Method and device for messenger unmanned aerial vehicle path planning under road network constraints and storage medium

Abstract

The application relates to a messenger unmanned plane path planning method and device under road network constraints and a storage medium, wherein the method comprises the following steps: S1, acquiring position and speed information of a messenger unmanned plane and unmanned vehicles; S2, acquiring road network information, and obtaining driving tracks of the unmanned vehicles in combination with initial positions and target positions of the unmanned vehicles; S3, determining visiting sequences of the unmanned vehicles which have not been visited; S4, determining re-visit time intervals of the unmanned vehicles which need to be visited by the messenger unmanned plane; S5, determining position intervals of the unmanned vehicles which need to be visited and two endpoints of the position intervals; S6, determining a path track of the messenger unmanned plane for re-visiting the unmanned vehicle which needs to be visited based on the two endpoints of the obtained position interval; and S7, after the positions of the other unmanned vehicles are updated, repeating steps S3 to S6 until the visiting of all the unmanned vehicles in the current period is completed. Compared with the prior art, the application has the advantages of reducing calculation amount and the like.

Description

Technical Field

[0001] This invention relates to the field of path planning for messenger drones, and in particular to a method, apparatus, and storage medium for path planning of messenger drones under road network constraints. Background Technology

[0002] Air-ground collaborative unmanned systems are a typical application of multi-robot collaboration. Heterogeneous multi-robot systems composed of drones and unmanned vehicles have the characteristics of strong environmental adaptability and high task execution efficiency. The high payload and long endurance of unmanned vehicles effectively make up for the shortcomings of drones in terms of poor endurance and low payload. On the other hand, the wide field of view and high mobility of drones can effectively make up for the shortcomings of unmanned vehicles in terms of slow movement and narrow field of view. Through the complementary advantages of the two types of task subjects, the system's task execution capability in complex environments can be improved.

[0003] In air-ground collaborative unmanned systems, autonomous vehicles (RVs) and drones can assume different roles based on mission characteristics and requirements. In the messenger mechanism, RVs act as actuators performing tasks on the ground, but they are far apart and cannot communicate directly. To achieve collaboration among multiple RVs, drones act as messengers, periodically traversing the RVs and relaying information between them. Each RV relies on the information relayed by the drones to make decisions. The path planning problem of the messenger drones is crucial to determining the overall collaborative efficiency of the system.

[0004] In practical applications, the travel paths of autonomous vehicles are often limited by the navigable roads in the environment. Given the starting coordinates and target point of the autonomous vehicle, its route can be planned in advance on a road network map. The path planning problem for messenger drones involves the drone periodically traversing all autonomous vehicles traveling on the road network to determine the order and location of visits. However, due to uncertainties caused by the movement of the autonomous vehicles or the environment, the drone cannot accurately predict the positions of the vehicles. Therefore, to ensure successful revisits of all autonomous vehicles on the road network, it is assumed that the speed of each vehicle varies within a certain range, and a maximum revisit time constraint is introduced. Based on the positional relationships between the communication neighborhoods formed by the drone at critical speeds within the maximum revisit time, different robust revisit strategies are selected for searching to ensure successful revisits of all vehicles. The path planning problem of the above-mentioned UAV is solved based on a decoupling method. First, the order in which the UAV visits each unmanned vehicle is determined by heuristic rules. Second, the boundary of the revisit time of the UAV to the unmanned vehicle is determined. Finally, the extreme position of the unmanned vehicle is determined based on the critical speed and critical revisit time of the unmanned vehicle. The corresponding robust revisit strategy is selected to plan the path by judging whether the two extreme positions belong to the same road segment and whether there is a common neighborhood.

[0005] Regarding the path planning problem of messenger drones under uncertain motion conditions of unmanned vehicles, no descriptions or reports similar to the present invention have been found so far, and no similar information has been collected at home and abroad. Summary of the Invention

[0006] The purpose of this invention is to provide a method, device, and storage medium for path planning of messenger drones under road network constraints. By determining the access order and access location of each unmanned vehicle by the messenger drone, the complex multivariate coupled optimization problem is successfully decoupled, and deterministic access of each unmanned vehicle by the messenger drone is realized. This can greatly reduce the amount of computation, improve efficiency, and reduce hardware costs.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for path planning of a messenger UAV under road network constraints includes:

[0009] Step S1: Obtain the initial position and heading angle of the messenger drone, the initial position and target position of each unmanned vehicle, and the speed range of each unmanned vehicle;

[0010] Step S2: Obtain road network information, and based on the initial and target positions of each unmanned vehicle, obtain the driving trajectory of each unmanned vehicle based on the road network information;

[0011] Step S3: Obtain all unvisited unmanned vehicles by the messenger drone during this period, and determine the access order for each unvisited unmanned vehicle based on its location.

