Method and system for rapidly planning hot-spot-free track of unmanned aerial vehicle in low-altitude airspace

Abstract

The invention relates to the technical field of air traffic management, in particular to a low-altitude airspace unmanned aerial vehicle hot-spot-free flight path rapid planning method and system, and the method comprises the steps: receiving a hot-spot-free flight path planning request, and determining the constraint condition of hot-spot-free flight path planning based on the hot-spot-free flight path planning request; based on the starting position, the ending position and the constraint condition, generating a directed graph representing the flight path of the unmanned aerial vehicle in a space-time grid constructed by the eight-in-and-out-point grid airspace model; the edges of the directed graph are endowed with weights based on the airspace use cost and the energy consumption cost, a weighted directed graph is generated, the weighted directed graph is searched based on the airspace use cost and the energy consumption cost, and an optimal path in the weighted directed graph is obtained and serves as a hot-spot-free track; according to the method, the search complexity can be reduced, the execution efficiency is improved, and the airspace use overhead is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of air traffic management, and in particular to a method and system for quickly planning a hotspot-free track for an unmanned aerial vehicle in low-altitude airspace. Background Art

[0002] For drone navigation in low-altitude airspace, if the demand for drone entry in a time window of an airspace unit exceeds its capacity, the time window of the airspace unit is called a hotspot. The drone flight conflict detection system for low-altitude airspace obtains the occupancy of the time windows of all airspace units in the airspace through the Common Information Service Provider (CISP), and plans a hotspot-free trajectory for the drone based on whether it is a hotspot, thereby improving the safety and efficiency of drone operations in low-altitude airspace.

[0003] The hotspot-free trajectories currently developed are usually for air traffic management of traditional commercial transport aircraft such as Boeing 737, and there are few hotspot-free trajectories specifically for air traffic management of drones. In traditional air traffic management for commercial transport aircraft, due to the large flight interval setting, the airspace traffic density is usually small. When eliminating hotspots, one of the two strategies of delay waiting procedure or rerouting is usually adopted. It is not necessary to mix the two strategies to greatly increase the search space, because doing so will significantly increase the complexity of the problem and thus reduce the solution speed. However, in the low-altitude airspace environment for drones, it is necessary to provide a hotspot-free trajectory planning method based on the flight characteristics of drones with small size and small safety interval, which makes the traffic density in low-altitude airspace higher.

[0004] In traditional air traffic management for commercial transport aircraft, most flights are usually scheduled flights, so hotspot-free trajectory planning can be carried out a day or even a week in advance, so it is not sensitive to calculations. However, in the low-altitude UAV operation environment, there are a large number of UAVs performing temporary tasks, such as food delivery, rescue, etc., which need to be able to quickly obtain trajectories and perform flight missions after receiving flight missions. Therefore, the hotspot-free trajectory planning method for UAVs requires a very fast calculation speed. The current hotspot-free trajectory planning method does not take into account the usage overhead of airspace units, that is, the service overhead of UTMSP. Summary of the invention

[0005] In view of the above problems, the present invention provides a method and system for rapid planning of UAV trajectories without hotspots in low-altitude airspace, which solves the technical problems of high search complexity, limited execution efficiency and failure to consider usage overhead in low-altitude UAV trajectory planning in the prior art.

[0006] On the one hand, the present invention provides a method for quickly planning a UAV non-hotspot trajectory in low-altitude airspace, comprising the following steps:

[0007] Step S1, receiving a hotspot-free trajectory planning request, and determining constraints for the hotspot-free trajectory planning based on the hotspot-free trajectory planning request; the hotspot-free trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed;

[0008] Step S2: Based on the starting position, the ending position and the constraint conditions, a directed graph representing the flight path of the UAV is generated in the space-time grid constructed by the eight-entry and exit point grid airspace model;

[0009] Step S3, assigning weights to the edges of the directed graph based on the airspace usage cost and the energy consumption cost to generate a weighted directed graph;

[0010] Step S4: searching the weighted directed graph based on the airspace use cost and the energy consumption cost to obtain the optimal path in the weighted directed graph as a hotspot-free track.

[0011] Preferably, in step S1, the constraints of the hotspot-free trajectory planning include:

[0012] 1) Hotspot constraint: The constraint condition is that the track cannot pass through a hotspot or cause a hotspot to be generated. The hotspot indicates the location where the UAV capacity has reached the upper limit;

[0013] 2) Energy constraint: The generated trajectory requires the UAV to have enough energy to reach the destination;

[0014] 3) Arrival time constraint: the generated trajectory requires the drone to arrive at the end location before the latest acceptable arrival time;

[0015] 4) Direction constraint: The constraint condition is: the drone is only allowed to fly in the direction close to the destination.

