A method and application of UAV field operation dispatching

Abstract

The present invention relates to the technical field of unmanned aerial vehicle (UAV) field dispatching, and provides a method and application for dispatching UAV field operation, the method comprising: S1, vertical take-off and landing field field modeling: constructing a field network topology diagram according to the layout of the take-off and landing points, waiting points, and parking positions of the vertical take-off and landing field, and constructing a UAV taxiing route network in combination with the take-off and landing points and according to the airport field network diagram; S2, airport field resource allocation modeling: establishing a UAV taxiway avoidance strategy, and constructing a UAV resource allocation model with the shortest total dispatching time of all UAVs as the optimization goal under the premise of satisfying constraint conditions; S3, airport field operation capacity evaluation: traffic flow simulation, and evaluating the operation capacity of the airport field. The present invention allocates airport field resources through airport field modeling, field traffic flow modeling, and designing intelligent algorithms to control the UAV sliding-in and sliding-out time, reduce the entry and departure delay time, and achieve the purpose of improving the field operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) field dispatching, and in particular to an unmanned aerial vehicle (UAV) field operation dispatching method and application thereof, which is suitable for a vertical take-off and landing composite wing UAV. Background Art

[0002] As a rapidly developing emerging industry, drones can be used in various scenarios such as logistics distribution, emergency rescue, traffic survey, forest protection, etc. in the future. Among them, fixed-wing and multi-rotor drones that perform vertical take-off and landing solve the requirements of drones for take-off and landing sites. At the same time, they have the advantages of long flight distance, fast speed and high altitude, and are widely used in unmanned operation scenarios. In the field operation stage, unlike traditional taxiing aircraft, vertical take-off and landing drones do not include runway operation stage. At present, the route between the take-off and landing area and the parking position (hangar) of drones is completed by manual remote control. The drone only needs to automatically take off from the take-off area or land to the landing area. This method is only applicable to a small number of take-offs and landings. In the future, when drones are operated on a large scale, high-density take-offs and landings will greatly reduce the efficiency of airport field operations and cause flight delays. Formulating an objective, accurate and scientific field operation scheduling strategy is an effective way to improve operation efficiency and expand operation capacity under limited airport field resources. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and application for dispatching the operation of unmanned aerial vehicles on the spot, which has the advantages of planning routes and improving the efficiency of the operation on the spot, and solves the problems raised by the background technology.

[0004] The present invention adopts the following technical solution:

[0005] On the one hand, the present invention provides a method for dispatching a UAV on-site operation, which is used for a vertical take-off and landing composite wing UAV, comprising the following steps:

[0006] S1. Airport scene modeling: According to the setting information of the parking positions, take-off and landing points and waiting points of the airport scene, an airport scene network diagram is obtained, and a UAV taxiing route network is constructed according to the airport scene network diagram;

[0007] S2. Airport surface resource allocation modeling: Establish a UAV taxiway avoidance strategy. Based on the constraints of the airport, and on the premise of satisfying the constraints, take the shortest total dispatch time of all UAVs as the optimization goal and build a UAV resource allocation model.

[0008] S3. Airport surface operation capacity assessment: For a given drone arrival flow and departure flow, the drone taxi route network generated in step S1 and the drone resource allocation model generated in step S2 are used to perform traffic flow simulation on the airport surface network diagram to assess the airport surface operation capacity.

[0009] According to any possible implementation described above, a further implementation is provided, wherein step S1 specifically comprises:

[0010] S1.1 The airport surface network diagram is composed of points and edges. The points are composed of take-off and landing points, waiting points and parking positions. The edges are straight line segments or arc segments between two points, that is, the routes that the UAV needs to move on the surface, including the line segments connecting the parking position and the waiting point, and the line segments connecting the waiting point and the adjacent take-off and landing points.

[0011] S1.2 Generate the UAV taxi route network. Assume that the vertical take-off and landing field network topology graph T = (V, E, P), V is the set of nodes, E is the set of edges, and P is the set of weights. The distance of each edge in the vertical take-off and landing field topology graph is used as the weight of the edge, and the expressions of the adjacency matrix V[i][j] and the weight matrix P[i][j] are obtained as follows:

[0012]

[0013]

[0014] The taxi route network of the vertical take-off and landing field is generated with the shortest distance and the least number of intersections between any parking position and the take-off and landing point as the goal and at least one route between the two points as the constraint.

