Unmanned aerial vehicle collision detection and obstacle avoidance method based on space-time capsule and building envelope

Abstract

The invention discloses a collision detection and obstacle avoidance method based on a building envelope and a space-time capsule. The method comprises the following steps: building a building envelope for a building in a target area; constructing a space-time capsule for the unmanned aerial vehicle flying in the target area, wherein the size of the space-time capsule is related to the type of the unmanned aerial vehicle, the horizontal dynamic interval and the vertical dynamic interval; performing airspace intersection detection based on the space-time capsule and the building envelope, and executing a flight plan if an intersection is not generated; and triggering collision detection when the unmanned aerial vehicle encounters an emergency situation in the flying process, and when a collision risk is detected, determining an avoidance party in real time and calculating an actual avoidance distance. According to the method, the flight safety of the unmanned aerial vehicle can be effectively guaranteed with low calculation amount and low cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-altitude intelligent route planning, and in particular relates to a collision detection and obstacle avoidance method based on building envelopes and space-time capsules. Background Art

[0002] In the field of drone technology, invention patent application publication number CN115761135A discloses a method for digitally modeling drones for drone traffic control. This method constructs space-time capsules for drones based on their models, enabling safe and efficient operation of large drone flows within a limited airspace, facilitating drone management. However, this method fails to consider the need for drones to maintain safe spacing between buildings during low-altitude urban operations, significantly limiting the patent's scope of application.

[0003] Under existing technology, when drones encounter buildings, they can only simply avoid them as obstacles. Patent application publication number CN116880541A discloses an adaptive conflict resolution method for drones in urban scenarios. This method constructs a minimum envelope for buildings, creating a cylindrical collision zone with distinct regularity. However, considering only the minimum envelope is insufficient, as current drone control technology is immature. Meter-level position deviations are common during actual flight. The theoretically calculated minimum envelope cannot guarantee flight safety, and a larger safety distance must be reserved based on measured data.

[0004] The invention patent application with publication number CN 119200640 A discloses a method for dynamic trajectory planning and collaborative obstacle avoidance of multiple UAVs based on reinforcement learning. In order to solve the problems of wasting airspace resources and low system operation efficiency without refined spatial allocation in the context of long-term high-density fusion flight, the concept of airspace gridding is proposed. However, this method has the following three problems: First, the grid size is a non-standard value, which is obtained by calculation and can change dynamically. This means that the patent only solves the problem of dynamic drones avoiding static obstacles, and cannot solve the problem of dynamic drones avoiding dynamic drones, because the grid sizes of different drone flight control systems are different, and there is no unified standard to calculate; second, the amount of grid calculation is very large, and each flight requires airspace grid division, which is impossible to calculate during real-time flight. The grid is calculated in advance on the ground, which requires extremely high real-time data transmission of flight control system data, and cannot guarantee flight safety. Moreover, the cost of performing grid calculation for each flight is very high and not feasible; third, its obstacle mapping is based on lidar, geographic information system GIS data, and GPS data. Due to the high price of lidar, its application scope is greatly limited and cannot be promoted on a large scale in cities.

[0005] Therefore, it is necessary to propose a low-computation, low-cost and effective UAV collision detection and obstacle avoidance method to ensure the flight safety of UAVs. Summary of the Invention

[0006] To solve the above problems, the present invention discloses a collision detection and obstacle avoidance method based on building envelope and space-time capsule, a computer-readable storage medium and an electronic device.

[0007] The specific technical solutions of the present invention are as follows:

[0008] The first aspect of the present invention discloses a collision detection and obstacle avoidance method based on building envelope and space-time capsule, comprising:

[0009] Constructing a building envelope for the buildings within the target area, wherein the buffer radius of the building envelope is related to a control accuracy coefficient; the control accuracy coefficient is updated once in each observation period;

[0010] A space-time capsule is constructed for drones flying within the target area. The size of the space-time capsule is related to the drone model and the horizontal dynamic interval and vertical dynamic interval. The horizontal dynamic interval is related to the set horizontal basic interval, horizontal observation distance, and control accuracy coefficient of the drones. The vertical dynamic interval is related to the set vertical basic interval, vertical observation distance, and control accuracy coefficient of the drones.

[0011] An airspace intersection check is performed based on the space-time capsule and the building envelope. If no intersection is generated, the flight plan is executed. The airspace intersection check includes checking whether the declared planned drone route and the airspace it occupies intersect with the airspace occupied by the building envelope in the target area or other planned routes and the airspace they occupy in the target area.