[0016] Preferably, the eight-entry and exit point grid airspace model in step S2 divides the airspace into units in the following manner: the airspace is divided into square grids arranged in a matrix on a plane, and a non-overlapping entry point and an exit point are set on each side of each grid as nodes through which the UAV flies; each grid has multiple time windows, and a time window of a grid is called a space-time grid; the airspace represents the aerial area where low-altitude UAVs navigate.

[0017] Preferably, the process of generating a directed graph representing the flight path of the drone in step S2 specifically includes:

[0018] 1) Determine the starting node and the ending node corresponding to the grid according to the starting position and the ending position;

[0019] 2) Based on the hot spot constraint, energy constraint, arrival time constraint and direction constraint, a breadth-first search method is used to search all nodes of all grids, and multiple branches in a directed graph are generated starting from the starting node;

[0020] 3) Determine whether there is a branch that can reach the end node; when all branches reach the end node or no new branches can be generated, complete the search and obtain the generated directed graph.

[0021] Preferably, in the constraint conditions of the hotspot-free trajectory planning,

[0022] The expression of the hot spot constraint is:

[0023]

[0024] in, represents the probability of UAV i occupying the spatiotemporal grid (m′,n′,k), the spatiotemporal grid (m′,n′,k) represents the kth time window of the grid with the m′th row and n′th column, m′,n′ is the spatial index of the grid that the UAV will enter next, and k is the time window index of the spatiotemporal grid; represents the remaining occupancy probability of the spatiotemporal grid (m′,n′,k) without generating hot spots; represents the set of spatiotemporal grids that drone i can potentially occupy in grid (m′,n′);

[0025] The expression of energy constraint is:

[0026]

[0027] in, Indicates the position coordinates of the nodes marked as φ1, φ2, and φ3 on the grid (m, n). m and n are the spatial indexes of the grid. When the values ​​of (φ1, φ2) are (0, 0), (0, 1), (1, 0), and (1, 1), they indicate that the nodes are on the left, right, bottom, and top of the current grid, respectively. When φ3 is 0, the node is the entrance, and when it is 1, the node is the exit. Indicates the node that the drone currently plans to pass through The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), It represents the potential minimum flight time required for UAV i to reach the end node after flying from grid (m,n) to grid (m′,n′); Indicates the moment when UAV i enters the target airspace, It represents the flight time that the energy capacity of UAV i can sustain in the target airspace;

[0028] The expression of the arrival time constraint is:

[0029]

[0030] in, represents the latest acceptable arrival time of drone i, Determined by the latest acceptable arrival time in the no-hotspot trajectory planning request;

[0031] The expression of the direction constraint is:

[0032]

[0033] in, and are the coordinates of the starting point and end point of UAV i.

[0034] Preferably, step S3 specifically includes:

[0035] Step S3-1, set the weight of the edge of the directed graph to:

[0036]

[0037] Among them, w e represents the weight of edge e, c Usage (e) represents the airspace usage fee of edge e, θ(·) represents the economic cost of calculating the input energy, Indicates the preset required power of drone i;

[0038]

[0039] represents the flight time of UAV i in the space-time grid (m,n,k) when it flies along edge e, u (m,n,k) Represents the unit price of the space-time grid (m,n,k), specifically the price per unit time; K e The index set of the time window occupied by edge e:

[0040]

[0041] Where k is the time index, k′ is the initial value of the first time window, k″ is the initial value of the second time window, and K is the time index range set;

[0042] Step S3-2, determine the virtual target node, and create a virtual edge from each valid end node to the virtual target node, set the weight of the virtual edge to 0, and finally obtain a weighted directed graph, which is represented as

[0043] Preferably, step S4 specifically includes:

[0044] Step S4-1, setting the actual cost of the a-star algorithm, taking the minimum value of the sum of the airspace usage cost and the energy consumption cost on the weighted directed graph as the actual cost;

[0045] Step S4-2, setting the heuristic cost of the a-star algorithm, and taking the sum of the minimum airspace usage cost and the minimum energy consumption cost on the weighted directed graph as the heuristic cost;

[0046] Step S4-3: Based on the actual cost and the heuristic cost, the a-star algorithm is used to search for an optimal path on a weighted directed graph as a hotspot-free track.