[0015] Any possible implementation described above further provides an implementation, in which in step S2, the UAV taxiway avoidance strategy includes:

[0016] Head-on collision: Head-on collision occurs when two drones are traveling in opposite directions on the same link. According to the first-come, first-served (FCFS) rule, the other drone is made to wait, thereby obtaining the delay time of the head-on collision.

[0017] Cross-conflict: Cross-conflict occurs at the intersection of different links. According to whether the time interval between two UAVs with potential cross-conflict passing through the same intersection meets the minimum time interval standard and priority, the UAV (low priority) that needs to stop and wait is determined, and then the delay time of the cross-conflict is obtained.

[0018] Rear-end collision: Rear-end collision occurs when two drones are traveling in the same direction on the same link. According to the wake interval of the drones, according to the maximum interval ΔL, combined with the relationship between the link length L and ΔL, virtual nodes are set to avoid rear-end collisions.

[0019] Any possible implementation described above further provides an implementation, in which in step S2, the drone resource allocation model is as follows:

[0020] S2.1 Select decision variables: Decision variables include take-off and landing point selection variables, path node selection variables, time variables for the drone to enter the mth node of the route, judgment variables for the existence of intersection conflicts, judgment variables for the existence of head-on conflicts, and judgment variables for the existence of rear-end conflicts;

[0021] S2.2 Determine the constraints: The constraints include initial state constraints, path constraints, taxiing speed constraints, taxiing conflict constraints, take-off and landing point constraints, take-off and landing point constraints on UAV models, time interval constraints for the front and rear UAVs assigned to the same take-off and landing point, and the number of UAVs waiting for take-off and landing;

[0022] S2.3 Under the constraints determined in step S2.2, resources are allocated to each drone with the shortest total scheduling time of all drones as the optimization goal; the resource allocation includes the take-off and landing points and taxiing paths of the drones.

[0023] According to any possible implementation described above, a further implementation is provided, wherein step S3 specifically includes:

[0024] S3.1 For a given UAV arrival and departure flow, set the simulation duration and the allowed delay level;

[0025] S3.2 Generate a simulated traffic flow based on the UAV taxi route network generated in step S1 and the UAV resource allocation model constructed in step S2, determine the target position and taxi route of each UAV, and repeat the calculation and update of the UAV speed and position as the simulation time goes by, so as to realize the movement of individual UAVs. When the UAV reaches the target position, the UAV disappears. When all generated UAVs disappear or the simulation time ends, the simulation is completed.

[0026] S3.3 outputs the taxiing time of each individual drone, the conflict-free taxiing speed curve of the drone, the number of drones that have completed the taxiing process, and the total dispatch time of all drones; and evaluates the operating capacity of the airport based on the output results.

[0027] As any possible implementation described above, a further implementation is provided, in which in step S3, traffic flow simulation is performed using NETLOGO software.

[0028] On the other hand, the present invention also provides an application of the above-mentioned drone scene operation scheduling method, which completes the theoretical capacity evaluation of the drone scene by repeatedly changing the density of drone arrival flow and departure flow and performing traffic flow simulation to obtain the maximum arrival flow and departure flow.

[0029] On the other hand, the present invention also provides an application of the above-mentioned UAV field operation scheduling method. For the set arrival flow and departure flow, the settings of the parking positions, take-off and landing points and waiting points at the airport field are changed multiple times. According to multiple simulation results, multiple different UAV total scheduling times are obtained; the UAV field corresponding to the smallest UAV total scheduling time is selected, and the selection of parking positions, take-off and landing points and waiting points is optimized and improved.

[0030] On the other hand, the present invention also provides an information processing terminal for implementing the above-mentioned UAV field operation scheduling method.

[0031] On the other hand, the present invention also provides a computer-readable storage medium, including instructions, which, when executed on a computer, enable the computer to execute the above-mentioned UAV field operation scheduling method.

[0032] The beneficial effects of the present invention are as follows: the present invention allocates airport surface resources through airport surface modeling, surface traffic flow modeling, and the design of intelligent algorithms, controls the sliding-in and sliding-out time of drones, reduces the delay time of drones entering and leaving the airport, and achieves the purpose of improving the surface operation efficiency.

[0033] Instruction Manual

[0034] Figure 1 Shown is an example of a vertical take-off and landing field topology diagram.

[0035] Figure 2 The figure is a flow chart of a method for dispatching the operation of a UAV on-site according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The specific embodiments of the present invention will be described in detail below in conjunction with specific drawings. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects.

