An unmanned aerial vehicle online path planning method based on opportunistic data collection

Abstract

The present application relates to a kind of unmanned plane online path planning method based on opportunity data acquisition, belong to unmanned plane path planning and unmanned plane auxiliary wireless communication field.Unmanned plane is after executing given task, opportunity data acquisition is carried out in the way of return flight.The process that unmanned plane flies from last hovering point to next hovering point and completes the data acquisition of this point is called opportunity data acquisition once.Each opportunity data acquisition process, unmanned plane first collects nearby environment and ground node information;According to the information collected and its remaining time, construct feasible hovering point subset;According to the maximum principle of data acquisition volume, optimal hovering point is selected from subset.The trajectory that each optimal hovering point is connected constitutes the online planning path of unmanned plane.The present application makes full use of the surplus time of unmanned plane return flight to carry out opportunity data acquisition, through online planning and optimizing the flight trajectory of unmanned plane, increase the data acquisition volume of unmanned plane, improve the use efficiency of unmanned plane.

Description

Technical Field

[0001] This invention belongs to the field of UAV path planning and UAV-assisted wireless communication, and relates to an online path planning method for UAVs based on opportunistic data acquisition. Background Technology

[0002] In recent years, rotary-wing drones have been frequently deployed for various missions due to their high flexibility and excellent line-of-sight links, such as patrolling remote areas or search and rescue. During missions or on their return journey, these drones can act as short-term data collectors, incidentally gathering data. This type of data collection, also known as opportunistic data collection, requires no additional drone deployment, greatly improving drone efficiency and reducing energy consumption. However, because short-term data collection is often an ad-hoc task and not frequently included in the drone's prescribed mission, the environment encountered by the drone when collecting opportunistic data is often unknown or rudimentary.

[0003] Currently, a few studies have conducted preliminary research on path planning for UAVs in partially or completely unknown environments. The main idea is to adjust the UAV's flight speed and hovering time in real time based on detected environmental data and network parameters, without altering the UAV's return flight path, thereby optimizing the opportunistic data acquisition process. However, due to specific mission requirements and flight safety constraints, the UAV's flight path and return time are strictly limited, resulting in limited coverage during data acquisition. How to optimize the UAV's data acquisition process and online trajectory in vast unknown environments to improve acquisition efficiency is a problem worthy of further research. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an online path planning method for unmanned aerial vehicles (UAVs) based on opportunistic data acquisition. This method calculates a subset of feasible hovering points based on detected environmental data and remaining time, and selects the optimal hovering point by combining the amount of data that can be collected at each hovering point. These optimal hovering points constitute the online planned path for UAV opportunistic data acquisition.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An online path planning method for unmanned aerial vehicles (UAVs) based on opportunistic data acquisition, comprising the following steps:

[0007] S1: Explore environmental information. After completing a given task, the rotary-wing UAV collects opportunistic data during its return journey. Assume the maximum opportunistic data collection time for the UAV is T. max During data acquisition, at a speed v mFlying at a constant speed, with a constant altitude z0, the process of the UAV taking off from the previous hovering point and flying to the next hovering point to complete data collection at that point is called an opportunistic data collection. Before each data collection, the UAV communicates with nearby ground nodes at the corresponding hovering point to collect information about the nodes and the surrounding environment, and records it.

[0008] S2: Establish a set of feasible hovering points. Assume the UAV performs N opportunistic data collections during its return journey, starting from its entry into the data collection area. For the i-th (i = 1, 2, ..., N) opportunistic data collection, the UAV calculates the hovering point P for the i-th data collection based on the collected information and its remaining time. ik The parameters are used to construct a feasible hovering point subset H. i .

[0009] S3: Select the optimal hovering point. Based on the principle of maximizing the amount of data that can be collected, select the optimal hovering point. If the feasible hovering point subset H... i If the value is not empty, calculate the amount of data that can be collected at each feasible hovering point, and select the hovering point with the largest amount of collected data as the optimal hovering point P. i * If H i If the value is empty, the drone will return directly to the base station from its current location, ending the opportunity data collection. The optimal hovering points from each opportunity data collection session constitute the drone's online planned path.