[0047] Preferably, step S4 further includes:

[0048] Step S4-4: If the optimal path cannot be found, the adaptive take-off time delay strategy is executed, including: when the departure time When the last acceptable arrival time requirement is met, the departure time is delayed by a time interval ΔT P , and try to regenerate the directed graph; if the requirement of the last acceptable arrival time is not met, it is determined that the hotspot-free track generation has failed, and the flight plan is rescheduled or cancelled;

[0049] The expression of the requirement of the final acceptable arrival time is:

[0050]

[0051] in, is the departure time of drone i, ν Start ,ν End are the starting node and the ending node of UAV i, respectively. min (·,·) indicates the distance between the two input nodes, V i Pre is the preset flight speed.

[0052] Preferably, the actual cost is expressed as:

[0053]

[0054] in, and Respectively represent the number of drone i in the weighted directed graph The actual airspace usage cost and actual energy consumption cost from the starting node to the node ν;

[0055] The expression of the heuristic cost is:

[0056]

[0057] in and They are respectively UAV i in the weighted directed graph Heuristic minimum airspace usage cost and heuristic minimum energy consumption cost on ;

[0058] The expression of the heuristic minimum airspace usage cost is:

[0059]

[0060] in, represents the potential minimum flight time from node ν to the destination node of UAV i; (m v ,n v ) is divided into the grid coordinates of node ν, t v is the time when the UAV arrives at node ν, is the latest possible arrival time of the drone;

[0061] The expression of the heuristic minimum energy cost is:

[0062]

[0063] On the one hand, the present invention provides a low-altitude UAV non-hotspot track rapid planning system, comprising the following modules:

[0064] A determination module, configured to receive a no-hotspot trajectory planning request, and determine a constraint condition of the no-hotspot trajectory planning based on the no-hotspot trajectory planning request; the no-hotspot trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed;

[0065] A directed graph generation module, for generating a directed graph representing the flight path of the UAV in a space-time grid constructed by an eight-entry-exit point grid airspace model based on the starting position, the ending position and the constraint conditions;

[0066] A directed graph weighting module, used for assigning weights to the edges of the directed graph based on airspace usage cost and energy consumption cost to generate a weighted directed graph;

[0067] The search module is used to search the weighted directed graph based on the airspace use cost and the energy consumption cost to obtain the optimal path in the weighted directed graph as a hotspot-free track.

[0068] Compared with the prior art, the present invention has at least the following beneficial effects:

[0069] (1) The present invention is effectively compatible with the currently used gridded low-altitude airspace model, ensuring its applicability in the existing airspace management system. At the same time, the method adopts an adaptive take-off time delay strategy and a path search strategy, which has a larger search space and higher optimization, and adapts to the characteristics of small size, small safety interval and high traffic density of UAVs in low-altitude airspace.

[0070] (2) The method provided by the present invention converts the complex non-hotspot trajectory planning problem into a graph-based path search problem. By combining the ground waiting procedure and the rerouting strategy, the search space is expanded, thereby improving the optimization while ensuring the ability of fast calculation, which can meet the needs of UAVs for fast track acquisition when performing temporary tasks.

[0071] (3) The method provided by the present invention optimizes the UAV trajectory planning by using power consumption and airspace usage fees as the weights of the edges in the graph, so that the generated trajectory minimizes the economic cost. This not only improves the operating efficiency of the UAV, but also reduces the operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0073] Figure 1 The present invention is a flow chart of a method for quickly planning a hotspot-free trajectory of a UAV in low-altitude airspace disclosed in the present invention.

[0074] Figure 2 This is a structural diagram of the hotspot-free trajectory rapid planning system for unmanned aerial vehicles in low-altitude airspace disclosed by the present invention.

[0075] Figure 3 This is a schematic diagram of the interaction of the airspace management device disclosed in the present invention.

[0076] Figure 4 It is a schematic diagram of the eight-in / out point grid airspace model disclosed in the present invention.

[0077] Figure 5 Generate a schematic diagram for the directed graph disclosed in the present invention.

[0078] Figure numerals: 11-UAV, 12-Ground control station, 13-UAV air traffic management service provider, 14-Airspace equipment including public information service provider, 15-No-hotspot trajectory planning, 20-Determination module, 21-Directed graph generation module, 22-Directed graph weighting module, 23-Search module. DETAILED DESCRIPTION

[0079] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0080] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. Figure 1 As shown, a method for fast planning of UAV non-hotspot trajectory in low-altitude airspace is disclosed, and the specific implementation steps are as follows:

[0081] Step S1, receiving a hotspot-free trajectory planning request, and determining constraints for the hotspot-free trajectory planning based on the hotspot-free trajectory planning request; the hotspot-free trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed;

[0082] For low-altitude UAV navigation, the designated air area is divided into units to obtain airspace units, which can also be called grids. The grid model is used to describe the spatial position and driving path of the UAV in each airspace unit.