[0037] like Figure 2 As shown, an embodiment of the present invention provides a method for dispatching a UAV on-site operation, comprising the following steps:

[0038] S1. Airport scene modeling: According to the setting information of the parking positions, take-off and landing points and waiting points of the airport scene, an airport scene network diagram is obtained, and a UAV taxiing route network is constructed according to the airport scene network diagram;

[0039] S2. Airport surface resource allocation modeling: Establish a UAV taxiway avoidance strategy. Based on the constraints of the airport, and on the premise of satisfying the constraints, take the shortest total dispatch time of all UAVs as the optimization goal and build a UAV resource allocation model.

[0040] S3. Airport surface operation capacity assessment: For a given drone arrival flow and departure flow, the drone taxi route network generated in step S1 and the drone resource allocation model generated in step S2 are used to perform traffic flow simulation on the airport surface network diagram to assess the airport surface operation capacity.

[0041] In a specific embodiment, S1, airport scene modeling:

[0042] S1.1 The airport surface network diagram is composed of points and edges. The points are the take-off and landing points, waiting points and parking positions. The edges are the straight line segments or arc segments between two points, that is, the routes that the drone needs to move on the surface, such as Figure 1 As shown in the figure, in the airport scene, the position of each point is planned in advance and is fixed; the waiting point is a point close to the take-off and landing point. A waiting point is connected to a take-off and landing point as a point for the take-off sequence to wait for take-off;

[0043] S1.2 Generate the UAV taxi route network. Assume that the vertical take-off and landing field network topology graph T = (V, E, P), V is the set of nodes, E is the set of edges, and P is the set of weights. The distance of each edge in the vertical take-off and landing field topology graph is used as the weight of the edge, and the expressions of the adjacency matrix V[i][j] and the weight matrix P[i][j] are obtained as follows:

[0044]

[0045]

[0046] The taxi route network of the vertical take-off and landing field is generated with the shortest distance and the least number of intersections between any parking position and the take-off and landing point as the goal and at least one route between the two points as the constraint.

[0047] In a specific embodiment, S2, airport surface resource allocation modeling:

[0048] (1) Establishing a UAV taxiway avoidance strategy, including:

[0049] Design the wake separation of UAVs according to the technical characteristics (maximum take-off weight) of the type of UAVs used and the current wake separation standards in China;

[0050] Head-on collision: Head-on collision occurs when two drones are traveling in opposite directions on the same link. According to the first-come, first-served (FCFS) rule, the other drone is made to wait, thereby obtaining the delay time of the head-on collision.

[0051] Cross-conflict: Cross-conflict occurs at the intersection of different links. According to whether the time interval between two UAVs with potential cross-conflict passing through the same intersection meets the minimum time interval standard and priority, the UAV that needs to stop and wait (low priority) is determined, and then the delay time of the cross-conflict is obtained;

[0052] Rear-end collision: Rear-end collision occurs when two drones are traveling in the same direction on the same link. According to the wake interval of the drones, according to the maximum interval ΔL, combined with the relationship between the link length L and ΔL, virtual nodes are set to avoid rear-end collisions.

[0053] (2) The UAV resource allocation model is as follows:

[0054] S2.1 Select decision variables, which include:

[0055] Landing point selection variable (0-1 variable);

[0056] Path node selection variable (0-1 variable);

[0057] The time variable when the UAV enters the mth node of the route;

[0058] There is a judgment variable for cross-conflict (UAV a enters node m during the planning period, and UAV b also enters node m during the planning period);

[0059] There is a head-to-head conflict (drone a moves from node m to node n during the planning period, and drone b moves from node n to node m during the planning period);

[0060] There is a judgment variable for the rear-end collision (UAV a moves from node m to node n during the planning period, and UAV b also moves from node m to node n during the planning period);

[0061] S2.2 Determine the constraints:

[0062] Initial state restriction constraints: restrict the initial position and time of the drone, that is, ensure that the first position of each traffic individual is at the original point (landing aircraft at the landing point, take-off aircraft at the parking position), and ensure that the departing aircraft will not move before the earliest push-out time;

[0063] Path constraint: ensure that the path passed is a feasible route on the taxiway;

[0064] Gliding speed limit: The glide speed cannot exceed the maximum speed allowed by the drone;

[0065] Sliding conflict restrictions:

[0066] a. UAV wake separation constraints (designed according to maximum take-off weight, including distance separation and time interval);

[0067] b. By setting virtual nodes on a route of length L (L ≥ ΔL), the route is divided into several roads of length ΔL to avoid potential rear-end collisions on the route;

[0068] Take-off and landing point restrictions: Each drone must be assigned and can only be assigned one take-off and landing point;

[0069] The restrictions on the drone model at the take-off and landing point. The drone model must meet the maximum model allowed at the take-off and landing point;

[0070] The entry and departure times of the preceding and following drones assigned at a takeoff point must meet certain time interval constraints. Any drone can only enter the area a certain time after its predecessor drone has slid out of the takeoff and landing point;

[0071] Takeoff waiting drone number. Limit the number of aircraft waiting to take off at each takeoff holding point.

