Information collection method for perception area and related device

Abstract

The information collection method of the sensing area and the related equipment provided by the embodiments of the present application, the sensing area includes a plurality of sensor nodes and at least one obstacle, the method is applied to a UAV, and the method comprises the following steps: acquiring a starting position and an end position of the UAV, and acquiring a node position of each sensor node and overall obstacle information of the at least one obstacle; generating a plurality of safe running spaces from the starting position to the end position based on the node position and the overall obstacle information; acquiring a smoothing optimization model, acquiring, in each safe running space in sequence according to a flight sequence of the UAV, a running parameter of the UAV, and acquiring a data interaction parameter between the UAV and the sensor nodes, solving the smoothing optimization model to obtain a stage optimization control point, and controlling the UAV to perform data collection according to the stage optimization control point, thereby effectively improving the reliability, running safety and smoothness of the UAV in the sensing area for information collection.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method for collecting information about a sensing area and related equipment. Background Art

[0002] In recent years, drones have been widely used in military and civilian fields such as communications, rescue, monitoring, and photography due to their high maneuverability and flexible deployment and adjustment. Drone trajectory planning for IoT data collection is a research hotspot in agricultural or industrial fields such as energy-saving communications, intelligent monitoring, and intelligent management, and numerous researchers from all over the world have conducted extensive research on this topic.

[0003] Related art drone control methods for operating a drone and collecting information between multiple communication nodes typically plan the drone's operations based on the drone's efficiency in collecting information between the communication nodes and its flight efficiency. The drone is then controlled to collect information from the multiple communication nodes based on the planned operations data. However, due to the common presence of obstacles in real-world applications, using these conventional drone operation planning methods for information collection can easily lead to safety issues such as collisions with obstacles during operation. Summary of the Invention

[0004] The embodiments of the present application provide a method and related equipment for collecting information in a perception area, which can improve the safety of drones collecting information in the perception area.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for collecting information in a sensing area, wherein the sensing area includes multiple sensor nodes and at least one obstacle. The method is applied to a drone and includes:

[0006] Obtaining a starting position and an ending position of the UAV, as well as a node position of each sensor node and overall obstacle information of at least one obstacle;

[0007] generating a plurality of safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information;

[0008] Acquire a smoothing optimization model, wherein the smoothing optimization model includes a data acquisition constraint and a smoothing optimization objective function;

[0009] According to the flight sequence of the UAV, the operating parameters of the UAV and the data interaction parameters between the UAV and the sensor node are obtained one by one in each of the safe operating spaces. Based on the operating parameters and the data interaction parameters, the smoothing optimization model is solved to obtain the stage optimization control points, and the UAV is controlled to collect data according to the stage optimization control points.

[0010] In some embodiments, generating a plurality of safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information includes:

[0011] determining an obstacle surface position of at least one of the obstacles based on the overall obstacle information;

[0012] Generating a shortest path between every two adjacent node positions one by one, wherein the shortest path includes a plurality of path points;

[0013] Each of the path points is used as the expansion center point of the expansion rectangle one by one, and the rectangle is expanded based on the expansion center point until the expansion rectangle reaches the surface position of the obstacle, and the expansion rectangle is used as the safe operation space.

[0014] In some embodiments, obtaining the smooth optimization model includes:

[0015] Obtain running variable parameters, data interaction variable parameters and control point parameters;

[0016] generating a Bezier motion trajectory function based on the motion variable parameters and the control point parameters, and generating the smoothing optimization objective function based on the Bezier motion trajectory function;

[0017] generating a channel capacity function based on the data interaction variable parameter and the Bessel motion trajectory function, and generating the data acquisition constraint based on the channel capacity function and the data interaction variable parameter;

[0018] The smoothing optimization model is generated based on the control point parameters, the smoothing optimization objective function, and the data acquisition constraints.

[0019] In some embodiments, the running variable parameters include running time and running direction, the Bezier running trajectory function includes running curves of multiple stages, and generating the Bezier running trajectory function based on the running variable parameters and the control point parameters includes:

[0020] Obtaining a curve scale factor corresponding to each of the stages and a stage initial time of each stage;

[0021] Obtaining Bezier curve parameters based on the difference between the running time and the initial time of the stage and dividing by the curve scaling factor;

[0022] The running curve of each stage is generated based on the running direction, the curve scale factor, the control point parameters, the Bezier curve, and the Bezier curve parameters.