[0083] like Figure 4 As shown, the method for fast planning of UAV hotspot-free trajectory provided by the present invention is based on an eight-entry and exit point grid airspace model, that is, the airspace is divided into square grids arranged in a matrix on a plane, and the grid with grid coordinates (m, n) represents the grid of the mth row and nth column in the square grid arranged in a matrix, and a non-overlapping entry point and an exit point are set on each side of each grid to serve as the path points passed by the UAV, or called nodes. Its mathematical description is:

[0084]

[0085] in, It is a set of nodes on the grid (m,n), containing a total of 8 path points. Represents one of the eight nodes, which can be distinguished by its subscripts φ1, φ2, and φ3. The values ​​of φ1 and φ2 jointly determine which side of the grid the node is on. When the values ​​of (φ1, φ2) are (0,0), (0,1), (1,0), and (1,1), they indicate that the node is on the left, right, bottom, and top of the grid, respectively. The value of φ3 is used to determine whether the node is the entry or exit point of the grid (m,n), that is, 0 is the entry and 1 is the exit. Ultimately, the values ​​of φ1, φ2, and φ3 can uniquely distinguish which of the eight nodes a specific node corresponds to.

[0086] Use L Unit Indicates the side length of the grid. The inlets and outlets on the common side of two adjacent grids are coincident, that is, the exit and entry points of one grid are exactly the entry and exit points of the other grid.

[0087] Indicates that drone i is in the grid (m,n) starting at node The set of end nodes as candidate edges can be expressed as:

[0088]

[0089] The above expression shows that when the drone enters a grid through an entrance and wants to leave the current grid, there are three optional trajectories.

[0090] Indicates that drone i is in the grid (m,n) When is the starting node, the set of grids that the next track node is about to enter can be expressed as:

[0091]

[0092] Through this expression, the relationship between the grid that the drone currently enters and the next grid is established.

[0093] The present invention generates a hotspot-free trajectory based on the above-mentioned airspace model. The starting point and the end point of the drone's flight in the airspace are respectively used as the starting point and the end point of the directed graph, and the entrance and the exit of each airspace unit in the airspace are used as nodes, and the edges are formed between the two nodes. The alternative trajectories that meet the constraints are used as edges to construct the directed graph. Weights are assigned to the edges in the directed graph according to the costs to be considered. The directed weighted graph is used to generate the optimal hotspot-free trajectory. Figure 4 As shown, a time window of a spatial unit (ie, a grid) is called a space-time grid. The space-time grid is indexed by m, n, and k, indicating that the grid of the mth row and nth column is in the kth time window.

[0094] In some embodiments, the starting point and end point of the UAV's flight in the airspace are determined by a received hotspot-free trajectory planning request, and the hotspot-free trajectory planning request may include information such as the starting position, departure time, end position, latest acceptable arrival time, and preset flight speed.

[0095] In the process of constructing a directed graph of the present invention, the constraints that need to be met are as follows:

[0096] 1) Hotspot constraint: The constraint condition is that the generated edge cannot pass through a hotspot or cause a hotspot to be generated. The hotspot represents a grid where the drone capacity has reached its upper limit.

[0097] For a drone i at the starting node of the grid (m,n) and end node The end node will be located at grid (m′,n′), and At this time, the hot spot constraint is expressed as:

[0098]

[0099] in, represents the probability of UAV i occupying the space-time grid (m′,n′,k), (m′,n′) is the spatial index of the space-time grid that the UAV will enter next, and k is the time window index of the space-time grid; represents the remaining occupancy probability of the spatiotemporal grid (m′,n′,k) without generating hot spots; represents the set of spatiotemporal cells that drone i can potentially occupy in the grid (m′, n′).

[0100] 2) Energy constraint: If the generated edge causes the drone to have insufficient energy to reach the destination, it is not allowed.

[0101] The energy constraint has two purposes: first, to ensure the feasibility of the generated edges from the perspective of energy consumption, i.e., the UAV has enough energy to complete the generated edges; second, to narrow the search space by checking the feasibility of the UAV to reach the destination.

[0102] The drone in the present invention maintains a constant preset speed V i Pre , the preset speed is the speed with the minimum required power. Since the speed is constant, the required power is fixed, so the energy constraint is expressed as a time relationship.

[0103]

[0104] in, Indicates that drone i currently plans to pass through node The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), represents the potential minimum flight time required for UAV i to reach the terminal node after flying from grid (m,n) to grid (m′,n′); Indicates the moment when drone i enters the current airspace. Indicates the flight duration that the energy capacity of drone i can sustain in the current airspace.