[0012] In addition, when an emergency occurs during the flight of the drone, collision detection is triggered. If a collision risk is detected, the avoidance party is determined in real time and the actual avoidance distance is calculated; the collision detection includes the detection of whether a collision will occur between the drone time-space capsule executing the planned route and other drone time-space capsules in the target area, and between the drone time-space capsule executing the planned route and the building envelope in the target area.

[0013] A second aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the collision detection and obstacle avoidance method based on building envelope and space-time capsule described in the first aspect of the present invention are implemented.

[0014] The third aspect of the present invention discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the collision detection and obstacle avoidance method based on building envelope and space-time capsule described in the first aspect of the present invention are implemented.

[0015] The present invention is based on the above technical solution and has the following beneficial effects:

[0016] (1) The present invention adopts a UAV collision detection and obstacle avoidance method based on space-time capsule combined with building envelope, which can leave a sufficient safety distance between the UAV and the building to prevent collision during actual flight, effectively avoiding the collision risk during the UAV flight. Compared with rasterization technology, it greatly reduces the amount of calculation, reduces operating costs, and does not rely on surveying and mapping equipment.

[0017] (2) When the present invention detects the existence of a collision risk, it calculates the avoidance priority based on factors such as the level of the organization to which the UAV belongs, the type of UAV and the type of mission the UAV is performing, which helps to ensure that relevant departments can perform their functions of safety supervision when identifying cooperative / non-cooperative UAVs flying between urban buildings.

[0018] (3) Based on the obtained smart city CIM building white model data, sensitive data is desensitized by adding an envelope to the building. In addition, the buffer zone in the building envelope can also leave enough operating time margin for drone operators.

[0019] (4) The present invention proposes to establish a negative feedback regulation mechanism for dynamically adjusting the building envelope and the minimum interval of the space-time capsule. According to the actual avoidance distance of the UAV in an emergency, the precision control coefficient is periodically optimized to continuously reduce the size of the space-time capsule and the building envelope. In this way, the airspace resources occupied by the UAV during flight are continuously reduced with the development and progress of relevant technologies to support UAV low-altitude operations with larger flow and higher density. In this way, airspace resources are intensively utilized to solve the problem of tight airspace resources in the context of increasingly intensive flight activities.

[0020] Figures in the specification

[0021] Figure 1 This is a schematic diagram of the building envelope effect;

[0022] Figure 2 Schematic diagram of the effect of drone avoidance. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0024] The present invention discloses a collision detection and obstacle avoidance method based on building envelopes and space-time capsule classification and grading. Space-time capsules of different sizes are constructed according to aircraft models, and building envelopes of different shapes and sizes are constructed according to the shapes and sizes of buildings. Collision detection is performed based on the constructed space-time capsules or building envelopes in combination with PostgreSQL's spatial calculation functions, and a reasonable avoidance plan is given when a collision risk is detected.

[0025] To address the need for drones to avoid buildings while navigating urban environments, this invention uses CIM (City Information Modeling) white model data to desensitize sensitive data by adding building envelopes. Furthermore, by combining building envelopes with time capsule technology, a sufficient safety distance is maintained between drones and buildings to prevent collisions during actual flight.

[0026] Among them, in order to solve the problem of airspace resource waste and improve airspace utilization efficiency, the present invention sets space-time capsules of different sizes according to different light, small, medium and large aircraft models, and for the first time proposes a negative feedback adjustment mechanism for dynamically adjusting the building envelope and the minimum interval between space-time capsules. According to the negative feedback mechanism in the actual operation process, the size of the space-time capsule and the building envelope is continuously reduced to intensively utilize airspace resources.

[0027] The collision detection and obstacle avoidance method based on building envelope and space-time capsule classification and grading disclosed in the present invention mainly includes the following steps:

[0028] Step 1: Build time capsules for drones in the target area based on the drone type.

[0029] The Time Capsule is an operational system based on four-dimensional spacetime trajectories. This invention uses it to manage drone flight trajectories and schedule them. By incorporating a scheduling algorithm, it ensures that each drone's flight trajectory does not overlap in time and space, thus avoiding mid-air collisions.

[0030] Different types of drones (light, small, medium, large) usually have different horizontal and vertical intervals that should be maintained. Based on the horizontal and vertical intervals of each type of drone, a time capsule that matches the drone is constructed.

[0031] According to the "Civil Unmanned Aerial Vehicle System Classification Management Measures", drones are divided into five categories according to weight, performance, etc.: micro, light, small, medium, and large. According to the "Interim Regulations on the Flight Management of Unmanned Aerial Vehicles", it is required that drones and drones, and drones and manned aircraft or other obstacles should maintain a minimum safety distance in the horizontal (front and back, left and right) and vertical (up and down) directions. Among them, micro / light / small drones should maintain a horizontal interval of ≥50m and a vertical interval of ≥30m; medium / large drones should maintain a horizontal interval of ≥80m and a vertical interval of ≥60m. The present invention uses this as the basic interval and takes values ​​in three directions (front and back, left and right, up and down), which is the initial size of the drone space-time capsule.