[0023] In some embodiments, generating the smoothing optimization objective function based on the Bessel trajectory function includes:

[0024] Obtaining the transformation matrix for each of the stages;

[0025] Performing a cubic derivation process on the operation curve, performing a square process, and then performing an integration process to obtain a smooth integral function of each stage;

[0026] Obtaining a smooth transition matrix based on the smooth integral function and the control matrix;

[0027] Based on the smooth transition matrix, a smooth integral matrix is ​​obtained;

[0028] The smooth optimization objective function of each stage is obtained based on multiplying the smooth transition matrix by the smooth integral matrix and then multiplying by the transposed matrix of the smooth transition matrix.

[0029] In some embodiments, the data interaction variable parameter includes the transmission power of the sensor node, and generating the channel capacity function based on the data interaction variable parameter and the Bessel trajectory function includes:

[0030] Determining a distance function between the UAV and the node position based on the running time, the Bessel running trajectory function, and the node position;

[0031] generating a large-scale fading function based on the distance function and the path loss exponent, and generating a channel gain function between the UAV and the sensor node based on the large-scale fading function, the Rayleigh fading function, and the Rice fading function;

[0032] The channel capacity function is obtained based on the product of the channel gain function and the transmit power.

[0033] In some embodiments, the data interaction variable parameters further include the amount and time interval of data collected by the drone at each stage, the channel capacity function includes a real-time channel function and a predicted channel function, and generating the data acquisition constraints based on the channel capacity function and the data interaction variable parameters includes:

[0034] Accumulating all the data collection amounts before the current moment to obtain a first information data collection amount;

[0035] obtaining a second information data collection amount based on a product of the real-time channel function and the time interval, and obtaining a third information data collection amount based on a product of the predicted channel function and the time interval;

[0036] The first information data collection amount, the second information data collection amount, and the third information data collection amount are accumulated to obtain a collectible data amount, and the data acquisition constraint is generated based on a size relationship between the collectible data amount and a minimum data collection amount.

[0037] In some embodiments, before solving the smoothing optimization model based on the operating parameters and the data interaction parameters to obtain the stage optimization control points, the method further includes:

[0038] Obtaining regional obstacle information, and determining updated obstacle surface positions of a plurality of obstacles close to the drone based on the regional obstacle information;

[0039] The safe operating space is updated based on the updated obstacle surface position.

[0040] To achieve the above objectives, a third aspect of an embodiment of the present application provides an information collection device for a sensing area, wherein the sensing area includes multiple sensor nodes and at least one obstacle. The device is applied to a drone, and includes:

[0041] A node position acquisition module, configured to acquire the node position of each sensor node and overall obstacle information of at least one obstacle, wherein the node position includes a starting position and an ending position;

[0042] a space generation module, configured to generate a plurality of safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information;

[0043] A model acquisition module acquires a smoothing optimization model, wherein the smoothing optimization model includes data acquisition constraints and a smoothing optimization objective function;

[0044] An operation control module is used to obtain the operation parameters of the drone and the data interaction parameters between the drone and the sensor node in each of the safe operation spaces according to the flight sequence of the drone, solve the smoothing optimization model based on the operation parameters and the data interaction parameters to obtain the stage optimization control points, and control the drone to collect data according to the stage optimization control points.

[0045] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the information collection method of the sensing area as described in the first aspect when executing the computer program.

[0046] To achieve the above object, a fourth aspect of the embodiments of the present application provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the information collection method of the sensing area as described in the first aspect.