[0105] The calculation expression is:

[0106]

[0107] Among them, V i Pre is the preset speed of drone i, is the flight distance of UAV i in the grid (m,n), φ1, φ2, φ 1′ ,φ 2′ They are the entry and exit node marks of grid (m,n) when UAV i flies from grid (m,n) to grid (m′,n′).

[0108] According to the characteristics of the eight-entry and exit point grid airspace model, the flight distance can be consistently expressed as:

[0109]

[0110] Among them, L Unit Indicates the side length of the grid.

[0111] Slave Node The potential minimum flight time required to reach the terminal node is calculated as:

[0112]

[0113] in, is the destination node reached by UAV i, S min (·,·) indicates calculating the distance between two input nodes.

[0114] The calculation expression is:

[0115]

[0116] in It represents the energy capacity that drone i can consume in the current airspace. Indicates the preset required power of drone i.

[0117] 3) Arrival time constraint: The generated edge ensures that the drone has the possibility to reach the end location before the latest acceptable arrival time.

[0118] The arrival time constraint is expressed as:

[0119]

[0120] in represents the latest acceptable arrival time of drone i.

[0121] 4) Direction constraint: For the sake of flight efficiency, the UAV is only allowed to fly in the direction close to the destination.

[0122] The direction constraint is expressed as:

[0123]

[0124]

[0125] in, and are the coordinates of the starting point and end point of UAV i.

[0126] Combining the above four constraints, in the process of generating the directed graph, the starting node of the alternative edge of drone i is The set of available spatiotemporal nodes for generating edges It can be expressed as:

[0127]

[0128] in, and Respectively represent the arrival node of drone i and planning moment.

[0129] The set of edges generated is It can be expressed as:

[0130]

[0131] Among them, (ν, t) represents a spatiotemporal node with ν node as spatial position and t as time index, and e[·,·] represents an edge formed by two input spatiotemporal nodes.

[0132] Step S2: Based on the starting position, the ending position and the constraint conditions, a directed graph representing the flight path of the UAV is generated in the space-time grid constructed by the eight-entry and exit point grid airspace model;

[0133] Based on the above constraints, the present invention adopts breadth-first search to generate a directed graph. In the process of generating the directed graph, a plurality of branches are formed by searching until all branches reach the destination of the UAV or no new branches can be generated. The search is completed.

[0134] Step S3, assigning weights to the edges of the directed graph based on the airspace usage cost and the energy consumption cost to generate a weighted directed graph;

[0135] In the process of generating the directed graph in the above steps, the present invention also considers the airspace usage fee and the energy consumption cost, and assigns weights to the edges of the directed graph based on the airspace usage fee and the energy consumption cost to generate a weighted directed graph. The specific process is as follows.

[0136] The goal is to optimize the economic cost obtained by comprehensively converting airspace usage fees and energy consumption costs, so the edge weight is set to:

[0137]

[0138] Among them, w e represents the weight of edge e, c Usage (e) represents the airspace usage fee of edge e, and θ(·) represents the economic cost of calculating the input energy. The economic cost of energy is calculated as: θ(W) = p EC W, where W is the energy consumed, p EC is the economic cost calculation coefficient.

[0139] In the present invention, the unit price of airspace usage fee changes with the time and space grid, and is charged according to the actual usage time. Usage (e) can be expressed as:

[0140]

[0141] in, represents the flight time of UAV i in the space-time grid (m,n,k) when it flies along edge e, u (m,n,k) Represents the unit price of the space-time grid (m,n,k), specifically the price per unit time; K e is the index set of the time window occupied by edge e, which can be expressed as:

[0142]

[0143] Wherein, k is the time index, k′ is the initial value of the first time window, k″ is the initial value of the second time window, and K is the time index range set of the spatial domain.

[0144] Since the weighted directed graph of the present invention is constructed based on the space-time dimension of two-dimensional space plus time, there may be multiple valid target nodes, that is, two-dimensional positions with the same position but different times. In order to facilitate the use of the graph search algorithm, the present invention introduces a virtual target node and creates virtual edges pointing to it from each valid end node, and sets the weights of the virtual edges to 0, and finally obtains a weighted directed graph, which is represented by Ω i WDG , which contains feasible trajectories without hot spots. Figure 5 As shown, there are multiple valid target nodes D1, D2 and D3. In order to facilitate the use of the graph search algorithm, a virtual target node D is introduced, and virtual edges pointing to it are created from each valid target node. The weights of these virtual edges are set to 0.