[0032] The data foundation for constructing the space-time capsule comes from relevant data published by the Civil Aviation Administration of China, making it practically feasible. Based on the initially constructed space-time capsule, the present invention also incorporates a safety factor during actual operation. The minimum separation required for real-time drone avoidance is multiplied by the safety factor to determine the actual size of the space-time capsule, thereby determining the drone's space-time capsule. The safety factor is the product of the control accuracy factor and the environmental accuracy factor. This ensures operational safety while allowing the space-time capsule to gradually shrink as the drone's control accuracy improves, thereby reducing airspace resource usage.

[0033] The present invention constructs a space-time capsule based on an ellipsoid model. The space-time capsule expression is as follows:

[0034]

[0035] in,

[0036] Where x, y, and z represent the coordinates of any point on the boundary of the space-time capsule; hdynamic 、v dynamic Respectively represent the horizontal dynamic interval and vertical dynamic interval of the UAV; h base 、v base Respectively represent the horizontal basic interval and vertical basic interval of the UAV, among which, for micro / light / small UAV, h base ≥50m,v base ≥30m, medium / large drone, h base ≥80m,v base ≥60m;h obs 、v obs They represent the horizontal observation distance and vertical observation distance of the UAV respectively; α is the control accuracy coefficient, which ranges from 0.8 to 1.2 and can be obtained through equipment calibration; β is the environmental accuracy coefficient, which ranges from 1.0 to 1.5 and is related to factors such as weather and airspace density, and can be set according to actual conditions; γ is the adjustment rate parameter, which ranges from 0.3 to 0.7 and is obtained from the air traffic control strategy.

[0037] Among them, the horizontal observation distance h obs is the minimum horizontal avoidance distance actually observed within the preset observation time window; the vertical observation distance v obs The minimum vertical avoidance distance actually observed within the preset observation time window. The parameter of the observation time window can be expressed as t obs , for example, the last 30 minutes. Horizontal observation distance h obs and vertical observation distance v obs It can be collectively referred to as the minimum observation avoidance distance. It can be understood that the present invention is applied to a low-altitude service monitoring platform to obtain the actual flight trajectory data of all drones in the target area.

[0038] The control accuracy coefficient α is typically correlated with the accuracy level of the drone's navigation system. Based on the airspace occupancy optimization strategy, the control accuracy coefficient α can be updated every preset observation period, for example, every six months. During the space-time capsule construction process, the updated control accuracy coefficient is typically used as the current control accuracy coefficient for calculating the minimum real-time avoidance interval.

[0039] It is worth noting that after the flight activity is completed, the present invention can also calculate the actual avoidance distance between drones during the avoidance process in the event of an emergency according to different classifications such as light, small, medium, and large types based on the real-time flight trajectory of each drone. Within a preset observation period (for example, half a year), the minimum observed avoidance distance of all similar drones classified by light, small, medium, and large types is calculated. The ratio of the minimum observed avoidance distance in the current period to the minimum observed avoidance distance in the previous observation period is multiplied by the original control accuracy coefficient (i.e., the control accuracy coefficient of the previous observation period). The result is stored as the updated control accuracy coefficient α, and the system calculation parameters are synchronously updated, with the updated control accuracy coefficient α being used in the calculation.

[0040] The calculation process of the control accuracy coefficient α is as follows:

[0041] A1: Extract the minimum avoidance distance in the current observation period: Count the actual distances (i.e., observed avoidance distances) of all emergency avoidance events of similar drone models, and take the minimum value as the minimum observed avoidance distance Dcurmin;

[0042] A2: Calculate the update coefficient: Read the minimum avoidance distance Dlastmin of the same type of aircraft in the previous observation period;

[0043] A3: Update control accuracy coefficient: α = α*Dcurmin / Dlastmin;

[0044] It is worth noting that the present invention constructs a space-time capsule based on the above-mentioned method. On the one hand, the method is simple and easy to execute, and sufficient time margin is reserved in combination with empirical values ​​to ensure safety; on the other hand, the method also supports the size of the space-time capsule to be reduced with the continuous advancement of drone control technology in the actual operation process, so as to meet the current demand for refined use of airspace due to the growing prosperity of low-altitude flight.

[0045] Step 2: Construct a building envelope for the buildings in the target area. The size of the building envelope is related to the building attribute classification and size.