[0012] Step S4: Based on the speed range of the unmanned vehicle that needs to be accessed and its driving trajectory, determine the revisit time range of the messenger drone for the unmanned vehicle that needs to be accessed.

[0013] Step S5: Based on the speed range and revisit time range of the autonomous vehicle to be visited, determine the location range of the autonomous vehicle to be visited, as well as the two endpoints of the location range;

[0014] Step S6: Based on the two endpoints of the obtained location interval, determine the path trajectory of the messenger drone to revisit the unmanned vehicle that needs to be visited;

[0015] Step S7: After updating the location of other driverless cars, repeat steps S3 to S6 until all driverless cars have been visited in this cycle.

[0016] Step S6 specifically includes:

[0017] If the two endpoints of the location interval are within the same road segment, and there is a common neighborhood between the communication neighborhoods of the two endpoints, then the control messenger drone will traverse this common neighborhood.

[0018] Step S6 specifically includes:

[0019] If the two endpoints of the location interval are in different road segments, determine the communication neighborhood of the unmanned vehicle at the end of the road segment where the previous endpoint is located. If there is a common neighborhood among the communication neighborhood of the unmanned vehicle and the communication neighborhood of the two endpoints, then control the messenger drone to pass through this common neighborhood.

[0020] Step S6 specifically also includes:

[0021] If the two endpoints of the location interval are in different road segments, but there is no common neighborhood between the communication domain of the unmanned vehicle at the end of the road segment where the first endpoint is located and the communication neighborhood of the unmanned vehicle at the two endpoints, then the control messenger drone searches along the safe corridor formed between the two endpoints.

[0022] The safety corridor passes through the communication area of ​​the driverless vehicle at the end of the road segment where the previous endpoint is located.

[0023] The safety corridor passes through the communication neighborhood of the driverless vehicle at both ends.

[0024] Step S6 specifically also includes:

[0025] If the two endpoints of the location interval are within the same road segment, but there is no common neighborhood between the communication neighborhoods of the two endpoints, then the control messenger drone searches along a reliable path formed between the two endpoints.

[0026] The reliable path traverses the communication neighborhood of the autonomous vehicle at both endpoints.

[0027] A path planning device for a messenger drone under road network constraints includes a memory, a processor, and a program stored in the memory, characterized in that the processor executes the program to implement the method described above.

[0028] A storage medium having a program stored thereon, which, when executed, implements the method described above.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. By determining the access order and location of each unmanned vehicle by the messenger UAV, the complex multivariate coupled optimization problem is successfully decoupled, enabling deterministic access of each unmanned vehicle by the messenger UAV. This greatly reduces the amount of computation, improves efficiency, and reduces hardware costs.

[0031] 2. Simultaneously considering the constraints of the road network and speed in the environment where the unmanned vehicle is located, the communication constraints between the messenger drone and the unmanned vehicle, and the dynamic constraints of the messenger drone itself, the idea of ​​rolling time-domain optimization is fully utilized to realize the online planning of feasible paths by the messenger drone. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the main steps of the method of the present invention;

[0033] Figure 2 This is a flowchart of the messenger UAV path planning method under road network constraints provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the road network for unmanned vehicles in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram illustrating a reliable access strategy for unmanned vehicles by a messenger drone when the unmanned vehicle's location range is within the same road segment and there is a common neighborhood in an embodiment of the present invention.

[0036] Figure 5 This is a schematic diagram illustrating a reliable access strategy for unmanned vehicles by a messenger drone when the unmanned vehicle's location range is within the same road segment and there is no common neighborhood in an embodiment of the present invention.

[0037] Figure 6 This is a schematic diagram illustrating a reliable access strategy for the unmanned vehicle by a messenger drone when the vehicle's location range is in different road segments and there is a common neighborhood in an embodiment of the present invention.