[0072] S2.3 Under the constraints determined in step S2.2, the total scheduling time t of all drones is minimized and resources are allocated to each drone. The total scheduling event is: the scheduling time of each drone, including the taxiing time t taxi,k and waiting time t wait,k The sum of, that is:

[0073]

[0074] In the formula, K is the set of UAVs; Q is the set of glide paths; is the waiting time of the kth UAV on path q; is the taxiing time of the kth UAV on path q. The resource allocation includes the take-off and landing points and taxiing paths of the UAVs.

[0075] In a specific embodiment, step S3 specifically includes:

[0076] S3.1 For a given UAV arrival and departure flow, set the simulation duration and the allowed delay level;

[0077] S3.2 Generate a simulated traffic flow based on the UAV taxi route network generated in step S1 and the UAV resource allocation model constructed in step S2, determine the target position and taxi route of each UAV, and repeat the calculation and update of the UAV speed and position as the simulation time goes by, so as to realize the movement of individual UAVs. When the UAV reaches the target position, the UAV disappears. When all generated UAVs disappear or the simulation time ends, the simulation is completed.

[0078] S3.3 outputs the taxiing time of each individual drone, the conflict-free taxiing speed curve of the drone, the number of drones that have completed the taxiing process, and the total dispatch time of all drones; and evaluates the operating capacity of the airport based on the output results.

[0079] Preferably, in step S3.2, traffic flow simulation is performed using NETLOGO software.

[0080] An embodiment of the present invention is an application of the above-mentioned drone scene operation scheduling method. By repeatedly changing the density of drone arrival flow and departure flow and performing traffic flow simulation, the maximum arrival flow and departure flow can be obtained to complete the theoretical capacity evaluation of the drone scene.

[0081] An embodiment of the present invention is an application of the above-mentioned drone field operation scheduling method. For the set arrival flow and departure flow, the settings of the parking positions, take-off and landing points and waiting points of the airport field are changed multiple times, and multiple different drone total scheduling times are obtained according to multiple simulation results; the drone field corresponding to the smallest drone total scheduling time is selected, and the selection or design of the parking positions, take-off and landing points and waiting points is optimized and improved, so as to obtain the optimal design of the parking positions, take-off and landing points and waiting points.

[0082] An embodiment of the present invention provides an information processing terminal for implementing the above-mentioned UAV field operation scheduling method.

[0083] An embodiment of the present invention provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enables the computer to execute the above-mentioned UAV field operation scheduling method.

[0084] The present invention allocates airport surface resources through airport surface modeling, surface traffic flow modeling, and the design of intelligent algorithms, controls the sliding-in and sliding-out time of drones, reduces the delay time of drones entering and leaving the airport, and can greatly improve the surface operation efficiency; it has broad application prospects.

[0085] Although several embodiments of the present invention have been given herein, those skilled in the art should understand that the embodiments of the present invention may be modified without departing from the spirit of the present invention. The above embodiments are merely exemplary and should not be used as a limitation on the scope of the present invention.