[0046] Building white model data based on CIM (City Information Modeling) (referred to as "CIM white model data") is the foundational 3D model data for digital and intelligent urban management. Its core features are lightweight geometric representation and spatial topology. This model represents the building's macroscopic form, spatial location, and height information through simplified building block outlines (usually at the Levels of Detail (LOD) 1 and 2), excluding detailed components and material textures.

[0047] Based on acquired CIM white model data, this method constructs building envelopes for each building within the target area, based on the shape characteristics of the buildings in the CIM white model data and combining building attribute classification. The envelopes are then expanded outwards along the X, Y, and Z directions to a specified distance according to the required spacing. The constructed building envelope data can be stored in a PostgreSQL database for easy access during route planning or real-time monitoring.

[0048] In GIS (Geographic Information Systems) or spatial analysis, buffer generation involves multiple parameter settings, primarily including the buffer radius, number of segments, and join style. The buffer radius primarily defines the minimum distance between the buffer boundary and the building outline, that is, the distance the buffer extends outward from the building outline. The number of segments (num_segments) primarily controls the smoothness of the buffer boundary and typically defaults to 8, which approximates a rounded corner with 8 segments. The join style refers to how the buffer boundary joins at corners, specifically when two line segments at the edge of the buffer meet at a corner. Three common join values ​​are miter, round, and bevel. The default is join = miter, which is a miter join. When join = miter, miter_limit controls the maximum allowable length ratio of the miter join. If the actual ratio exceeds miter_limit (for example, due to a very small angle), the join style is automatically downgraded to bevel.

[0049] The building envelope constructed in this paper refers to an area (referred to as a "buffer zone") formed by extending a certain distance outward from the location, shape, and height of smart city buildings. This area can be used to detect potential collision risks with low-altitude drones, such as drones, during flight. The building envelope is typically a simplified geometric model (such as a cuboid or a Level of Detail (LOD) 1 (LOD) 1 3D model) that represents the spatial footprint, height, and basic form of the building and its buffer zone.

[0050] The present invention can set the number of segments of the buffer to 16, which makes the buffer edge smoother. In the connection style design, when two line segments intersect at an acute angle or an obtuse angle, round will use a circular arc to smoothly transition at the connection point, that is, rounded connection.

[0051] Specifically, the buffer parameters are set as follows:

[0052] The buffer radius r is 50 meters (assuming 1 degree ≈ 111 km, approximately 0.00045 degrees);

[0053] Number of segments: 16 segments;

[0054] Connection style: join=round.

[0055] Based on the above settings, a 50-meter-wide buffer zone with smooth edges and rounded corners will be generated to protect the target building.

[0056] It is understandable that in GIS or spatial analysis, a circular buffer with a fixed point as the center cannot be calculated or simulated. In actual calculations, it is necessary to simulate the circle with an approximate polygon. For example, taking an approximate 8-sided polygon as an example, the point information of a buffer (approximately an 8-sided polygon) with a radius of 1 and a center point (0.0) is calculated as follows:

[0057] Buffer≈{(1,0),(0.707,0.707),(0,1),(-0.707,0.707),(-1,0),

[0058] (-0.707,-0.707),(0,-1),(0.707,-0.707)}

[0059] The resulting building envelope is as follows Figure 1 shown.

[0060] For example, taking the "Oriental Gate" building in Suzhou as an example, the building envelope construction process is as follows:

[0061] Step A1: Simplify the projection of the "Oriental Gate" building on the XY plane into a rectangular polygon, and obtain the expression of the main building on the XY plane based on the coordinates of the four vertices of the rectangle.

[0062] The vertices are the corner points of the building's outer contour. The vertex coordinates of a rectangular building are as follows:

[0063] A (120.715°E, 31.324°N);

[0064] B (120.717°E, 31.324°N);

[0065] C (120.717°E, 31.326°N);

[0066] D(120.715°E,31.326°N).

[0067] Correspondingly, the mathematical representation of the "Oriental Gate" on the XY plane is as follows:

[0068] G={(x,y)|120.715≤x≤120.717,31.324≤y≤31.326}.

[0069] Step A2: Calculate the buffer radius of the buffer zone.

[0070] For polygons, the building envelope can be represented as:

[0071]

[0072] Among them, d(p,G) is the minimum distance from point p on the building's outer contour to the building envelope G, Represents the entire point set in two-dimensional space, and r is the buffer radius.

[0073] The buffer calculation coefficient σ is usually set to 0.05 to 0.2, and the control accuracy coefficient α is usually set to 0.8 to 1.2. For example, if σ = 0.1 is selected as the unit conversion coefficient and α = 1 as the control accuracy coefficient, the final buffer radius is adjusted to:

[0074] r=r init *α*σ=50*1*0.1=5 meters (about 0.00045 degrees)

[0075] Where r init The initial buffer radius.

