Cooperative path planning method and system based on grid space-time conflict detection, and storage medium

Abstract

The invention discloses a cooperative path planning method based on grid space-time conflict detection, and the method comprises the steps: obtaining a grid unit, setting an initial passing state, generating a flight path of an aircraft according to the passing state of a flight airspace grid unit, and carrying out the timestamp marking of the flight path; acquiring a flight path conflict risk grid table, judging whether the space conflict and the time overlap exist between the grid units through which the flight path passes and the stored grid units, if the space conflict and the time overlap are met at the same time, modifying the passing state of the conflict grid units in the airspace grid state, and returning to regenerate a new flight path. And updating the flight route conflict risk grid table. The flight path is planned according to the traffic state of the dynamic grid unit and the conflict risk unit, and the safety of the aircraft is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft path planning, and in particular to a collaborative path planning method, system and storage medium based on grid spatiotemporal conflict detection. Background Art

[0002] In recent years, with the rapid growth of urban populations and the number of vehicles, surface and underground transportation resources have become increasingly strained, and traffic congestion has become increasingly severe, seriously impacting the sustainable development of cities and disrupting the daily lives of residents. To address this challenge, the development of three-dimensional transportation models utilizing urban vertical space has gradually become a key approach to improving transportation efficiency and expanding transportation capacity. With the booming drone industry and the widespread opening of low-altitude airspace, Urban Air Mobility (UAM) has gradually become a focus of public attention, indicating that the future urban transportation system will develop in the direction of aerial transportation, which is becoming an inevitable trend. National and local governments have also introduced a number of policies to actively encourage the development of the unmanned low-altitude manned aircraft industry, providing strong policy support for the rapid growth of this emerging industry.

[0003] As the concept of urban air mobility gradually takes shape, achieving safe and efficient collaborative trajectory planning for aircraft in urban airspace has become a key issue that needs to be addressed. Urban low-altitude multi-aircraft collaborative trajectory planning must not only consider the impact of urban buildings and no-fly zones on trajectory planning, but also the impact of collisions. Traditional conflict risk assessments rely on point-to-point conflict detection calculations, determining whether a conflict exists based on distance. However, as the number of paths increases, the amount of detection calculations continues to increase, resulting in low detection efficiency when dealing with future high-density urban air traffic systems.

[0004] Existing multi-aircraft collaborative trajectory planning mostly uses the A* algorithm, which boasts high planning efficiency. As a heuristic search algorithm, it combines the characteristics of breadth-first search and best-first search to find the shortest path. To address the A* algorithm's lack of smoothness, which increases the number of aircraft turns and wastes resources, several researchers have improved it. Yin Hongyu et al. extended the A* algorithm to a 26-neighborhood, taking into account turning angle constraints and employing a collision avoidance method based on the equation of motion to achieve multi-aircraft collaborative trajectory planning. Du employed an improved A* algorithm, utilizing a wavefront planner, to replan the route for each obstacle change, thus avoiding collisions and achieving multi-aircraft collaborative trajectory planning. Xie Hua, building on A*, used a jumping point search algorithm to achieve three-dimensional path planning. Zhang Honghai, building on A*, set a dynamic step size and performed path planning based on the objective function of minimizing risk, noise, and transportation costs. Yang Guo et al. addressed the A* algorithm's shortcomings, such as insecurity, numerous turning points, and non-smoothness, by improving the neighborhood and extracting necessary turning points to enhance the safety and reliability of the path. Christophe filters the nodes in the A* open list through the P function and the W function, ultimately reducing the number of turns and shortening the path length. However, most of the existing improvements to the A* path planning method use dynamic parameters, which need to be adjusted according to the external environment. The low-altitude airspace environment is complex, and dynamic adjustments increase uncertainty and reduce flight safety. In addition to using the A* algorithm, domestic and foreign scholars also use other algorithms for trajectory planning. Castro proposed an online adaptive path planning scheme based on the fusion of rapid exploration random tree (RRT) and deep reinforcement learning (DRL) algorithms for the generation and control of autonomous trajectories of UAVs

[13] . Phung proposed a new algorithm called spherical vector-based particle swarm optimization (SPSO) to deal with the path planning problem of UAVs responding to multiple threats in complex environments. Dai Jian used the "Z" path coverage method and the Dubins turning path to conduct search path planning for each UAV covering its sub-area, which can quickly divide the target area and generate flight routes.