[0010] Furthermore, in the opportunity data collection process described in step S1, such as Figure 1 As shown, after completing a given task, the rotary-wing UAV performs opportunistic data collection during its return journey. Assume the given return time for the UAV is T. g During the return journey, at a speed of v m The drone flies at a constant speed, with its altitude z0 remaining constant. When the drone enters an unknown ground network at time T0, what is the maximum time T that the drone can use for opportunistic data acquisition? max It can be represented as: T max =T g-T0. Because drones cannot obtain sufficient global information in unknown environments, they can only adjust their flight path and data collection area online based on real-time collected information. The process of a drone flying from one hovering point to the next and completing data collection at that point is called an opportunistic data collection. Assuming the drone performs N data collections during its return journey, for the i-th data collection process, the drone flies at a constant speed above the hovering point, hovers there to collect data, and probes the environmental information needed for the next data collection. Based on the probed data and its remaining flight time, it selects the optimal hovering point for the next data collection process. After completing the data collection, it leaves the hovering point. If an optimal hovering point for the next data collection exists, the drone flies at a constant speed to the next hovering point and repeats the above process; otherwise, the drone flies back to the base station to recharge and submit the collected data. Starting from an unknown area, the optimal hovering points the drone flies through constitute the online planned path for opportunistic data collection. This scheme, while ensuring the drone can safely return to the base station, breaks through the original fixed flight path, allowing the drone to collect data from more ground nodes and improving data collection efficiency.

[0011] Furthermore, in step S2, the hovering point P of the i-th data acquisition is calculated. ik The parameters and the construction of a feasible subset of hovering points H i The process is as follows:

[0012] (1) Service time. Assume that during the i-th data acquisition process, the UAV reaches the k-th (k = 1, 2, ..., K) hovering point P. ik The total flight time is The data collection time at the kth hovering point is Ignoring acceleration and deceleration time during hovering, it starts from the previous optimal hovering point. To the current k-th hovering point P ik Service time t ik for:

[0013]

[0014] (2) Flight time. Because the drone travels at speed v during its return journey... m Assuming uniform flight, neglecting acceleration and deceleration, its flight time depends on the position of the hovering point. and P ik The coordinates are respectively: (x i-1 ,y i-1 ,z0)(x ik ,y ik If z0), then the drone will start from... To P ik The flight time is expressed as:

[0015]

[0016] (3) Data Acquisition Time. Data acquisition time mainly depends on the transmission rate between the hovering point and the ground node, and the amount of data to be uploaded by the ground node. Assume that in the i-th data acquisition, the UAV needs to collect N data at the k-th hovering point. ik Data for ground nodes, ground node j (j = 1, 2, ..., N) ik The amount of data to be uploaded is f kj The data collection time of the UAV at the kth hovering point is expressed as:

[0017]

[0018] Among them, R kj Let the data transmission rate between node j and the k-th hovering point of the UAV at bandwidth B be expressed as:

[0019] R kj =Blog2(1+γ) kj (4)

[0020] Where, γ kj Let the signal-to-noise ratio between node j and the UAV at the k-th hovering point be expressed as:

[0021]

[0022] Among them, P kj Let J be the transmit power of node j. and μ represents the transmission probability of line-of-sight and non-line-of-sight links, respectively. LoS and μ NLoS σ represents the attenuation factor for line-of-sight and non-line-of-sight links, respectively. 2 Let f be the variance of additive white Gaussian noise. c Where c is the carrier frequency, c0 is the speed of light, and d is the carrier frequency. kj Let P be the distance between node j and the drone at the k-th hovering point. kj =(x kj ,y kj If ,0), then d kj Represented as:

[0023]

[0024] (4) Establish a feasible subset of hovering points. To ensure that the UAV can fly back to the base station within a given time, the UAV must satisfy the following conditions when flying to the k-th hovering point to collect data:

[0025]

[0026] Among them, tn This represents the service time consumed by the UAV in the nth (n = 1, 2, ..., i-1) data collection session. Let P be the time required for the drone to fly back to the base station from the kth hovering point. bs Coordinates are (x bs ,y bs If ,0), then for

[0027]

[0028] If the drone flies from its current position to hovering point P ik If the data collection time satisfies the above formula, then mark P. ik For feasible hovering points, add them to the feasible hovering point subset H. i .

[0029] Furthermore, the strategy for selecting the optimal hovering point for the UAV in S3 during the i-th opportunity data acquisition is as follows:

[0030] If the feasible hovering point subset H constructed in S2 i If H is empty, the drone will fly directly from its current location to the base, ending the opportunity data collection; if H is empty... i Not empty, such as Figure 2 As shown, the optimal hovering point P is selected based on maximizing the amount of data collected at feasible hovering points. i * , is represented as:

[0031]

[0032] By solving the above optimization problem using intelligent algorithms, the optimal hovering point of the UAV during the i-th opportunity data acquisition in an unknown environment is obtained.

[0033] The trajectory formed by connecting all the optimal hovering points is the online planning path for UAV opportunistic data collection.