[0038] Figure 7 This is a schematic diagram illustrating a reliable access strategy for the unmanned vehicle by a messenger drone when the vehicle's location range is in different road segments and there is no common neighborhood in an embodiment of the present invention.

[0039] Figure 8 This is a schematic diagram illustrating the path taken by the messenger drone in one periodic traversal of each unmanned vehicle in an embodiment of the present invention. Detailed Implementation

[0040] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0041] The path planning problem for messenger UAVs in a multi-point dynamic assembly mission can be described as follows:

[0042] Taking a firefighting mission in an urban environment as an example, the fire points are known to be distributed in different locations within the city. An air-ground collaborative system consisting of a messenger drone and multiple unmanned vehicles (UAVs) is responsible for completing the firefighting mission. The status of the fire points changes dynamically. Each UAV has a certain firefighting capability and makes dynamic decisions based on the status of each fire point and other UAVs, heading to different fire locations to carry out firefighting work. The mission ends when all fires are extinguished. Because the fire points are far apart and the communication range of each UAV is limited, direct communication between them is impossible. The messenger drone acts as a messenger, periodically traversing the communication neighborhood of each UAV, transmitting the status of the fire points and information about other UAVs. In this mission, due to uncertainties in the environment or its own movement, the messenger drone cannot accurately determine the location of the UAVs, thus failing to ensure successful re-access to each UAV. It is known that the UAVs always travel along passable sections of the road network, and their speeds vary within a finite range. The goal of the messenger drone is to plan the shortest path to ensure successful access to all UAVs.

[0043] In this embodiment of the invention, the messenger drone is modeled as a Dubins car subject to curvature constraints, and its kinematic model is as follows:

[0044]

[0045] Among them, (x A ,y A θ represents the position coordinates of the messenger drone. A Indicates the orientation angle, v A Indicates the speed of the messenger drone, r A Indicates the minimum turning radius of the messenger drone, u A Indicates control input, (x A ,y A ,θ A ) represents the state of the messenger drone, simplified to (P A ,h A ), where P A Represents coordinates, h A Indicates direction.

[0046] The model for the path planning problem of a messenger drone under uncertain motion conditions of an autonomous vehicle can be represented as:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] Where, N G S represents the number of driverless cars. * This indicates the order in which the messenger drone visits each unmanned vehicle, specifically... P * This indicates the location of each unmanned vehicle visited by the messenger drone. and These represent the initial position and initial orientation of the messenger drone, respectively. This indicates that the messenger drone is for the unmanned vehicle. i Access location within the communication neighborhood. Messenger drones for unmanned vehicles i+1 Access location within the communication neighborhood. Indicates in Driverless cars i Location coordinates, This indicates the communication radius of the messenger drone. Let represent the communication radius of the i-th autonomous vehicle, and T represent the set of revisit times. This indicates that the messenger drone is for the unmanned vehicle. i The time for the follow-up visit, This indicates that the messenger drone is for the unmanned vehicle. i The set of candidate access locations, This indicates that the messenger drone is for the unmanned vehicle. i The actual revisit path length.

[0055] In the constraints of the above problem model, the first term represents the motion model constraint of the messenger UAV, the second term represents the communication radius constraint, the third term represents the speed constraint of the UAV, the fourth term represents the revisit time constraint, the fifth term represents the revisit location constraint, and the sixth term represents the revisit distance constraint.

[0056] Based on the above path planning problem model, the path planning method for messenger UAVs under road network constraints provided in this embodiment of the invention is as follows: Figure 1 and Figure 2 As shown, the specific technical solution includes the following steps:

[0057] Step S1: Obtain the initial position and heading angle of the messenger UAV, the initial position and target position of each UAV, and the speed range of each UAV. Specifically, given the initial position of the messenger UAV... and heading angle Initial position of each autonomous vehicle and target location and the speed range of each autonomous vehicle Where N G Indicates the number of driverless cars;

[0058] Step S2: Obtain road network information and, based on the initial and target positions of each autonomous vehicle, determine the driving trajectory of each vehicle using the road network information. Specifically, assuming the autonomous vehicles always travel along the road network, the trajectory is determined based on the starting point of each vehicle. and target point Graph search algorithms are used to determine the driving paths of each autonomous vehicle on the road network, including:

[0059] Step S2.1: Input as follows Figure 3 The road network map shown consists of nodes and undirected edges. The autonomous vehicle can only start from the starting node, travel along a predetermined line segment, and reach the target node.