[0005] While some progress has been made in collaborative trajectory planning both domestically and internationally, existing improvements to the A* path planning method mostly use dynamic parameters that require adjustment based on the external environment. Low-altitude airspace is complex, and dynamic adjustments increase uncertainty and reduce flight safety.

[0006] There is an urgent need for a path planning method that can reduce dependence on dynamic environmental data, improve the flight safety and efficiency of aircraft, and meet the development needs of urban air traffic systems. Summary of the Invention

[0007] In order to overcome the problems in the prior art, the purpose of the present invention is to provide a collaborative path planning method, system and storage medium based on grid spatiotemporal conflict detection, which can reduce the aircraft's dependence on dynamic environmental data during flight and improve the aircraft's flight safety.

[0008] To achieve the above objectives, the present invention provides a collaborative path planning method based on grid spatiotemporal conflict detection, comprising the following steps:

[0009] S1: Set the take-off and landing sites and flight altitude levels for the aircraft's cruise mission;

[0010] S2: Obtain the initial traffic status of the grid cells, generate the flight path of the aircraft based on the traffic status of the flight airspace grid cells, and timestamp the flight path;

[0011] S3: Obtain a flight path conflict risk grid table and determine whether the grid cells passed by the flight path have spatial conflicts and temporal overlaps with the stored grid cells. If both spatial conflicts and temporal overlaps are satisfied, modify the traffic status of the conflicting grid cells and return to step S3 to regenerate a new flight path. If neither spatial conflicts nor temporal overlaps are satisfied, output the flight path S.

[0012] S4: Update the flight path conflict risk grid table according to the timestamp of each grid cell that the flight path S passes through.

[0013] S5: Use B-spline to smooth the flight path S.

[0014] In step S2, the grid cell traffic state includes dividing the flight airspace of the aircraft into layers and altitude layers, generating grid cells with coded identifiers, and setting the initial state of the grid cells; specifically, the following steps are included:

[0015] The flight airspace is divided into levels according to the map resolution, aircraft type and minimum safety interval, and the grid unit side length L is calculated according to the level P , the formula is:

[0016]

[0017] Among them, L P is the side length of the grid unit, P is the grid unit;

[0018] Configure the flight altitude layer Height according to the aircraft type, minimum safety interval and maximum cruising speed of the aircraft;

[0019] The GeoSOT coding system is used to generate unique spatial identifiers for grid cells;

[0020] Initialize the settings based on GPS map data information. The formula is:

[0021]

[0022] Where P is the grid unit.

[0023] Furthermore, in step S2, generating the flight path of the aircraft using the A* path planning algorithm according to the traffic status of the flight airspace grid cells includes the following steps:

[0024] S21: Define the grid cell where the preset landing site is located as the target node, define the grid cell where the take-off site is located as the starting node X0, define the initial open list and closed list as empty; the starting node is used as the current node X0. 当前 Join the open list;

[0025] S22: Judge X 当前 Is it the target node? If so, terminate the iteration and execute step S27; if not, continue to execute step S23;

[0026] S23: Get the current node X 当前 Adjacent nodes X n , determine the adjacent node X n If it is in an impassable state, node X is ignored. n If it is a passable state, then determine the node X n Is it in the closed list? If it is in the closed list, ignore node X. n ; If it is not in the closed list, then the node X n Execute the calculation of step S34;

[0027] S24: Calculate each adjacent node X of the current node X n Cost function f(X); judge each adjacent node X n Is it in the open list? If so, proceed to step S25; if not, move the adjacent node X to the open list. n Add to the open list and set the current node X to X n The parent node of the node is executed in step S26; wherein the cost function f(X) is calculated as follows: f(X) = g(X) + h(X)

[0028] Where g(X) is the cost of moving from the starting node to the node to be calculated; h(X) is the cost of moving from the node to be calculated to the target node;

[0029] S25: Determine whether g(0,n) < g(0, current) + g(current,n). If so, execute step S26. If not, first set the current node X 当前Set to adjacent node X n The parent node of , then execute step S26;

[0030] S26: Change the current node X 当前 Remove the open list, add the closed list, and calculate the X of all nodes in the open list openlist The cost function f(X) is used to select the node with the minimum cost function as the new current node X 当前 , return to step S22;

[0031] S27: Trace back the parent node from the target node to the starting point node to generate a flight path node sequence S.