[0076] It is worth noting that with the continuous advancement of drone control technology, the control accuracy coefficient α will also be continuously updated due to changes in the minimum observation avoidance distance, so that the buffer radius will continue to shrink as the control accuracy improves.

[0077] Step A3: Construct a building buffer zone based on the obtained projection of the main building on the XY plane.

[0078] The buffer zone is constructed as follows: first, each straight edge of the building is translated outward by a distance r, based on the straight edges of the building. Then, each vertex is connected with a quarter arc, using 16 line segments to approximate each quarter arc. For rectangular buildings, the buffer zone will generate four translated straight edges and arcs at the four corners (each arc consists of 16 segments).

[0079] The specific calculation is as follows:

[0080] Take the AB side of a rectangle as an example:

[0081] Original equation: y = 31.324 (120.715 ≤ x ≤ 120.717)

[0082] Equation after translation: y = 31.324 - 0.00045 = 31.32355

[0083] For point A (corner point) of the rectangle:

[0084] Generate arc points using polar coordinates: θ ranges from 270° to 360°, divided into 16 equal parts. The horizontal and vertical coordinates of the arc points are expressed as follows:

[0085] x=120.715+r*cosθ;

[0086] y=31.324+r*sinθ.

[0087] Repeat the above process until all four sides and four corner points are drawn, resulting in the buffer zone for the rectangular "Oriental Gate" building. As you can see, the resulting building envelope is also roughly rectangular, encompassing the original building and the outward-extending buffer zone. The minimum distance between all boundary points of the building envelope and the building's outer contour is approximately 5 meters. Furthermore, the corners of the building envelope are all smooth arcs approximated by 16 line segments.

[0088] Step 3: When declaring flight activities, check whether the planned routes and airspaces declared by the operating company intersect with the airspace occupied by the building envelope or other planned routes and airspaces in the target area. In other words, use the spatial calculation function ST_Intersection in the PostgreSQL database to detect whether the airspaces intersect.

[0089] It is understandable that both space-time capsules and building envelopes essentially occupy a certain spatial area, and the main difference is the shape. Therefore, the spatial intersection detection can be calculated using the same spatial calculation function.

[0090] PostgreSQL (also known as Postgres) is a powerful open-source object-relational database system. ST_Intersection is a spatial calculation function in the PostgreSQL PostGIS extension that calculates the intersection of two geometries. The ST_Intersection function can be expressed as ST_Intersection(GA,GB), where GA represents the first input geometry and GB represents the second input geometry. The ST_Intersection function returns a geometry representing the intersection of the two input geometries; if the two geometries do not intersect, an empty geometry is returned.

[0091] The return value expression of the ST_Intersection function is as follows:

[0092] ST_Intersection(GA,GB)={x|x∈GA∧x∈GB}.

[0093] Using the spatial calculation functions of the PostgreSQL database, it is possible to check whether the planned drone route (low-altitude operators' flight plans generally must be filed with the relevant authorities at least one day before flight and approved before execution) and airspace declared by the operator intersect with the airspace occupied by the building envelope within the target area and other planned routes and airspace stored in the PostgreSQL database. If so, a new route is replanned, circumventing the edges of the building envelope or other planned routes and airspace, combining the drone's planned route and airspace with the building envelope or other planned routes and airspace that could collide. The airspace occupied by the route is then calculated based on information such as the height and width of the new route. Using the spatial calculation functions of the PostgreSQL database, the above steps are repeated, continuously performing spatial calculations to determine whether the airspace occupied by the newly planned route intersects with the building envelope, until a route is planned that does not conflict with the building envelope or other planned routes and airspace. If not, the original planned route is submitted to the relevant authorities for review.

[0094] Step 4: During the flight, under normal circumstances, the drone strictly follows the flight plan and does not need to perform real-time collision detection. However, in an emergency, the drone will trigger a yaw alarm and perform real-time collision detection between the drone's space-time capsule and other drones' space-time capsules and the envelope of buildings in the nearby airspace. When a collision risk is detected, an avoidance plan is determined, including avoidance priority and actual avoidance distance.

[0095] After flight activity declarations are approved, operators may encounter a series of emergencies (referred to as "emergencies") during the execution of the flight plan, such as extreme weather, equipment anomalies, and unauthorized intrusions. These emergencies can cause the drone to deviate from its planned route, leading to collision risks. Therefore, during real-time drone flight, the system provides real-time capsule-to-capsule and capsule-to-building envelope collision detection. The collision detection calculation logic is the same as in step 3, utilizing spatial calculation functions in the PostgreSQL database.