[0060] Step S2.2: via A * The algorithm calculates the shortest path for each autonomous vehicle, where the path consists of a sequence of traversed nodes.

[0061] Step S3: Determine the sequence numbers of all unmanned vehicles that the messenger drone has not visited during this period, and calculate the order in which the messenger drone visits each unmanned vehicle based on its current location using a heuristic method, including:

[0062] Step S3.1: Calculate the geometric center point based on the current position of each unmanned vehicle.

[0063]

[0064] Where, q i (i = 1, 2, ..., N) G ) represents the current position of the i-th unmanned vehicle, and q0 represents the current position of the messenger drone;

[0065] Step S3.2: Based on the geometric center point Calculate each q i Angle α relative to q0 i ;

[0066] Step S3.3: Adjust the angle Sort the data to obtain the final access order of the messenger drones to each unmanned vehicle. Assume... The access order is as follows

[0067] Step S4: Based on the currently accessed driverless car number s i and speed range Determine driverless car iThe lower bound Δt of the revisit time of the messenger drone for the unmanned vehicle while traveling along the road network. min and the upper bound of the revisit time Δt max The two together form a time interval, specifically including:

[0068] Step S4.1: Determine the time from the last time the messenger drone visited the unmanned vehicle to the time of the last visit to the unmanned vehicle s in the current cycle. i-1 The time it takes for the access to complete is denoted as t. AB ;

[0069] Step S4.2: Calculate the unmanned vehicle s i In t AB During this period at minimum speed The nearest location that can be reached by traveling along the road network The communication neighborhood corresponding to the location of the autonomous vehicle is denoted as

[0070] Step S4.3: Calculate the unmanned vehicle s i In t AB During this period at maximum speed The farthest point that can be reached by traveling along the road network Its corresponding communication neighborhood is denoted as

[0071] Step S4.4: According to The location of the beginning and end nodes of the road network segment is used to determine the location of the messenger drone. The boundary range during access is defined. Within this boundary range, N uniform sampling operations are performed to obtain N sampling points. Each sampling point can be represented as:

[0072]

[0073] in, and They represent The x and y coordinates, Indicates driverless car s i The communication radius is θ, where θ represents the sampling angle.

[0074] Step S4.5: Assume the driverless car travels at minimum speed from Continuing along the road network will... For each sampling point on the boundary, calculate the time it takes for the messenger drone to visit that moving sampling point.

[0075] Step S4.6: Calculate the minimum access time of the messenger drone to all sampling points.

[0076]

[0077] Step S4.7: Calculate the relationship between the messenger drone and the unmanned vehicle s i The lower bound Δt of the revisit time min :

[0078]

[0079] Step S4.8: According to The location of the beginning and end nodes of the road network segment is used to determine the location of the messenger drone. The boundary range during access is defined. Within this boundary range, N uniform sampling operations are performed to obtain N sampling points. Each sampling point can be represented as:

[0080]

[0081] in, and They represent The horizontal and vertical coordinates.

[0082] Step S4.9: Assume the driverless car is traveling at its maximum speed. from Continuing along the road network will... For each sampling point on the boundary, calculate the time it takes for the messenger drone to visit that moving sampling point.

[0083] Step S4.10: Calculate the maximum access time of the messenger UAV to all sampling points.

[0084]

[0085] Step S4.11: Calculate the message from the messenger drone to the unmanned vehicle s i The upper bound of the revisit time Δt max :

[0086]

[0087] Step S5: In the speed range and time interval [Δt] min ,Δt max Under the constraints of ], determine the unmanned vehicle s i The nearest and farthest positions reachable by uniform motion on the road network are denoted as the two endpoints of the position interval, respectively. and include:

[0088] Step S5.1: Calculate the unmanned vehicle s i at minimum speed At the minimum revisit time Δtmin Coordinates of the nearest location for travel along the inner road network:

[0089]

[0090] Step S5.2: Calculate the unmanned vehicle s i At maximum speed At the maximum revisit time Δt max Coordinates of the furthest point reached by travel along the inner road network:

[0091]

[0092] Step S6: Based on the two endpoints of the obtained location interval, determine the path trajectory for the messenger drone to revisit the unmanned vehicle that needs to be visited. Specifically:

[0093] 1) If and Located on the same road segment of the road network, and the driverless car i exist The formed communication neighborhood With driverless cars i exist The formed communication neighborhood There exists a common neighborhood S, such as Figure 4 As shown, if the drone passes through this public neighborhood, it can achieve [the following]: ... drone] can pass through this public neighborhood] to achieve [the following]: [the following]: [the following] i Repeat visits include:

[0094] Step S6.1.1: Calculation and The center P of the formed public neighborhood S P ;

[0095] Step S6.1.2: Based on the obtained access order, assume the unmanned vehicle s i+1 At maximum speed In t AB During this period, the location coordinates obtained by traveling along the road network are marked as P. P+1 ;

[0096] Step S6.1.3: Based on the obtained access order, the drone accesses the unmanned vehicle s i-1 The access location is denoted as P. P-1 Then we can calculate the angle α = ∠P P P P-1 P P+1 and β=∠P P P P+1 P P-1 ;

[0097] Step S6.1.4: Calculate P based on the angular relationship between α and β.γ Coordinates:

[0098]

[0099] in, This indicates the solution to P. P Online segment The function for projection onto the top, MidPoint(P) P-1 ,P P+1 ) indicates solving P P-1 and P P+1 A function that connects the midpoints of a line.

[0100] Step S6.1.5: Calculate P γ and P P The intersection point P of the line and the common neighborhood S S The intersection point P S These are the coordinates of the public neighborhood accessed by the drone;

[0101] Step S6.1.6: Calculate the drone's access to the unmanned vehicle s i The orientation angle θ at that time:

[0102]

[0103] 2) If and Located on the same road segment of the road network, but the driverless car... i exist The formed communication neighborhood With driverless cars i exist The formed communication neighborhood There is no common neighborhood between them, such as Figure 5 As shown, the drone follows the path of... and The search for reliable access paths between these paths enables the autonomous vehicle to perform searches. i Access includes:

[0104] Step S6.2.1: with Starting point As the endpoint, As the channel width, a straight-line search channel constrained by the communication radius is constructed. The UAV can only communicate with the unmanned vehicle if it always searches within the straight-line channel. i Encounter;

[0105] Step S6.2.2: Based on the vector The direction to determine the drone's position The boundary range during access is defined, and m uniform samplings are performed within this boundary range to obtain m sampling points. For each sampling point... It can be represented as:

[0106]

[0107] Where, φ m This indicates the corresponding sampling angle.

[0108] Step S6.2.3: Select sampling points in sequence Calculate the drone from the previous unmanned vehicle s i-1 End of visit location With an orientation angle h min to sampling point Path length and access time

[0109] Step S6.2.4: Determine the UAV's path along the vector The direction, from Departure and Communication neighborhood The coordinates of their positions at the time of their meeting are denoted as .

[0110] Step S6.2.5: Calculate the drone's... As the starting point, along arrive Path length and access time in Indicates that the drone is from arrive Search for driverless cars i The angle of orientation at that time;

[0111] Step S6.2.6: If For the selected sampling points Calculate the following Dubins path:

[0112]

[0113] in, Indicates drones Starting from the designated point, proceed to the next driverless vehicle to be visited. i+1 Location The length of the Dubins path at that time.

[0114] Step S6.2.7: Repeat steps S6.2.3 to S6.2.6, and record all sampling points that satisfy the conditions described in step S6.2.6. and the corresponding Dubins path length

[0115] Step S6.2.8: Compare the Dubins path lengths of all recorded sampling points. Select minimum path length corresponding sampling points As an alternative access point

[0116] Step S6.2.9: Based on the vector The direction to determine the drone's position The boundary range during access is defined, and m uniform samplings are performed within this boundary range to obtain m sampling points. For each sampling point... It can be represented as:

[0117]

[0118] Where, φ m This indicates the corresponding sampling angle.

[0119] Step S6.2.10: Select sampling points in sequence Calculate the drone from the previous unmanned vehicle s i-1 End of visit location With an orientation angle h max to sampling point Path length

[0120] Step S6.2.11: Determine the UAV's path along the vector The direction, from Departure and Communication neighborhood The coordinates of their positions at the time of their meeting are denoted as .

