horizontal coordinate of the shoreline identified by the algorithm to be evaluated in the i-th survey frame, μ standard Represents the standard coastline point cloud spacing variance, μ i Represents the point cloud spacing variance of the coastline identified by the algorithm to be evaluated.

3. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: The following formula is used to calculate the navigation attitude safety of each inspection frame based on the pitch angle and roll angle returned by the algorithm to be evaluated in each inspection frame, and the navigation attitude safety of the algorithm to be evaluated is obtained: Among them, M6 represents the navigation attitude safety of the algorithm to be evaluated, N IF represents the number of inspection frames, σ i1 represents the pitch angle of the i-th survey frame, σ1 lim Indicates the maximum value of the pitch angle, σ i2 represents the roll angle of the i-th inspection frame, σ2 lim represents the roll angle limit, ω1 and ω2 represent weights, α i1 and α i2 is the intermediate parameter.

4. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: The following formula is used to calculate the landing direction deviation and landing position deviation based on the heading angle and positioning data of the amphibious vehicle-free person returned by the algorithm to be evaluated at the landing point: Among them, M4 represents the landing direction deviation, M5 represents the landing position deviation, (X0, Y0) represents the target landing point coordinates, (X self ,Y self ) represents the coordinate position of the amphibious unmanned vehicle when it completes landing, δ represents the heading angle of the amphibious unmanned vehicle when it completes landing, θ represents the expected landing direction in the global coordinate system, R d Indicates the error radius.

5. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: The following method is used to determine whether the amphibious unmanned vehicle has a collision risk based on the real-time positioning data returned by the algorithm to be evaluated, and to obtain the traffic safety degree: Based on the distance between the positioning data returned by the algorithm to be evaluated in each inspection frame and the center point of the obstacle, it is determined whether the amphibious unmanned vehicle has collided with the obstacle in the inspection frame; The traffic safety degree is obtained according to the following formula: Among them, M2 represents the traffic safety, m 2i Indicates the communication security of the i-th inspection frame, N RBi represents the total number of risk obstacles in the i-th inspection frame, n RBi N represents the number of times the amphibious unmanned vehicle collides with risk obstacles in the i-th inspection frame, IF Indicates the number of inspection frames.

6. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: Calculate the task complexity based on the evaluation data package of the algorithm to be evaluated, including: Calculate the average proportion of semantic label types in each scene frame, and obtain the label type complexity based on the average proportion of semantic label types in each scene frame; The complexity of the inspection frame number is obtained according to the number of inspection frames in the task data; Obtaining the guidance point complexity according to the guidance point information in the task data; The complexity of the landing area is obtained based on the area ratio of the landing area and the area ratio of the passable area in the mission data; The complexity of the slope of the landing point is obtained according to the slope of the landing point; The task complexity is obtained based on the complexity of label type, complexity of inspection frame number, complexity of guidance point, complexity of landing area and complexity of landing point slope.

7. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 1 is characterized in that: The guidance point complexity is obtained based on the guidance point information in the task data, including: The dimension complexity of the guiding point is obtained according to the coordinate dimension of the guiding point; The complexity of the number of guiding points is obtained according to the number of guiding points; Calculating the curvature of each guiding point, and obtaining the curvature complexity of the guiding point according to the ratio of the number of guiding points whose curvature exceeds a first threshold to the total number of guiding points; The guidance point complexity is obtained based on the guidance point dimension complexity, the guidance point dimensional complexity and the guidance point curvature complexity.

8. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 7 is characterized in that: The curvature of each guide point is calculated as follows: For each guide point, find the four nearest guide points, and perform data fitting on the guide point and its four nearest guide points to obtain a fitting function; According to the formula Calculate the curvature of the guide point, where K represents the curvature of the guide point and y″ represents the curvature of the fitting function at x. h The second derivative value of the point, y′ represents the fitting function at x h The first derivative value of the point, x h Indicates the X-axis coordinate value of the guide point.

9. The method for evaluating the task completion of the cross-domain landing algorithm for an amphibious unmanned vehicle according to claim 6 is characterized in that: The following formula is used to calculate the average semantic label type ratio of each scene frame: Among them, N F Indicates the number of scene frames, N tagi Indicates the number of label types for the i-th scene frame.

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