[0032] Furthermore, in step S3, the method for timestamping the flight path S is specifically as follows:

[0033] Define the start time and end time of the i-th grid unit as [T start (i), T end (i)], the starting time T of the grid unit being occupied start (i) The following settings are available:

[0034]

[0035] Where i is the sequence number of the i-th grid unit in the flight path S, t0 is the start time of the cruise mission; T end (i-1) represents the time when the aircraft left the last grid cell;

[0036] The end time when the aircraft is occupied in the i-th grid cell is set as follows:

[0037]

[0038] Among them, d i is the flight distance of the i-th grid; v is the cruising speed of the aircraft.

[0039] Furthermore, in step S3, the method for determining the spatial conflict is as follows: detecting whether there is a grid cell P' in the flight route conflict risk grid table with the same coordinate position as the grid cell P in the flight path S; if so, there is a spatial conflict between the grid cell P and the stored flight route, and the time overlap detection of the grid cells P and P' is continued; if not, the flight path S is directly output and step S4 is executed.

[0040] Furthermore, in step S3, the method for determining whether the time overlap occurs is:

[0041] Get the time occupied by spatially conflicting grid cells P and P' [T start , T end ] and [T'exist_start , T' exist_end ], determine whether the two time windows satisfy the following formula. If so, there is no time overlap between grid cells P and P'. Otherwise, there is time overlap between grid cells P and P'. The formula is:

[0042]

[0043] Among them, T start_P is the starting time when the flight path S occupies the grid cell P, T end_P is the end time of the flight path occupying grid cell P, T exist_start_P′ is the starting time of the stored flight path occupying grid cell P', T exist_end_P′ The end time of the stored flight path occupying grid cell P'.

[0044] Furthermore, in step S4, updating the flight path conflict risk grid table includes the following steps:

[0045] S41: Obtain the coordinates and timestamp of each grid cell P that the flight route passes through;

[0046] S42: Preset the collision risk safety distance σ of the grid unit P, calculate the collision range of the grid unit P (x, y), the coordinate formula of the upper left corner of the collision range of the grid unit P (x, y) is expressed as: (X-σ, Y+σ), and the coordinate formula of the lower right corner grid unit is expressed as: (X+σ, Y-σ); where σ=R collsion +R risk ; R collsion is the conflict avoidance distance; R risk is the radius distance of the conflict risk area;

[0047] S43: Record the conflict range of the grid unit P as P', assign the timestamp of the grid unit P to the grid unit P', and store the grid unit P' and its timestamp in the flight path conflict risk grid table.

[0048] Furthermore, the step S5 uses B-spline to smooth the flight path S; specifically, the step S5 includes the following steps:

[0049] S51: Get the grid cell path node set {Q j |j=0,1,…m}; where m is the number of grid unit nodes that the flight path S passes through;

[0050] S52: Preset parameter value t for each path node j ;

[0051] S53: Constructing Linear Systems Solve the control point {Di};

[0052] in, ti is the i-th node value in the node vector, t i+k -t i is the span length of the node interval; n is the number of control points, k is the B-spline order, t is the curve parameter, F i,k (t) k-order B-spline basis function corresponding to the i-th control point, D i is the i-th control point;

[0053] S54: Set k-order to generate k-order B-spline curve

[0054] Where C(t) is the point on the B-spline curve corresponding to the parameter t.