[0145] Step S4: searching the weighted directed graph based on the airspace use cost and the energy consumption cost to obtain the optimal path in the weighted directed graph as a hotspot-free track.

[0146] After obtaining the weighted directed graph, the a-star algorithm is used to quickly find the optimal path on the graph, that is, the optimal hotspot-free trajectory. The evaluation function of the a-star algorithm consists of the actual cost and the heuristic cost:

[0147]

[0148] in, and Represent the current weighted directed graph of UAV i The estimated cost of node ν, the actual cost from the origin to node ν, and the heuristic cost from node ν to the destination.

[0149] Actual cost is the optimal path from the origin to the node ν with the minimum sum of airspace usage fee and energy consumption cost, and the expression is:

[0150]

[0151] in and Respectively represent the number of drone i in the weighted directed graph The actual airspace usage fee and energy consumption cost from the starting node to the node ν on the a star can be obtained during the search process of star a. The A-star algorithm ensures that the optimal solution is obtained under the condition that the heuristic cost is always less than or equal to the actual cost from node ν to the destination.

[0152] Since the target consists of two parts: airspace usage fee and energy consumption cost, setting the heuristic function to the sum of the minimum values ​​of these two parts can ensure that the heuristic value of node ν to the destination is always less than or equal to its corresponding actual value. It can be expressed as:

[0153]

[0154] in, and They represent the destination node from node ν to drone i in the weighted directed graph. Since airspace usage is charged based on usage time, the heuristic minimum airspace usage cost can be set as:

[0155]

[0156] in, represents the potential minimum flight time from node ν to the destination node of UAV i; (m v ,n v ) is divided into the grid coordinates of node ν, t v is the time when the UAV arrives at node ν, is the latest possible arrival time of the drone.

[0157] Since the method of the present invention uses direction constraints in edge generation, the track of UAV i after node ν can only appear in the rectangle formed by node ν and its end node. The physical meaning of the above formula is to use the unit price of the minimum airspace usage fee that UAV i may charge as the product of its potential minimum flight time to reach the destination as the heuristic airspace usage fee.

[0158] The present invention assumes that the drone is moving at a constant speed V i Pre Flying and time interval without hot spots trajectory planning requires specified power Therefore It can be expressed as:

[0159]

[0160] The physical meaning of this formula is to use the energy i consumed by the drone in the potential minimum flight time to reach the destination with a predetermined power as the heuristic energy consumption cost. Therefore, the heuristic minimum airspace usage cost can be rewritten as:

[0161]

[0162] Finally, the a-star algorithm is used in the weighted directed graph The best non-hotspot track is obtained by searching represents the optimal hotspot-free trajectory of UAV i.

[0163] If there is no optimal hotspot-free trajectory, the adaptive take-off time delay strategy is implemented, including: when the departure time When the requirement of the last acceptable arrival time is met, Delay the departure time by a certain interval ΔT P , and try to regenerate the directed graph; if it is not satisfied, it is determined that the hotspot-free track generation has failed, and the flight plan can be rearranged or cancelled.

[0164] The present invention provides a pseudo code of the hotspot-free track fast planning method, as shown in Table 1.

[0165] Table 1

[0166]

[0167] The present invention also provides a low-altitude UAV non-hotspot track rapid planning system, comprising the following modules:

[0168] The determination module 20 is used to receive a non-hotspot trajectory planning request, and determine the constraint conditions of the non-hotspot trajectory planning based on the non-hotspot trajectory planning request; the non-hotspot trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time and a preset flight speed;

[0169] A directed graph generation module 21 is used to generate a directed graph representing the flight path of the UAV in the space-time grid constructed by the eight-entry and exit point grid airspace model based on the starting position, the end position and the constraint conditions;

[0170] A directed graph weighting module 22, used to assign weights to the edges of the directed graph based on airspace usage cost and energy consumption cost, to generate a weighted directed graph;

[0171] The search module 23 is used to search the weighted directed graph based on the airspace use cost and the energy consumption cost, and obtain the optimal path in the weighted directed graph as a hotspot-free track.

[0172] In some embodiments, the fast track planning method provided by the present invention is applied in the interaction process of airspace equipment. The airspace equipment includes a common information service provider 14 (CISP), an unmanned aircraft system traffic management service provider 13 (UTMSP), a ground control station 12 (GCS) and a drone 11, such as Figure 3 shown.

[0173] The UAV hotspot-free trajectory rapid planning 15 in the low-altitude airspace is executed by UTMSP, which obtains the occupancy status of the airspace unit from CISP to perform hotspot-free trajectory planning for the UAV controlled by the GCS it serves.