[0034] The beneficial effects of this invention are as follows: When the data collection volume and utilization efficiency of a drone during opportunistic data acquisition are relatively low due to a fixed return route, the drone can break through the fixed return route and optimize the opportunistic data acquisition process in a wider, unknown environment. This invention, while ensuring the drone can safely reach the base station, plans the drone's return path online in unknown environments, enabling the drone to collect as much data as possible, thereby improving the drone's data acquisition efficiency.

[0035] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0037] Figure 1 This is a diagram of an online path planning model for unmanned aerial vehicles (UAVs) based on opportunity data collection.

[0038] Figure 2 A schematic diagram illustrating the search process for the optimal hovering point during the i-th opportunity data acquisition; Detailed Implementation

[0039] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0040] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0041] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0042] This invention is implemented through an online path planning system for unmanned aerial vehicles (UAVs) based on opportunistic data acquisition. For example... Figure 1 As shown, after completing a given task, a rotary-wing UAV performs opportunistic data collection on its return journey. Because the UAV cannot obtain sufficient global information in an unknown environment, it can only adjust its flight path and data collection area online based on real-time collected information. The process of the UAV flying from one hovering point to the next and completing data collection at that point is called an opportunistic data collection. Assuming the UAV performs N data collections during its return journey, for the i-th data collection process, the UAV flies at a constant speed above the hovering point, hovers there to collect data, and probes the environmental information needed for the next data collection. Based on the probed data and its remaining flight time, it selects the optimal hovering point for the next data collection process. After completing the data collection, it leaves the hovering point. If an optimal hovering point for the next data collection exists, the UAV flies at a constant speed to the next hovering point and repeats the above process; otherwise, the UAV returns to the base station to recharge and submit the collected data. Starting from an unknown area, the optimal hovering points the UAV flies through constitute the online planned path for opportunistic data collection.

[0043] like Figure 2 As shown, after the UAV enters an unknown network, it performs N data collections during its return journey. For the i-th data collection, the UAV searches for nearby nodes and communicates with nodes within its coverage area. Through communication between ground nodes, it obtains information such as the distribution of ground nodes and the number of service nodes around the hovering point. Based on the collected information, the UAV establishes a node topology table of the surrounding environment and calculates the hovering point P of the i-th data collection. ik The parameters are determined and a feasible subset of hover points is constructed.

[0044] During the i-th data acquisition process, to ensure that the UAV can fly back to the base station within a given time, if a hovering point is found that satisfies the following constraints, it is marked as a feasible hovering point and added to the hovering point subset H. i middle.

[0045]

[0046] Where t n This represents the service time consumed by the UAV in the nth (n = 1, 2, ..., i-1) data collection iteration. Let P be the time required for the drone to fly back to the base station from the k-th (k = 1, 2, ..., K) hovering point. bs Coordinates are (x bs ,y bs If ,0), then It can be represented as: And t ikFor the drone to hover from the previous optimal hovering point To the current k-th hovering point P ik The service time, ignoring the acceleration and deceleration time during hovering, includes the time from the drone to the k-th hovering point P. ik The total flight time is The time to collect data at the kth hovering point is

[0047] (1) Flight time. Because the drone travels at speed v during its return journey... m Assuming uniform flight, neglecting acceleration and deceleration, its flight time depends on the position of the hovering point. and P ik The coordinates are respectively: (x i-1 ,y i-1 ,z0)(x ik ,y ik If z0), then the drone will start from... To P ik The flight time is expressed as:

[0048]

[0049] (2) Data Acquisition Time. Data acquisition time mainly depends on the transmission rate between the hovering point and the ground node, and the amount of data to be uploaded by the ground node. During the hovering phase, the signal-to-noise ratio γ between node j and the UAV at the k-th hovering point... kj It can be represented as:

[0050]

[0051] Among them, P kj Let J be the transmit power of node j. and μ represents the transmission probability of line-of-sight and non-line-of-sight links, respectively. LoS and μ NLoS σ represents the attenuation factor for line-of-sight and non-line-of-sight links, respectively. 2 Let f be the variance of additive white Gaussian noise. c Where c is the carrier frequency, c0 is the speed of light, and d is the carrier frequency. kj Let P be the distance between node j and the drone at the k-th hovering point. Assume the coordinates of node j are P. kj =(x kj ,y kj If ,0), then

[0052] Therefore, the data transmission rate R between node j and the k-th hovering point of the UAV when the bandwidth is B is... kj It can be represented as:

[0053] R kj=Blog2(1+γ) kj (4)