[0121] Step S6.2.12: Calculate the drone's... As the starting point, along arrive Path length in Indicates that the drone is from arrive Search for driverless cars i The angle of orientation at that time;

[0122] Step S6.2.13: For the selected sampling points Calculate the following Dubins path:

[0123]

[0124] in, Indicates drones Starting from the designated point, proceed to the next driverless vehicle to be visited. i+1 Location The length of the Dubins path at that time.

[0125] Step S6.2.14: Repeat steps S6.2.10 to S6.2.13 to record all sampling points. and the corresponding Dubins path length

[0126] Step S6.2.15: Compare the Dubins path lengths of all recorded sampling points. Select minimum path length corresponding sampling points As an alternative access point

[0127] Step S6.2.16: Compare Dubins path lengths and Choose the alternative access point corresponding to the smaller value as the final access point. drones Using this as the starting point for the search, and according to the corresponding access method, the search for the unmanned vehicle s is achieved. i Reliable access to the communication neighborhood;

[0128] 3) If and Located on different sections of the road network, determine the autonomous vehicle s i arrive The position coordinates of the last node of the road segment driverless cars The formed communication neighborhood is represented as

[0129] like and Located on different sections of the road network, and such as Figure 6 As shown, the communication neighborhood and If a common neighborhood exists among the three, then the drone can achieve control of the unmanned vehicle by traversing that common neighborhood. i Repeat visits include:

[0130] Step S6.3.1: Determine and Positional relationship, if and There is an intersection point and and intersection and If the points are the same, the drone will directly visit that intersection.

[0131] Step S6.3.2: If and There are two intersection points and and and There is one and only one intersection point The drone will then directly access the intersection.

[0132] Step S6.3.3: If and There are two intersection points and and and There is one and only one intersection point The drone will then directly access the intersection.

[0133] Step S6.3.4: If and There are two intersection points, and the common neighborhood formed by these two points is denoted as . and and Both have two intersection points, forming a common neighborhood. and Furthermore, among the two intersections with each communication neighborhood, there exists one in... On the border, one in Phenomena outside the borders, such as drone access, are governed by... and Forming a common neighborhood intersection;

[0134] Step S6.3.5: If and There are two intersection points, and the common neighborhood formed by these two points is denoted as . and and Both have two intersection points, forming a common neighborhood. and in and Both intersections are in Outside the border, and Both intersections are in At the border, drones can directly access public neighborhoods.

[0135] Step S6.3.6: If and There are two intersection points, and the common neighborhood formed by these two points is denoted as . and and Both have two intersection points, forming a common neighborhood. and in and Both intersections are in Outside the border, and Both intersections are in At the border, drones can directly access public neighborhoods.

[0136] Step S6.3.7: If and There are two intersection points, and the common neighborhood formed by these two points is denoted as . and and Both have two intersection points, forming a common neighborhood. and in and Both intersections are in Outside the boundary, and and Both intersections are also in Outside the boundary, drones can directly access public neighborhoods.

[0137] 4) If and Located on different sections of the road network, but such as Figure 7 As shown, the communication neighborhood and If there is no common neighborhood among the three, then the drone will travel along the path of... and Searching the safe corridor formed between them enables the autonomous vehicle to... i Access includes:

[0138] Step S6.4.1: Unmanned vehicle s i by Starting point As the endpoint, As the width of the corridor, a safe corridor constrained by the communication radius is constructed. The safe corridor is composed of multiple segments of the channel described in step S6.2.1. The drone can only communicate with the unmanned vehicle if it stays within the corridor. i Upon meeting, the drone cuts into the next section of the channel at the junction with the smallest turning circle.

[0139] Step S6.4.2: Determine the unmanned vehicle s i Along arrive Intermediate nodes required along the flight path and the communication neighborhood corresponding to each intermediate node Where n represents the number of intermediate nodes;

[0140] Step S6.4.3: Calculate the drone's... When departing, flying along the boundaries of each segment of the safety corridor and... The coordinates of the point of tangency are denoted as and This serves as the flight boundary for each segment of the passage.

[0141] Step S6.4.4: Based on the vector The direction to determine the drone's position The boundary range during access is defined, and m uniform samplings are performed within this boundary range to obtain m sampling points. For each sampling point... It can be represented as:

[0142]

[0143] Where, φ m This indicates the corresponding sampling angle.