[0047] The information collection method of the sensing area and the related device provided by the embodiments of the present application, the sensing area includes a plurality of sensor nodes and at least one obstacle, the method is applied to a UAV, and the method comprises the following steps: firstly, obtaining a starting position and an end position of the UAV, and obtaining a node position of each sensor node and overall obstacle information of the at least one obstacle; then, generating a plurality of safe running spaces from the starting position to the end position based on the node position and the overall obstacle information; next, obtaining a smoothing optimization model, the smoothing optimization model comprising a data acquisition constraint and a smoothing optimization objective function; finally, according to a flight sequence of the UAV, obtaining a running parameter of the UAV in each safe running space one by one, and obtaining a data interaction parameter between the UAV and the sensor nodes, solving the smoothing optimization model based on the running parameter and the data interaction parameter to obtain a stage optimization control point, and controlling the UAV to perform data collection according to the stage optimization control point. The embodiments of the present application utilize the node position of the plurality of sensor nodes and the obstacle information to pre-divide a plurality of safe running spaces in which no collision occurs for the UAV, and then utilize the smoothing optimization model obtained by the data acquisition constraint and the smoothing optimization objective function to perform running planning of the UAV in each safe running space one by one, so as to effectively improve the smoothness of the UAV in the running process while ensuring the reliability of the UAV in performing information collection in the sensing area and the running safety of the UAV in the running area.

[0048] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a structural schematic diagram of a sensing area provided by an embodiment of the present application.

[0050] Figure 2 is a flowchart of an information collection method of a sensing area provided by another embodiment of the present application.

[0051] Figure 3 This is a schematic diagram of the shortest path in a perception area provided by another embodiment of the present application.

[0052] Figure 4 This is a schematic diagram of a safe operating space in a perception area provided by another embodiment of the present application.

[0053] Figure 5 This is a structural diagram of an information collection device for a sensing area provided in another embodiment of the present application.

[0054] Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0056] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0058] First, let’s analyze some of the terms used in this application:

[0059] Drone communication refers to the transmission and exchange of information between drones or between drones and ground stations. Drone communication can be achieved through radio waves, satellite communications, fiber optic communications, and other means. Drone communication allows drones to collect information sent by ground nodes during flight.

[0060] Trajectory optimization involves finding the optimal trajectory under given conditions through algorithms and optimization methods. Trajectory optimization can be applied across a variety of fields, including drone flight, robotic path planning, and transportation scheduling. In drone flight, trajectory optimization can be used to improve flight efficiency, reduce energy consumption, and avoid obstacles.

[0061] A Bezier curve is a mathematical curve whose shape is determined by several control points. The starting and ending points are called endpoints, and the other control points are called anchor points. The shape of the curve can be changed by adjusting the position and weight of the anchor points.

[0062] In recent years, drones have been widely used in military and civilian fields such as communications, rescue, monitoring, and photography due to their high maneuverability and flexible deployment and adjustment. Drone trajectory planning for IoT data collection is a research hotspot in agricultural or industrial fields such as energy-saving communications, intelligent monitoring, and intelligent management, and numerous researchers from all over the world have conducted extensive research on this topic.

[0063] Related art drone control methods for operating a drone and collecting information between multiple communication nodes typically plan the drone's operations based on the drone's efficiency in collecting information between the communication nodes and its flight efficiency. The drone is then controlled to collect information from the multiple communication nodes based on the planned operations data. However, due to the common presence of obstacles in real-world applications, using these conventional drone operation planning methods for information collection can easily lead to safety issues such as collisions with obstacles during operation.

[0064] In order to improve the safety of information collection by drones within the perception area, the embodiment of the present application utilizes the node positions and obstacle information of multiple sensor nodes to pre-divide the drone into multiple safe operating spaces where collisions will not occur, and then utilizes a smooth optimization model obtained by data acquisition constraints and smooth optimization objective functions to plan the operation of the drone in each safe operating space one by one, thereby effectively improving the smoothness of the drone during operation while ensuring the reliability of information collection by the drone within the perception area and the safety of the drone's operation within the operation area.

[0065] In order to better describe the information collection method of the perception area provided by this application, the following first describes the perception area applied to the information collection method of the perception area. Figure 1 , is a schematic diagram of the structure of a sensing area provided in an embodiment of the present application. Figure 1 As shown, the communication system includes a starting position and an end position of the UAV, multiple sensor nodes, and at least one obstacle. When collecting information in the sensing area, the UAV begins its operation at the starting position, passes by each sensor node in sequence, and finally reaches the end position. During this operation, the UAV interacts with the sensor nodes to collect information.

[0066] Based on the above-mentioned perception area, the information collection method and related equipment of the perception area provided in the embodiment of the present application will be further described below. The information collection method of the perception area provided in the embodiment of the present application can be applied to drones or to control nodes connected to drones.