[0096] When a collision risk is detected, and the drone is still some distance from an actual collision, the drone's central control platform first calculates an avoidance priority based on three factors: the drone's organizational level (e.g., company, government, or military), the drone's type, and the mission category. This priority determines the avoidance party. Based on this priority, the lower-priority party is assigned to avoid the higher-priority party. If the priorities are the same, the central control platform randomly selects the party to avoid. After determining the avoidance party, the avoidance distance to be maintained is determined based on the avoidance party's type. This generates an avoidance plan, including the avoidance priority and distance, and notifies the flight control platform of the drone to be avoided. The flight control platform ultimately executes the avoidance plan, mitigating the collision risk and ensuring the drone's flight safety. When avoiding an obstacle, the drone typically determines the avoidance direction in the order of up, right, left, down, and hovering. This is referred to as the avoidance direction selection principle. For example, if an obstacle is still above an upward avoidance attempt, the drone will avoid to the right.

[0097] The avoidance priority is mainly composed of three core elements. The calculation model of the avoidance priority is as follows:

[0098] P=L×T+M

[0099] Where P is the avoidance priority, L is the organization level base, T is the UAV model coefficient, and M is the weight bonus of the task performed by the UAV.

[0100] Table 1 shows the classification of institutional level base (L)

[0101] Table 2 shows the classification of drone type coefficient (T)

[0102] Model Classification T-value Weight range Example miniature 0.7 <1.5kg DJI Mini Series Lightweight 0.9 1.5-4kg DJI Air series Small 1.1 4-7kg DJI M300RTK Medium 1.3 7-25kg CW-15 Large 1.5 >25kg Wing Loong-2 military drone

[0103] Table 3 shows the classification of task weight bonus (M)

[0104] Task Category M value Typical scenarios Combat Missions 100 Military strike / reconnaissance Emergency Response 80 Disaster Relief / Counter-Terrorism VIP security 70 Leader's Guard Public Safety 60 Police patrol Infrastructure Inspection 40 Power / Oil and Gas Inspection Commercial Operations 20 Logistics / Surveying and Mapping Leisure and Entertainment 10 Personal aerial photography

[0105] Based on Tables 1 to 3, in the gradient design, the difference between each level of the organization level is 10 points; the model coefficient increases by a gradient of 0.2 by weight; and the task weight is 20 points. In the organization level setting, the lowest military unit (provincial military region 80) is still higher than the national government (70). Military drones used for combat missions (+100) have a P value ≥ 217 and will have a higher priority. These fully guarantee the military level. The above method also has a high elastic expansion capability. New organization types can be inserted into the corresponding L value interval, and new drones automatically match the T value according to weight.

[0106] For example, in avoidance decision scenario 1: a collision risk occurs between a theater medium reconnaissance aircraft and a provincial emergency light aircraft. The avoidance priority is calculated as follows:

[0107] P(battle zone) = 90*1.3+100 = 217;

[0108] P(provincial level)=60*0.9+80=134;

[0109] Based on the calculated avoidance priority, provincial drone avoidance decisions can be made.

[0110] In avoidance decision scenario 2, where a collision risk occurs between a large private logistics company and a small county-level inspection team, the avoidance priority is calculated as follows:

[0111] P(private)=20*1.5+20=58;

[0112] P(county level)=40*1.1+40=84;

[0113] Based on the calculated avoidance priority, decisions can be made on how to avoid private drones.

[0114] In avoidance decision scenario 3: two city-level micro-aerial cameras, the avoidance priority is calculated as follows:

[0115] P(city-level A)=50*0.7+10=45;

[0116] P(city level B)=50*0.7+10=45;

[0117] Based on the calculated avoidance priority, a city-level B drone can be randomly selected for avoidance.

[0118] Based on the above-mentioned avoidance priority calculation model and core element classification method, the present invention has passed the simulation test of the Civil Aviation Administration of China and can meet the principles of 100% priority avoidance for military units, clear hierarchical relationships between government agencies, and fair avoidance for civilian drones.

[0119] After the avoidance party is determined, the safe distance to be maintained can be further determined based on the type of drone to be avoided. The following example illustrates this:

[0120] The avoidance party is determined to be the M300RTK UAV, which has an empty weight of 3.6 kg (excluding batteries) but a maximum takeoff weight of 9 kg, which meets the standards for small UAVs. base is 50m, vertical foundation interval v base The current flight altitude is 120m, the flight speed is 30m / s (108km / h), and the maximum take-off weight is 9kg (considering the most unfavorable situation).