[0144] Step S6.4.5: For any sampling point Computing drones The flight originates from the communication neighborhood of each intermediate node. At that time, and tangent point coordinates

[0145] Step S6.46: If the coordinates of the tangent point exist Not within the flight boundary of this section of the channel Inside, drones are used as sampling points. Search for driverless cars as a starting point i If the path is a non-flyable path, proceed to step S6.4.4 and select the next sampling point.

[0146] Step S6.4.7: If the coordinates of all tangent points All are within the flight boundaries of the corresponding channels. Inside, the drone calculates the speed of the previous unmanned vehicle. i-1 End of visit location With an orientation angle h min to sampling point Path length and access time t P1 ;

[0147] Step S6.4.8: Calculate the drone's data with all intermediate nodes. tangent point coordinates The length of the flight path to the waypoint is denoted as . and access time in Indicates that the drone departed from the sampling point The angle of orientation when entering the safety corridor. Indicates when the drone flies out of the safe corridor and The intersection;

[0148] Step S6.4.9: If For the selected sampling points Calculate the following Dubins path:

[0149]

[0150] in, Indicates drones Starting point, heading angle is Fly out of the security corridor and arrive at the next driverless car to visit. i+1 Location The length of the Dubins path at that time.

[0151] Step S6.4.10: Repeat steps S6.4.4 to S6.4.9, and record all sampling points that satisfy the conditions described in step S6.4.9. and the corresponding Dubins path length

[0152] Step S6.4.11: Compare the Dubins path lengths of all recorded sampling points. Select minimum path length corresponding sampling points As an alternative access point

[0153] Step S6.4.12: Calculate the drone's... When departing, flying along the boundaries of each segment of the safety corridor and... The coordinates of the point of tangency are denoted as and This serves as the flight boundary for each segment of the passage.

[0154] Step S6.4.13: Determine the unmanned vehicle s i arrive The position coordinates of the first node of the road segment And based on vector The direction to determine the drone's position The boundary range during access is defined, and m uniform samplings are performed within this boundary range to obtain m sampling points. For each sampling point... It can be represented as:

[0155]

[0156] Where, φ m This indicates the corresponding sampling angle.

[0157] Step S6.4.14: For any sampling point Computing drones The flight originates from the communication neighborhood of each intermediate node. At that time, and tangent point coordinates

[0158] Step S6.4.15: If the coordinates of the tangent point exist Not within the flight boundary of this section of the channel Inside, drones are used as sampling points. Search for driverless cars as a starting point i If the path is a non-flyable path, proceed to step S6.3.13 and select the next sampling point;

[0159] Step S6.4.16: If the coordinates of all tangent points All are within the flight boundaries of the corresponding channels. Inside, the drone calculates the speed of the previous unmanned vehicle. i-1 End of visit location With an orientation angle h max to sampling point Path length

[0160] Step S6.4.17: Calculate the drone's data with all intermediate nodes. tangent point coordinates The length of the flight path to the waypoint is denoted as . and access time in Indicates that the drone departed from the sampling point The angle of orientation when entering the safety corridor. Indicates when the drone flies out of the safe corridor and The intersection;

[0161] Step S6.4.18: For the selected sampling points Calculate the following Dubins path:

[0162]

[0163] in, Indicates drones Starting point, heading angle is Fly out of the security corridor and arrive at the next driverless car to visit. i+1 Location The length of the Dubins path at that time.

[0164] Step S6.4.19: Repeat steps S6.4.13 to S6.4.18 and record the sampling points. and the corresponding Dubins path length

[0165] Step S6.4.20: Compare the Dubins path lengths of all recorded sampling points. Select minimum path length corresponding sampling points As an alternative access point

[0166] Step S6.4.21: Compare Dubins path lengths and Choose the alternative access point corresponding to the smaller value as the final access point. drones Starting from the corresponding access method, the autonomous vehicle 's' can be deterministically searched. i To ensure compatibility with driverless cars i The encounter;

[0167] Step S7: Record the order in which the messenger drone accesses each unmanned vehicle and the sequence of visits to each unmanned vehicle within the current period. i The location of the access, based on the messenger drone's access to the unmanned vehicle. i The access time is used to update the location coordinates of other driverless vehicles;

[0168] Then repeat steps S3 to S6 above until the messenger drone has completed its visits to all unmanned vehicles in this cycle, and record the final visit order and the location of each unmanned vehicle visited.

[0169] Step S8: Reset the serial number of the unmanned vehicle that has completed the visit, and the messenger drone enters the next visit cycle. Repeat steps S3 to S7 until the preset maximum number of visit cycles is completed.