[0174] In some embodiments, Figure 3 As shown, the interaction process of airspace equipment is as follows:

[0175] Step 101, the GCS sends a hotspot-free trajectory planning request to the UTMSP according to its flight mission requirements, wherein the hotspot-free trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed.

[0176] Step 102, CISP sends the current airspace information, i.e., the occupancy status of the airspace unit, to UTMSP so that it can plan a hotspot-free trajectory.

[0177] Step 103, UTMSP calls its hotspot-free trajectory planning module to perform hotspot-free trajectory rapid planning based on the obtained airspace information and service request.

[0178] Step 104: the hotspot-free trajectory planning module returns the generated hotspot-free trajectory planning to the UTMSP.

[0179] Step 105: UTMSP sends the planned hotspot-free trajectory to CISP to update the airspace occupancy status, including the remaining maximum occupancy probability of each space-time network.

[0180] Step 106, UTMSP sends the planned hotspot-free track to GCS to complete the track planning service.

[0181] Step 107, the GCS sends the obtained hotspot-free track to the UAV to which it belongs to execute the flight mission.

[0182] Although the specific embodiments of the present invention depict various actions or steps in a specific order, this should be understood as requiring such actions or steps to be performed in the specific order shown or in a sequential order, or requiring all illustrated actions or steps to be performed to obtain the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

[0183] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for fast planning of UAV trajectory without hot spots in low-altitude airspace, characterized in that: The following steps are involved: Step S1, receiving a hotspot-free trajectory planning request, and determining constraints for the hotspot-free trajectory planning based on the hotspot-free trajectory planning request; the hotspot-free trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed; Step S2: Based on the starting position, the ending position and the constraint conditions, a directed graph representing the flight path of the UAV is generated in the space-time grid constructed by the eight-entry and exit point grid airspace model; Step S3, assigning weights to the edges of the directed graph based on the airspace usage cost and the energy consumption cost to generate a weighted directed graph; Step S4: searching the weighted directed graph based on the airspace use cost and the energy consumption cost to obtain the optimal path in the weighted directed graph as a hotspot-free track.

2. The method for rapid planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 1 is characterized in that: In step S1, the constraints of the hotspot-free trajectory planning include: 1) Hotspot constraint: The constraint condition is that the track cannot pass through a hotspot or cause a hotspot to be generated. The hotspot indicates the location where the UAV capacity has reached the upper limit; 2) Energy constraint: The generated trajectory requires the UAV to have enough energy to reach the destination; 3) Arrival time constraint: the generated trajectory requires the drone to arrive at the end location before the latest acceptable arrival time; 4) Direction constraint: The constraint condition is: the drone is only allowed to fly in the direction close to the destination.

3. The method for fast planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 2 is characterized in that: The eight-in / out point grid airspace model in step S2 divides the airspace into units in the following way: The airspace is divided into square grids arranged in a matrix on a plane, and a non-overlapping entry point and an exit point are set on each side of each grid as nodes through which the UAV flies; each grid has multiple time windows, and a time window of a grid is called a space-time grid; the airspace represents the aerial area where low-altitude UAVs fly.

4. The method for rapid planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 3 is characterized in that: The process of generating a directed graph representing the flight path of the drone in step S2 specifically includes: 1) Determine the starting node and the ending node corresponding to the grid according to the starting position and the ending position; 2) Based on the hot spot constraint, energy constraint, arrival time constraint and direction constraint, a breadth-first search method is used to search all nodes of all grids, and multiple branches in a directed graph are generated starting from the starting node; 3) Determine whether there is a branch that can reach the end node; when all branches reach the end node or no new branches can be generated, complete the search and obtain the generated directed graph.

5. The method for fast planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 4 is characterized in that: Among the constraints of the hotspot-free trajectory planning, The expression of the hot spot constraint is: in, represents the probability of UAV i occupying the spatiotemporal grid (m′,n′,k), the spatiotemporal grid (m′,n′,k) represents the kth time window of the grid with the m′th row and n′th column, m′,n′ is the spatial index of the grid that the UAV will enter next, and k is the time window index of the spatiotemporal grid; represents the remaining occupancy probability of the spatiotemporal grid (m′,n′,k) without generating hot spots; represents the set of spatiotemporal grids that drone i can potentially occupy in grid (m′,n′); The expression of energy constraint is: in, Indicates the position coordinates of the nodes marked as φ1, φ2, and φ3 on the grid (m, n). m and n are the spatial indexes of the grid. When the values ​​of (φ1, φ2) are (0, 0), (0, 1), (1, 0), and (1, 1), they indicate that the nodes are on the left, right, bottom, and top of the current grid, respectively. When φ3 is 0, the node is the entrance, and when it is 1, the node is the exit. Indicates the node that the drone currently plans to pass through The moment of entering the grid (m,n), Indicates i drone slave node The flight time in grid (m,n) when flying to grid (m′,n′), It represents the potential minimum flight time required for UAV i to reach the end node after flying from grid (m,n) to grid (m′,n′); Indicates the moment when UAV i enters the target airspace, It represents the flight time that the energy capacity of UAV i can sustain in the target airspace; The expression of the arrival time constraint is: in, represents the latest acceptable arrival time of drone i, Determined by the latest acceptable arrival time in the no-hotspot trajectory planning request; The expression of the direction constraint is: in, and are the coordinates of the starting point and end point of UAV i.