Claims

1. A method for dispatching UAV field operations for vertical take-off and landing composite wing UAVs, characterized in that: The method comprises the following steps: S1. Airport scene modeling: According to the setting information of the parking positions, take-off and landing points and waiting points of the airport scene, an airport scene network diagram is obtained, and a UAV taxiing route network is constructed according to the airport scene network diagram; S2. Airport surface resource allocation modeling: Establish a UAV taxiway avoidance strategy. Based on the constraints of the airport, and on the premise of satisfying the constraints, take the shortest total dispatch time of all UAVs as the optimization goal and build a UAV resource allocation model. S3, airport surface operation capacity assessment: for a given drone arrival flow and departure flow, the drone taxi route network generated in step S1 and the drone resource allocation model generated in step S2 are used to perform traffic flow simulation on the airport surface network diagram to assess the airport surface operation capacity; Step S1 is specifically as follows: S1.1 The airport surface network diagram is composed of points and edges. The points are composed of take-off and landing points, waiting points and parking positions. The edges are straight line segments or arc segments between two points, including the line segments connecting the parking position and the waiting point, and the line segments connecting the waiting point and the adjacent take-off and landing points. S1.2 Generate a UAV taxi route network; take the distance of each edge in the vertical take-off and landing field topology as the edge weight, obtain the adjacency matrix and the weight matrix, take the shortest distance between any parking position and the take-off and landing point and the minimum number of intersections as the goal, and there must be at least one route between the two points as the constraint, and generate the vertical take-off and landing field taxi route network; In step S2, the drone resource allocation model is as follows: S2.1 Select decision variables: Decision variables include take-off and landing point selection variables, path node selection variables, time variables for the drone to enter the mth node of the route, judgment variables for the existence of intersection conflicts, judgment variables for the existence of head-on conflicts, and judgment variables for the existence of rear-end conflicts; m is the unified node number of the airport scene; S2.2 Determine the constraints: The constraints include initial state constraints, path constraints, taxiing speed constraints, taxiing conflict constraints, take-off and landing point constraints, take-off and landing point constraints on UAV models, time interval constraints for the front and rear UAVs assigned to the same take-off and landing point, and the number of take-off and landing waiting UAVs constraints; S2.3 Under the constraints determined in step S2.2, resources are allocated to each UAV with the shortest total dispatch time of all UAVs as the optimization goal; the resource allocation includes the take-off and landing points and taxiing paths of the UAVs; Step S3 specifically includes: S3.1 For a given UAV arrival and departure flow, set the simulation duration and the allowed delay level; S3.2 Generate simulated traffic flow based on the UAV taxi route network generated in step S1 and the UAV resource allocation model constructed in step S2, determine the target position and taxi route of each UAV, and repeat the calculation and update of the UAV speed and position as the simulation time goes by, so as to realize the movement of individual UAVs. When the UAV reaches the target position, the UAV disappears. When all generated UAVs disappear or the simulation time ends, the simulation ends. S3.3 Output the taxiing time of each UAV, the conflict-free taxiing speed curve of the UAV, the number of UAVs that have completed the taxiing process, and the total dispatch time of all UAVs; and evaluate the operating capacity of the airport based on the output results.

2. The method for dispatching the UAV field operation according to claim 1, characterized in that: In step S2, the UAV taxiway avoidance strategy includes: Head-on collision: Head-on collision occurs when two drones are traveling in opposite directions on the same link. According to the first-come-first-served rule, the other drone is allowed to wait, thereby obtaining the delay time of the head-on collision. Cross-conflict: Cross-conflict occurs at the intersection of different links. The time interval between two UAVs with potential cross-conflict passing through the same intersection meets the minimum time interval standard and priority, and the UAV that needs to stop and wait is determined, thereby obtaining the delay time of the cross-conflict. Rear-end collision: Rear-end collision occurs when two drones are traveling in the same direction on the same link. According to the wake interval of the drones, according to the maximum interval ΔL, combined with the relationship between the link length L and ΔL, virtual nodes are set to avoid rear-end collisions.

3. The method for dispatching the UAV operation on the spot according to claim 1, characterized in that: In step S3, traffic flow simulation is performed using NETLOGO software.

4. A method for evaluating the theoretical capacity of a UAV field based on the UAV field operation scheduling method according to any one of claims 1 to 3, characterized in that: The method changes the density of the arrival flow and departure flow of drones multiple times, and uses the drone scene operation scheduling method to simulate traffic flow, obtain the maximum arrival flow and departure flow, and complete the theoretical capacity evaluation of the drone scene.

5. A method for optimizing a drone scene based on the method for dispatching drone scene operations as described in any one of claims 1 to 3, characterized in that: For the set arrival flow and departure flow, the settings of the parking positions, take-off and landing points and waiting points at the airport are changed multiple times, and the traffic flow simulation is performed using the UAV field operation scheduling method, and multiple different UAV total scheduling times are obtained based on multiple simulation results; The vertical take-off and landing field scheduling strategy corresponding to the minimum total UAV scheduling time is selected, and the selection of parking positions, take-off and landing points and waiting points is optimized and improved.

6. A computer-readable storage medium comprising instructions, which, when executed on a computer, enables the computer to execute the drone field operation scheduling method as described in any one of claims 1-3.

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