[0170] To verify the effectiveness of this invention, a simulation experiment was conducted to assess the path planning effect. The experiment used four unmanned vehicles (UAVs) and one messenger UAV to form an air-ground cooperative system. The initial position of the messenger UAV was (0,0), with an orientation angle of π / 2. The initial positions and target points of each UAV were located at different nodes in the road network. For example, UAV 1's initial coordinates were at node 13, its target position was at node 10, and its planned path was [13,11,10], with a speed range of [1m / s, 2m / s]. UAV 2's initial coordinates were at node 1, its target position was at node 4, and its planned path was [13,11,10], with a speed range of [1m / s, 2m / s]. The paths of each unmanned vehicle are [1,2,4], with a speed range of [2m / s,4m / s]. Unmanned vehicle 3's initial coordinates are at node 4, its target position is at node 7, and its planned path is [4,5,7] with a speed range of [1m / s,3m / s]. Unmanned vehicle 4's initial coordinates are at node 9, its target position is at node 7, and its planned path is [9,8,7] with a speed range of [2m / s,3m / s]. Each unmanned vehicle has a communication radius of 5m, while the messenger drone has a communication radius of 20m, a turning radius of 3m, and a speed of 10m / s. The paths taken by the messenger drone to complete one periodic traversal of each unmanned vehicle are as follows: Figure 8 As shown.

[0171] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

[0172] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A path planning method for a messenger UAV under road network constraints, characterized in that, include: Step S1: Obtain the initial position and heading angle of the messenger drone, the initial position and target position of each unmanned vehicle, and the speed range of each unmanned vehicle; Step S2: Obtain road network information, and based on the initial and target positions of each unmanned vehicle, obtain the driving trajectory of each unmanned vehicle based on the road network information; Step S3: Obtain all unvisited unmanned vehicles by the messenger drone during this period, and determine the access order for each unvisited unmanned vehicle based on its location. Step S4: Based on the speed range of the unmanned vehicle that needs to be accessed and its driving trajectory, determine the revisit time range of the messenger drone for the unmanned vehicle that needs to be accessed. Step S5: Based on the speed range and revisit time range of the autonomous vehicle to be visited, determine the location range of the autonomous vehicle to be visited, as well as the two endpoints of the location range; Step S6: Based on the two endpoints of the obtained location interval, determine the path trajectory of the messenger drone to revisit the unmanned vehicle that needs to be visited; Step S7: After updating the location of other unmanned vehicles, repeat steps S3 to S6 until all unmanned vehicles have been visited in this cycle. Step S6 specifically includes: If the two endpoints of the location interval are in the same road segment, and there is a common neighborhood between the communication neighborhoods of the two endpoints, then the control messenger drone will pass through this common neighborhood. Step S6 specifically includes: If the two endpoints of the location interval are in different road segments, determine the communication neighborhood of the unmanned vehicle at the end of the road segment where the previous endpoint is located. If there is a common neighborhood among the communication neighborhood of the unmanned vehicle and the communication neighborhood of the two endpoints, then control the messenger drone to pass through this common neighborhood. Step S6 specifically also includes: If the two endpoints of the location interval are in different road segments, but there is no common neighborhood between the communication domain of the unmanned vehicle at the end of the road segment where the first endpoint is located and the communication neighborhood of the unmanned vehicle at the two endpoints, then the control messenger drone searches along the safe corridor formed between the two endpoints. Step S6 specifically also includes: If the two endpoints of the location interval are within the same road segment, but there is no common neighborhood between the communication neighborhoods of the two endpoints, then the control messenger drone searches along a reliable path formed between the two endpoints.

2. The method for path planning of a messenger UAV under road network constraints according to claim 1, characterized in that, The safety corridor passes through the communication area of ​​the driverless vehicle at the end of the road segment where the previous endpoint is located.

3. The method for path planning of a messenger UAV under road network constraints according to claim 2, characterized in that, The safety corridor passes through the communication neighborhood of the driverless vehicle at both ends.

4. The method for path planning of a messenger UAV under road network constraints according to claim 1, characterized in that, The reliable path traverses the communication neighborhood of the autonomous vehicle at both endpoints.

5. A path planning device for a messenger UAV under road network constraints, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-4.

6. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-4.

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