[0055] The present invention also provides a collaborative path planning system based on grid spatiotemporal conflict detection, comprising:

[0056] Grid management module, used for airspace grid division, coding and status storage;

[0057] Spatiotemporal conflict detection module, used to calculate grid spatial conflicts and time window overlaps;

[0058] Dynamic path planner for A* path planning algorithm and B-spline smoothing of flight paths;

[0059] The storage module is used to store the flight path conflict risk grid table, the flight path trajectory and its timestamp.

[0060] The present invention also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned collaborative path planning methods based on grid spatiotemporal conflict detection.

[0061] The present invention obtains information about complex terrain and various obstacles within the airspace, divides the airspace using a GeoSOT three-dimensional grid, and marks the traffic status of the grid cells. Furthermore, after the aircraft cruise mission uses the A* path planning algorithm to generate an initial flight path, the A* path planning algorithm is continuously adjusted according to a dynamic flight path conflict risk grid table, combined with the grid cell conflict status and time window overlap determination, until a safe path node is generated. Finally, a B-spline curve is constructed using the path nodes as type value points to generate a flyable trajectory with continuous curvature. By setting the optimal flight path plan before the aircraft cruise mission, the present invention improves the safety of the aircraft's flight and avoids the inconvenience of the aircraft needing to modify the planned route in real time due to dynamic monitoring of the surrounding environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a collaborative path planning method based on grid spatiotemporal conflict detection provided in Example 1 of the present invention; DETAILED DESCRIPTION

[0063] Example 1

[0064] For the first flight path S0, a path planning method based on grid-based spatiotemporal conflict detection includes the following steps:

[0065] S1: Set the take-off and landing sites and flight altitude levels for the aircraft's cruise mission.

[0066] Select the appropriate flight altitude according to the type of aircraft, and set the starting and ending points of the aircraft's cruise route according to the cruise mission.

[0067] S2: Obtain the initial traffic status of the grid cells, use the A* path planning algorithm to generate the flight path of the aircraft based on the traffic status of the flight airspace grid cells, and timestamp the flight path.

[0068] The grid unit traffic state includes dividing the flight airspace of the aircraft into layers and altitude layers, generating grid units with coded identifiers, and setting the initial state of the grid units; specifically, the following:

[0069] The flight airspace of the aircraft is divided into layers and altitude layers, grid cells with coded identifiers are generated, initial traffic status is set for the grid cells, and a flight path conflict risk grid table is preset. Specifically, the following steps are included:

[0070] The flight airspace is divided into levels according to the map resolution, aircraft type and minimum safety interval, and the grid unit side length L is calculated according to the level P , the formula is:

[0071]

[0072] Among them, L P is the side length of the grid unit, and P is the grid unit.

[0073] Configure the flight altitude layer Height based on the aircraft type, minimum safety interval, and maximum cruising speed of the aircraft.

[0074] Specifically, this embodiment divides the low-altitude airspace into three flight altitude layers, as follows:

[0075] Upper layer: suitable for aircraft with higher flight speeds, focusing on flight speed and flight distance;

[0076] Mid-tier: Suitable for aircraft where the flight experience is more important, with a focus on user experience;

[0077] Lower layer: mainly considers the environmental impact of drone flights.

[0078] Based on the minimum safety distance of 93m and considering the convenience of management, the height of each floor is set at 100m.

[0079] The GeoSOT coding system is used to generate unique spatial identifiers for grid cells.

[0080] In this embodiment, the grid cells are divided using GeoSOT (Geographic Coordinate Subdividing Grid with One Dimension Integral Coding on 2n-tree). GeoSOT is a global latitude and longitude subdivision grid based on a one-dimensional array of 2n integers. It can rationally divide the Earth's surface space and construct a specialized grid system for efficient remote sensing data organization and management. Those skilled in the art are capable of setting appropriate levels and altitude layers for the grid system based on factors such as aircraft type, minimum safe separation, and maximum cruising speed.

[0081] According to the GPS map information, mark the GeoSOT grid cell code of the obstacle to ensure that the flight path avoids obstacles and no-fly zones, and initialize the grid cell traffic status:

[0082]

[0083] Where P is the grid unit.