[0054] Assume that the UAV needs to collect N data at the k-th hovering point during the i-th data acquisition. ik Data for ground nodes, ground node j (j = 1, 2, ..., N) ik The amount of data to be uploaded is f kj The data collection time of the UAV at the k-th hovering point is expressed as:

[0055]

[0056] Finally, if feasible, the subset of hovering points H i If H is empty, the drone will fly directly from its current location to the base, ending the opportunity data collection; if H is empty... i If not empty, calculate the amount of data that the drone can collect at each feasible hovering point in the set, and select the hovering point with the largest amount of collectable data as the optimal hovering point P. i * , is represented as:

[0057]

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for online path planning of unmanned aerial vehicles (UAVs) based on opportunistic data acquisition, characterized in that: Includes the following steps: S1: Explore environmental information; After completing a given task, the rotary-wing UAV collects opportunistic data during its return journey; assuming the maximum given time for opportunistic data collection by the UAV is... During the data acquisition process, speed is paramount. Uniform speed flight, flight altitude Unchanged; After entering the unknown area, the process of the UAV flying from the previous hovering point to the next hovering point and completing the data collection at that point is called an opportunistic data collection; Before each data collection, the UAV communicates with nearby ground nodes at the corresponding hovering point to collect information about the nodes and the surrounding environment, and records it. S2: Establish a set of feasible hovering points; assuming the drone performs a total of [number] hovering points during the entire return journey, starting from entering the data collection area. Second opportunity for data collection; for the first Second opportunity data collection The drone calculates the number of seconds based on the information it has collected and its remaining time. Hover point of secondary data acquisition The parameters are used to construct a subset of feasible hovering points. ; In S2, the feasible hovering point subset The construction process is as follows: (1) Service time; assuming it is on the 1st During the second data collection process, the drone reached the... hovering point The total flight time is , In the The data collection time at each hovering point is [time]. Ignoring acceleration and deceleration time during hovering, it starts from the previous optimal hovering point. Up to the current number hovering point Service Hours for: （1） (2) Flight time; due to the high speed of the drone during the return journey. Assuming uniform flight, neglecting acceleration and deceleration, its flight time depends on the position of the hovering point. and The coordinates are as follows: , Then the drone from arrive The flight time is expressed as: (2) (3) Collection time; The data collection time depends on the transmission rate between the hovering point and the ground node, and the amount of data to be uploaded by the ground node; assuming the UAV is in the [missing information]... The first data collection in the second Each hovering point needs to be collected. Data from each ground node, ground node The amount of data to be uploaded is , Then the drone in the 1st The acquisition time for each hovering point is expressed as: (3) in, For nodes With bandwidth Time and Drones The data transmission rate of each hovering point is expressed as: (4) in, For nodes With drones in The signal-to-noise ratio at each hovering point is expressed as: (5) in, For nodes The transmission power, and These represent the transmission probabilities of line-of-sight and non-line-of-sight links, respectively. and These represent the attenuation factors for line-of-sight and non-line-of-sight links, respectively. The variance of additive white Gaussian noise, For carrier frequency, At the speed of light, For drones in the hovering points and nodes The distance between them; let the node be The coordinates are ,but Represented as: (6) (4) Establish a feasible subset of hovering points; to ensure that the UAV can fly back to the base station within a given time, the UAV flies to the first... Data collected at each hovering point must meet the following requirements: (7) in, For drones in the Service time during this data collection. , For drones from the first The time required for each hovering point to fly back to the airspace above the base station, if the base station Coordinates ,but for (8) If the drone flies from its current location to the hovering point If the data collection time satisfies the above formula, then it is marked. Add feasible hovering points to the subset of feasible hovering points. ; S3: Select the optimal hovering point; based on the principle of maximizing the amount of data that can be collected, select the optimal hovering point; if a subset of hovering points is feasible... If the value is not empty, calculate the amount of data that can be collected at each feasible hovering point, and select the hovering point with the largest amount of collected data as the optimal hovering point. ;like If the value is empty, the drone will return directly to the base station from its current location, and the opportunity data collection will end. The trajectory formed by connecting the optimal hovering points of each opportunity data collection session constitutes the online planning path of the drone.

2. The online path planning method for unmanned aerial vehicles based on opportunistic data acquisition according to claim 1, characterized in that: In S3, the strategy for selecting the optimal hovering point from the set of feasible hovering points is as follows: If feasible, a subset of hovering points If empty, the drone will fly directly from its current location to the base, ending the opportunity to collect data; if... If the value is not empty, the optimal hovering point is selected based on the maximum amount of data collected at feasible hovering points. , represented as: (9) The path formed by connecting all the optimal hovering points is the online planning path for UAV opportunistic data collection.

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