[0121] According to the M300RTK performance manual and typical operating environment, the control accuracy coefficient α = 0.9 (RTK positioning accuracy is high), the environmental complexity coefficient β = 1.1 (120m altitude medium airflow interference), the minimum observation avoidance distance within the last preset observation time window, the horizontal observation distance h obs is 20m, vertical observation distance v obs is 25m.

[0122] According to the avoidance direction selection principle, the M300RTK UAV avoids upwards, so only the vertical avoidance distance needs to be calculated. Specifically, the vertical dynamic interval v of the M300RTK UAV is dynamic The calculation formula is as follows:

[0123] v dynamic =max(v base ,α·β·v obs )=max(30,0.9×1.1×25)=

[0124] max(30, 24.75) = 30m;

[0125] As can be seen, the M300RTK's theoretical minimum avoidance distance, or vertical dynamic separation, is 30 meters, meaning the two drones maintain a minimum vertical separation of 30 meters. This distance is greater than the vertical observation distance of 25 meters, providing ample safety margin and effectively ensuring drone flight safety. Therefore, the real-time calculated dynamic separation can be used as the actual avoidance distance for avoidance maneuvers.

[0126] It should be noted that, in theory, the dynamic interval may be smaller than the minimum observed avoidance distance. In this case, the minimum observed avoidance distance can be directly used as the actual avoidance distance. However, due to the characteristics of the control accuracy coefficient and the environmental complexity coefficient, this phenomenon generally does not occur.

[0127] Against the backdrop of increasingly busy low-altitude flight activities, while large intervals can ensure flight safety, they waste airspace resources. With the development of the low-altitude economy, drone-related control technologies are also constantly improving, and the control accuracy of drones is constantly improving. The safe interval distance can be continuously reduced to a reasonable range to further improve the utilization efficiency of airspace resources. Therefore, the present invention further establishes a negative feedback mechanism for minimum interval distance correction. Based on the negative feedback mechanism during actual operation, the size of the space-time capsule and the building envelope is continuously optimized (mainly reduced), and the size is continuously revised downward based on the empirical value during actual operation, in order to occupy less airspace resources, move towards the goal of occupying less or even no airspace resources, and promote the rapid development of the low-altitude economy.

[0128] Specifically, after the flight activity ends, based on the real-time flight trajectory and real-time point data of the drones (drone spatiotemporal information obtained by positioning systems such as GPS / Beidou, including latitude and longitude, altitude, speed, heading, and timestamp, etc.) collected over a preset observation period (for example, half a year), the actual separation distance between drones (not the distance between capsules) during drone avoidance during emergencies is counted according to the classification of drones into small, medium, and large sizes. The minimum separation distance for each type of drone in the current observation period (referred to as the current minimum observation interval) is obtained. The ratio of the current minimum observation interval to the minimum interval in the previous observation period is used as the latest control accuracy coefficient α and entered into the rule base, providing a basis for updating the minimum distances for horizontal and vertical separation in the rule base.

[0129] This invention effectively monitors and guides all drones flying over cities by detecting collisions between time capsules and building envelopes in real time, effectively reducing the risk of collisions with urban buildings and other drones. Because when a collision risk is detected between a time capsule and a building envelope, there's still a safe distance before a drone actually hits a building, our city-level low-altitude service monitoring platform sends an alert to the low-altitude operator's flight control platform. This gives the operator ample time to maneuver their drones according to the alert, reducing the probability of accidents.

[0130] According to the expanded technical solution, the present invention can effectively eliminate the risk of collision in the pre-planning stage by performing collision detection between the space-time capsule and the building envelope when the low-altitude operator declares the next day's flight plan and submits the release report before the flight. Unlike traditional manual analysis and judgment methods, the present invention relies on intelligent computing power to serve automatic detection, greatly improving the reporting efficiency, reducing the supervision cost, and eliminating all risks before takeoff, thereby maximizing the safety of drone takeoff.

[0131] This invention can be applied in city-level low-altitude surveillance service systems, coordinating with the system's 5G-A, ADS-B, RemoteID, shelters, and other radar, optoelectronic or electromagnetic spectrum equipment, as well as drone track information reported in real time by flight control platforms, to maintain safe intervals. The relevant equipment will detect the track information, perform multi-source data fusion via the IoT platform, and then connect it to the application server. The application server, combined with the real-time track information reported by each flight control platform, calculates the space-time capsule and building envelopes based on an algorithm. This server performs real-time monitoring and alerts, while also pushing the information to the graphics rendering server of the digital twin computing service center for display on the digital twin map.

[0132] Finally, it should be noted that although the embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and not restrictive. Under the guidance of this specification, those skilled in the art can also make many forms without departing from the scope of protection of the claims of the present invention, and all of these forms are protected by the present invention.