[0084] Furthermore, the time state of the grid unit is initialized, and the entry time and exit time of the grid unit are both set to 0.

[0085] The grid information is stored in the original airspace grid table, and the flight route conflict risk grid table and flight route table are initialized.

[0086] Generating the flight path of the aircraft using the A* path planning algorithm according to the traffic status of the flight airspace grid cells includes the following steps:

[0087] S21: Define the grid cell where the preset landing site is located as the target node, define the grid cell where the take-off site is located as the starting node X0, define the initial open list and closed list as empty; the starting node is used as the current node X0. 当前 Join the open list;

[0088] S22: Judge X 当前 Is it the target node? If so, terminate the iteration and execute step S37; if not, continue to execute step S23;

[0089] S23: Get the current node X 当前 Adjacent nodes X n , determine the adjacent node X n If it is in an impassable state, node X is ignored. n If it is a passable state, then determine the node X n Is it in the closed list? If it is in the closed list, ignore node X n ; If it is not in the closed list, then the node X n Execute the calculation of step S24;

[0090] S24: Calculate each adjacent node X of the current node X n Cost function f(X); judge each adjacent node X n Is it in the open list? If so, proceed to step S25; if not, move the adjacent node X to the open list. n Add to the open list and set the current node X to X n The parent node of , execute step S26; wherein, the calculation formula of the cost function f(X) is:

[0091] f(X)=g(X)+h(X)

[0092] Where g(X) is the cost of moving from the starting node to the node to be calculated; h(X) is the cost of moving from the node to be calculated to the target node;

[0093] S25: Determine whether g(0,n) < g(0, current) + g(current,n). If so, execute step S26. If not, first set the current node X 当前 Set to adjacent node X n The parent node of , then execute step S26;

[0094] S26: Change the current node X 当前 Remove the open list, add the closed list, and calculate the X of all nodes in the open list openlist The cost function f(X) is used to select the node with the minimum cost function as the new current node X 当前 , return to step S22;

[0095] S27: Trace back the parent node from the target node to the starting point node to generate a flight path node sequence S.

[0096] The time stamping of the flight path in step S2 specifically includes the following steps:

[0097] Define the start time and end time of the occupation of the i-th grid unit as [T start (i), T end(i)], the starting time T of the grid unit being occupied start (i) The following settings are available:

[0098]

[0099] Where i is the sequence number of the i-th grid unit in the flight path, t0 is the start time of the cruise mission; T end (i-1) represents the time of leaving the previous grid cell.

[0100] The end time when the aircraft is occupied in the i-th grid cell is set as follows:

[0101]

[0102] Among them, d i is the flight distance of the i-th grid; v is the cruising speed of the aircraft.

[0103] S3: Based on the flight path conflict risk grid table, determine whether the grid cells passed by the flight path have spatial conflicts and temporal overlaps with the stored grid cells. If both spatial conflicts and temporal overlaps are satisfied, modify the traffic status of the conflicting grid cells and return to step S3 to regenerate a new flight path. If both spatial conflicts and temporal overlaps are not satisfied, output the flight path S.

[0104] Specifically, the flight route conflict risk grid table can be set to periodically clear all data. For example, if the flight route conflict risk grid table is set to be cleared daily, the flight route conflict risk grid table should be in an empty table state. Then, for the first flight path S0 set for the first flight on the second day, there will be no spatial conflict and time overlap in the grid cells passed by the first flight path S0, and the first flight path S0 can be directly output.

[0105] For the flight path S of the αth item α , the flight path conflict risk grid table is not empty, then the αth flight path S α Perform spatial conflict and temporal overlap detection.

[0106] First, the spatial conflict detection method is to detect whether there is a grid cell P' in the flight path conflict risk grid table and the flight path S α The position of the grid cell P in is the same as that of the stored grid cell P. If so, there is a spatial conflict between the grid cell P and the stored grid cell P', and the temporal overlap detection of the grid cells P and P is continued.