6. The method for fast planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 5 is characterized in that: Step S3 specifically includes: Step S3-1, set the weight of the edge of the directed graph to: Among them, w e represents the weight of edge e, c Usage (e) represents the airspace usage fee for edge e, represents the economic cost of calculating the energy input, Indicates the preset required power of drone i; represents the flight time of UAV i in the space-time grid (m,n,k) when it flies along edge e, u (m,n,k) Represents the unit price of the space-time grid (m,n,k), specifically the price per unit time; K e The index set of the time window occupied by edge e: Where k is the time index, k′ is the initial value of the first time window, k″ is the initial value of the second time window, and K is the time index range set; Step S3-2, determine the virtual target node, and create a virtual edge from each valid end node to the virtual target node, set the weight of the virtual edge to 0, and finally obtain a weighted directed graph, which is represented as 7. The method for fast planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 6 is characterized in that: Step S4 specifically includes: Step S4-1, setting the actual cost of the a-star algorithm, taking the minimum value of the sum of the airspace usage cost and the energy consumption cost on the weighted directed graph as the actual cost; Step S4-2, setting the heuristic cost of the a-star algorithm, and taking the sum of the minimum airspace usage cost and the minimum energy consumption cost on the weighted directed graph as the heuristic cost; Step S4-3: Based on the actual cost and the heuristic cost, the a-star algorithm is used to search for an optimal path on a weighted directed graph as a hotspot-free track.

8. The method for fast planning of UAV hotspot-free trajectory in low-altitude airspace according to claim 7 is characterized in that: Step S4 also includes: Step S4-4: If the optimal path cannot be found, the adaptive take-off time delay strategy is executed, including: when the departure time When the last acceptable arrival time requirement is met, the departure time is delayed by a time interval ΔT P , and try to regenerate the directed graph; if the requirement of the last acceptable arrival time is not met, it is determined that the hotspot-free track generation has failed, and the flight plan is rescheduled or cancelled; The expression of the requirement of the final acceptable arrival time is: in, is the departure time of drone i, ν Start ,ν End are the starting node and the ending node of UAV i, respectively. min (·,·) indicates the distance between the two input nodes, V i Pre is the preset flight speed.

9. The method for fast planning of UAV non-hotspot trajectory in low-altitude airspace according to claim 8 is characterized in that: The actual cost is expressed as: in, and Respectively represent the number of drone i in the weighted directed graph The actual airspace usage cost and actual energy consumption cost from the starting node to the node ν; The expression of the heuristic cost is: in and They are respectively UAV i in the weighted directed graph Heuristic minimum airspace usage cost and heuristic minimum energy consumption cost on ; The expression of the heuristic minimum airspace usage cost is: in, represents the potential minimum flight time from node ν to the destination node of UAV i; (m v ,n v ) is divided into the grid coordinates of node ν, t v is the time when the UAV arrives at node ν, is the latest possible arrival time of the drone; The expression of the heuristic minimum energy cost is:

10. A fast track planning system for drones without hot spots in low-altitude airspace, characterized in that: Includes the following modules: A determination module, configured to receive a no-hotspot trajectory planning request, and determine a constraint condition of the no-hotspot trajectory planning based on the no-hotspot trajectory planning request; the no-hotspot trajectory planning request includes a starting position, a departure time, an end position, a latest acceptable arrival time, and a preset flight speed; A directed graph generation module, for generating a directed graph representing the flight path of the UAV in a space-time grid constructed by an eight-entry-exit point grid airspace model based on the starting position, the ending position and the constraint conditions; A directed graph weighting module, used for assigning weights to the edges of the directed graph based on airspace usage cost and energy consumption cost to generate a weighted directed graph; The search module is used to search the weighted directed graph based on the airspace use cost and the energy consumption cost to obtain the optimal path in the weighted directed graph as a hotspot-free track.

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