Claims

1. A collision detection and obstacle avoidance method based on building envelope and space-time capsule, characterized in that: include: Constructing a building envelope for buildings within the target area, wherein a buffer radius of the building envelope is related to a control accuracy coefficient; The control accuracy coefficient is updated once in each observation period; A space-time capsule is constructed for drones flying within the target area. The size of the space-time capsule is related to the drone model and the horizontal dynamic interval and vertical dynamic interval. The horizontal dynamic interval is related to the set horizontal basic interval, horizontal observation distance, and control accuracy coefficient of the drones. The vertical dynamic interval is related to the set vertical basic interval, vertical observation distance, and control accuracy coefficient of the drones. An airspace intersection check is performed based on the space-time capsule and the building envelope. If no intersection is generated, the flight plan is executed. The airspace intersection check includes checking whether the declared planned drone route and the airspace it occupies intersect with the airspace occupied by the building envelope in the target area or other planned routes and the airspace they occupy in the target area. In addition, when an emergency occurs during the flight of the drone, collision detection is triggered. If a collision risk is detected, the avoidance party is determined in real time and the actual avoidance distance is calculated; the collision detection includes the detection of whether a collision will occur between the drone time-space capsule executing the planned route and other drone time-space capsules in the target area, and between the drone time-space capsule executing the planned route and the building envelope in the target area.

2. The collision detection and obstacle avoidance method according to claim 1, wherein: The building envelope is specifically expressed as: Where d(p,G) is the minimum distance from point p on the building's outer contour to the building's envelope G. Represents the set of all points in two-dimensional space, r is the buffer radius; The calculation formula of the buffer radius r is: r=r init *a*s Where r init is the initially set buffer radius, σ is the set buffer calculation coefficient, and α is the control accuracy coefficient.

3. The collision detection and obstacle avoidance method according to claim 1, wherein: The updated control accuracy coefficient is the product of the ratio of the minimum observed avoidance distance in the current observation period to the minimum observed avoidance distance in the previous observation period and the current control accuracy coefficient.

4. The collision detection and obstacle avoidance method according to claim 1, wherein: The space-time capsule is represented by an ellipsoid model, which is specifically represented as follows: in, Where x, y, and z represent the coordinates of any point on the boundary of the space-time capsule; h dynamic Indicates the horizontal dynamic interval, v dynamic Indicates vertical dynamic interval; h base Indicates the horizontal base interval, v base Indicates the vertical base interval; h obs Indicates the horizontal observation distance; v obs represents the vertical observation distance; α represents the control accuracy coefficient; β represents the environmental complexity coefficient; The horizontal observation distance h obs and vertical observation distance v obs They are respectively the minimum horizontal avoidance distance and the minimum vertical avoidance distance actually observed within the preset observation time window.

5. The collision detection and obstacle avoidance method according to claim 1, wherein: When a collision risk is detected, the avoidance party is determined in real time and the actual avoidance distance is calculated; specifically, the following steps are involved: Calculating an avoidance priority and using the party with the lower priority as the avoiding party, wherein the avoidance priority is related to at least three factors: the level of the organization to which the UAV belongs, the type of UAV, and the type of mission the UAV is performing; Based on the avoidance direction selection principle, the dynamic interval in the horizontal or vertical direction is calculated in real time; The dynamic interval is compared with the minimum observed avoidance distance in the previous preset observation time window. If the dynamic interval is greater than or equal to the minimum observed avoidance distance, the dynamic interval is used as the actual avoidance distance; otherwise, the minimum observed avoidance distance is used as the actual avoidance distance.

6. The collision detection and obstacle avoidance method according to claim 5, wherein: The calculation formula of the avoidance priority is: P=L×T+M Where P is the avoidance priority, L is the building's organizational level base, T is the UAV model coefficient, and M is the mission weight bonus performed by the UAV. The organizational level base, UAV model coefficient, and mission weight bonus all adopt a gradient design. The party with the smaller P value is determined to be the avoiding party; If the P values ​​of the two are equal, one of them is randomly selected as the avoiding party.

7. The collision detection and obstacle avoidance method according to claim 1, wherein: The spatial calculation function ST_Intersection of the PostgreSQL database is used to perform spatial intersection detection and collision detection.

8. The collision detection and obstacle avoidance method according to claim 1, wherein: The observation period is 6 months.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the collision detection and obstacle avoidance method based on building envelope and space-time capsule described in any one of claims 1 to 8 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the collision detection and obstacle avoidance method based on building envelope and space-time capsule described in any one of claims 1 to 8 are implemented.

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