[0107] For the grid cells P and P' that have spatial conflicts, continue to perform time overlap detection to obtain the time [T start , T end] and [T' exist_start , T' exist_end ], the time overlap detection method is:

[0108] There are four situations in which time overlap occurs:

[0109]

[0110] Determine whether the time occupied by grid cells P and P' satisfies the following formula. If so, there is no time overlap between grid cells P and P'. If not, there is time overlap between grid cells P and P'. The formula is:

[0111]

[0112] Among them, T start_P is the flight path S α The start time of occupying grid cell P, T end_P is the flight path S α End time of occupying grid cell P, T exist_start_P′ is the starting time of the stored flight path occupying grid cell P', T exist_end_P′ is the end time of the stored flight path occupying grid cell P', Is an empty set.

[0113] If both spatial conflict and time overlap are satisfied, the passable state of the grid unit P is set to an inaccessible state, and the process returns to step S2 to generate a new flight path S. α , perform spatial conflict and time overlap detection again until a flight path S is generated that does not have spatial conflict and time overlap detection. α .

[0114] S4: According to the flight path S α The flight path conflict risk grid table is updated with the timestamp of each grid cell passed through, specifically including the following steps:

[0115] S41: Get flight route S α Each grid cell P passed through and its timestamp.

[0116] S42: Preset the collision risk safety distance σ of the grid unit P, calculate the collision range of the grid unit P (x, y), the coordinate formula of the upper left corner of the collision range of the grid unit P (x, y) is expressed as: (X-σ, Y+σ), and the coordinate formula of the lower right corner grid unit is expressed as (X+σ, Y-σ); where σ=R collsion +R risk ; R collsion is the conflict avoidance distance; R risk is the radius of the conflict risk area.

[0117] S43: Record the conflict range of the grid unit P as P', assign the timestamp of the grid unit P to P', and store it in the flight path conflict risk grid table.

[0118] Furthermore, the flight route S is stored in a flight route table. The flight route table may be a daily flight route table, and all flight paths in the flight route table are cleared every day.

[0119] S5: Use B-spline to calculate the flight path S α The smoothing process includes the following steps:

[0120] The flight path S generated by the A* path planning algorithm α It is composed of discrete grid nodes with jagged transitions, which does not comply with the dynamic constraints of the aircraft.

[0121] S51: Flight path S generated from the A* path planning algorithm α Get the grid cell path node set {Q j |j=0,1,…,m}.

[0122] m is the flight path S α The number of grid cell nodes passed through.

[0123] S52: Preset parameter value t for each path node j , where t is the curve parameter.

[0124] S53: Constructing Linear Systems Solve the control point {D i}.

[0125] in, ti is the i-th node value in the node vector, t i+k -t i is the span length of the node interval, D i is the i-th control point;

[0126] S54: Generate k-degree B-spline curve C(t) is the point on the B-spline curve corresponding to parameter t, F i,k (t) The k-order B-spline basis function corresponding to the i-th control point, t is the curve parameter, n is the number of control points, and k is the B-spline order.

[0127] In this embodiment, k is selected as 3, that is, a cubic B-spline curve is used, and the control point P is substituted into the curve equation to obtain the cubic B-spline curve equation expressed as:

[0128] C(t)=D0*F 0,3 (t)+D1*F 1,3(t)+D2*F 2,3 (t)+D3*F 3,3 (t);

[0129] The path nodes are interpolated through cubic B-spline curves to generate a smooth trajectory with continuous curvature, eliminating sharp turns and meeting the minimum turning radius requirement of the aircraft.

[0130] The present invention obtains information about complex terrain and various obstacles within the airspace, divides the airspace using a GeoSOT three-dimensional grid, and marks the traffic status of the grid cells. Furthermore, after the aircraft cruise mission uses the A* path planning algorithm to generate an initial flight path, the A* path planning algorithm is continuously adjusted according to a dynamic flight path conflict risk grid table, combined with the grid cell conflict status and time window overlap determination, until a safe path node is generated. Finally, a B-spline curve is constructed using the path nodes as type value points to generate a flyable trajectory with continuous curvature. By setting the optimal flight path plan before the aircraft cruise mission, the present invention improves the safety of the aircraft's flight and avoids the inconvenience of the aircraft needing to modify the planned route in real time due to dynamic monitoring of the surrounding environment.

[0131] Example 2:

[0132] The present invention also provides a collaborative path planning system based on grid spatiotemporal conflict detection, which is used to implement the collaborative path planning method based on grid spatiotemporal conflict detection as described in the above embodiment 1, including:

[0133] Grid management module, used for airspace grid division, coding and status storage;

[0134] Spatiotemporal conflict detection module, used to calculate grid spatial conflicts and time window overlaps;

[0135] Dynamic path planner for A* path planning algorithm and B-spline smoothing of flight paths;

[0136] The storage module is used to store the flight path conflict risk grid table, the flight path trajectory and its timestamp.

[0137] Example 3:

[0138] The present application also discloses a computer-readable storage medium, which includes a stored computer program. When the computer program is executed, the computer-readable storage medium controls a device to execute the collaborative path planning method based on grid-based spatiotemporal conflict detection according to the first embodiment. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a ROM, an erasable programmable read-only memory, a hard disk, a CD-ROM, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer-readable storage medium known in the art.

[0139] The three embodiments described above are only preferred specific implementation methods of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A collaborative path planning method based on grid spatiotemporal conflict detection, characterized in that: The following steps are involved: S1: Set the take-off and landing sites and flight altitude levels for the aircraft's cruise mission; S2: Obtain the initial traffic status of the grid cells, generate the flight path of the aircraft based on the traffic status of the flight airspace grid cells, and timestamp the flight path; S3: Obtain a flight path conflict risk grid table and determine whether the grid cells passed by the flight path have spatial conflicts and temporal overlaps with the stored grid cells. If both spatial conflicts and temporal overlaps are satisfied, modify the traffic status of the conflicting grid cells and return to step S3 to regenerate a new flight path. If neither spatial conflicts nor temporal overlaps are satisfied, output the flight path S. S4: Update the flight path conflict risk grid table according to the timestamp of each grid cell that the flight path S passes through; S5: Use B-spline to smooth the flight path S.

2. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: In step S2, the grid unit traffic state includes dividing the flight airspace of the aircraft into layers and altitude layers, generating grid units with coded identifiers, and setting the initial state of the grid units; specifically, include the following: The flight airspace is divided into levels according to the map resolution, aircraft type and minimum safety interval, and the grid unit side length L is calculated according to the level P , the formula is: Among them, L P is the side length of the grid unit, P is the grid unit; Configure the flight altitude layer Height according to the aircraft type, minimum safety interval and maximum cruising speed of the aircraft; The GeoSOT coding system is used to generate unique spatial identifiers for grid cells; Initialize the settings based on GPS map data information. The formula is: Where P is the grid unit.

3. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: In step S2, the flight path of the aircraft is generated using the A* path planning algorithm according to the traffic status of the flight airspace grid cells, including the following steps: S21: Define the grid cell where the preset landing site is located as the target node, define the grid cell where the take-off site is located as the starting node X0, define the initial open list and closed list as empty; the starting node is used as the current node X0. 当前 Join the open list; S22: Judge X 当前 Is it the target node? If so, terminate the iteration and execute step S27; if not, continue to execute step S23; S23: Get the current node X 当前 Adjacent nodes X n , determine the adjacent node X n If it is in an impassable state, node X is ignored. n If it is a passable state, then determine the node X n Is it in the closed list? If it is in the closed list, ignore node X n ; If it is not in the closed list, then the node X n Execute the calculation of step S24; S24: Calculate the current node X 当前 Each adjacent node X n Cost function f(X); judge each adjacent node X n Is it in the open list? If so, proceed to step S25; if not, move the adjacent node X to the open list. n Add to the open list and set the current node X 当前 Set to X n The parent node of the node is executed in step S26; wherein the cost function f(X) is calculated as follows: f(X) = g(X) + h(X) Where g(X) is the cost of moving from the starting node to the node to be calculated; h(X) is the cost of moving from the node to be calculated to the target node; S25: Determine whether g(0,n) < g(0, current) + g(current,n). If so, execute step S26. If not, first set the current node X 当前 Set to adjacent node X n The parent node of , then execute step S26; S26: Change the current node X 当前 Remove the open list, add the closed list, and calculate the X of all nodes in the open list openlist The cost function f(X) is used to select the node with the minimum cost function as the new current node X 当前 , return to step S22; S27: Trace back the parent node from the target node to the starting point node to generate a flight path node sequence.

4. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: In step S2, the method for timestamping the flight path is specifically as follows: Define the start time and end time of the i-th grid unit as [T start (i), T end (i)], the starting time T of the grid unit being occupied start (i) The following settings are available: Where i is the sequence number of the i-th grid unit in the flight path, t0 is the start time of the cruise mission; T end (i-1) indicates the time when the aircraft left the last grid cell; The end time when the aircraft is occupied in the i-th grid cell is set as follows: Among them, d i is the flight distance of the i-th grid; v is the cruising speed of the aircraft.

5. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: In step S3, the method for determining the spatial conflict is as follows: detecting whether there is a grid cell P' in the flight route conflict risk grid table with the same coordinate position as the grid cell P in the flight path S; if so, there is a spatial conflict between the grid cell P and the stored flight route, and the time overlap detection of the grid cells P and P' is continued; if not, the flight path S is directly output and step S4 is executed.

6. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 5, characterized in that: In step S3, the time overlap detection method is: Get the time occupied by spatially conflicting grid cells P and P' [T start , T end ] and [T' exist_start , T' exist_end ], determine whether the two time windows satisfy the following formula. If so, there is no time overlap between grid cells P and P'. If not, there is time overlap between grid cells P and P'. The formula is: Among them, T start_P is the starting time of the flight path occupying grid cell P, T end_P is the end time of the flight path occupying grid cell P, T exist_start_P′ is the starting time of the stored flight path occupying grid cell P', T exist_end_P′ The end time of the stored flight path occupying grid cell P'.

7. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: In step S4, updating the flight path conflict risk grid table includes the following steps: S41: Obtain the coordinates and timestamp of each grid cell P that the flight route S passes through; S42: Preset the collision risk safety distance σ of the grid unit P, calculate the collision range of the grid unit P (x, y), the coordinate formula of the upper left corner of the collision range of the grid unit P (x, y) is expressed as: (X-σ, Y+σ), and the coordinate formula of the lower right corner grid unit is expressed as: (X+σ, Y-σ); where σ=R collsion +R risk ; R collsion is the conflict avoidance distance; R risk is the radius distance of the conflict risk area; S43: Record the conflict range of the grid unit P as P', assign the timestamp of the grid unit P to the grid unit P', and store the grid unit P' and its timestamp in the flight path conflict risk grid table.

8. The collaborative path planning method based on grid spatiotemporal conflict detection according to claim 1, characterized in that: The step S5 specifically includes the following steps: S51: Get the grid cell path node set {Q j |j=0,1,...m}; where m is the number of grid unit nodes that the flight path S passes through; S52: Preset parameter value t for each path node j ; S53: Constructing Linear Systems Solve the control point {D i }; in, ti is the i-th node value in the node vector, t i+k -t i is the span length of the node interval; n is the number of control points, k is the B-spline order, t is the curve parameter, F i,k (t) k-order B-spline basis function corresponding to the i-th control point, D i is the i-th control point; S54: Set k-order to generate k-order B-spline curve Where C(t) is the point on the B-spline curve corresponding to the parameter t.

9. A collaborative path planning system based on grid spatiotemporal conflict detection, for implementing a collaborative path planning method based on grid spatiotemporal conflict detection as described in any one of claims 1 to 8, characterized in that: include: Grid management module, used for airspace grid division, coding and status storage; Spatiotemporal conflict detection module, used to calculate grid spatial conflicts and time window overlaps; Dynamic path planning module, used for A* path planning algorithm and B-spline smoothing flight path; The storage module is used to store the flight path conflict risk grid table, the flight path trajectory and its timestamp.

10. A computer-readable storage medium, characterized in that Used to store a computer program, when the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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