[0067] The following will describe in detail the information collection method of the perception area in the embodiment of the present application. Figure 2 , which is an optional flow chart of the method for collecting information about the sensing area provided in an embodiment of the present application, Figure 2 The method may include but is not limited to steps 100 to 400. It is also understood that this embodiment is Figure 2 The order of steps 100 to 400 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0068] Step 100: Obtain the starting position and the ending position of the UAV, as well as the node position of each sensor node and the overall obstacle information of at least one obstacle.

[0069] The following describes step 100 in detail.

[0070] In some embodiments, after responding to information collection requests from multiple sensor nodes within a sensing area, the drone must pre-acquire the node locations of all sensor nodes within the sensing area that require information collection, as well as globally obtain overall obstacle information for at least one obstacle, in order to effectively and safely operate within the complex environment of the sensing area and collect information from all sensor nodes that require information collection. It is understood that the initially acquired overall obstacle information is relatively rough and may be incomplete. Therefore, it is necessary to obtain localized obstacle information in real time during subsequent drone operation to supplement the relevant obstacle information.

[0071] Furthermore, the drone will travel from its starting position to its final position. It should be understood that the starting and final positions are spatial nodes and are not subject to any constraints in this embodiment. The starting and final positions can also be located at any sensor node or at additional spatial nodes. Furthermore, the obstacle and node positions represent a three-dimensional spatial range with a volume, not just a single location point.

[0072] Step 200: Generate multiple safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information.

[0073] Step 200 is described in detail below.

[0074] In some embodiments, to enable a drone to effectively acquire information within a complex perception area, it is necessary to first generate multiple collision-free safe operating spaces between the starting and ending locations based on pre-acquired node information and overall obstacle information. This facilitates subsequent operation planning within these multiple safe operating spaces, ensuring the drone's operational safety. The following describes the process of constructing these safe operating spaces in detail.

[0075] The process of generating a plurality of safe operation spaces from a starting position to an end position based on the node positions and the overall obstacle information includes the following steps 210 to 230 .

[0076] Step 210: Determine the obstacle surface position of at least one obstacle based on the overall obstacle information.

[0077] Step 220: Generate the shortest path between every two adjacent node positions one by one.

[0078] Step 230: Each path point is used as the expansion center point of the expansion rectangle one by one, and the rectangle is expanded based on the expansion center point until the expansion rectangle reaches the obstacle surface position, and the expansion rectangle is used as the safe operation space.

[0079] Steps 210 to 230 are described in detail below.

[0080] In some embodiments, Figure 1 In the perception area shown, the obstacle surface position of each known obstacle in three-dimensional space is first determined based on the overall obstacle information, and then the shortest path between each two adjacent node positions is generated one by one using a commonly used shortest path algorithm (such as the A* algorithm) based on all node positions and obstacle surface positions. Figure 3 , is a schematic diagram of the shortest path in a sensing area provided by an embodiment of the present application. Figure 3 As shown in , using the starting position Q1, the ending position Q2, multiple obstacle positions and multiple node positions (G1, G2 and G3), multiple shortest paths are generated for the drone to reach the ending position Q2 after starting from the starting position Q1 without touching the obstacle position and passing through multiple node positions (G1, G2 and G3) in sequence.

[0081] After obtaining multiple shortest paths, each path point in each shortest path is used as the center point of the expansion rectangle, and the rectangle is expanded based on the grid corresponding to the expansion center point until the expansion rectangle reaches the obstacle surface. The expansion rectangle is used as the safe operation space. If the expansion space of multiple path points is the same, a "pruning" operation is performed, and only the expansion space of one path point needs to be retained. Each rectangle in the figure corresponds to a corridor, and a series of corridors form a feasible channel. Figure 4 , is a schematic diagram of a safe operating space in a sensing area provided by an embodiment of the present application. Figure 4 As shown in , taking the shortest path between the starting position Q1 and the node position G1 of the first sensor node as an example, multiple path points in the shortest path are used as expansion center points, and rectangular expansion is performed until the expanded rectangle touches the surface position of the obstacle, and these expanded rectangles are used as safe operation space. It can be understood that, as Figure 4 The safe operating space shown in the figure is two-dimensional for ease of understanding. In practice, the safe operating space should be three-dimensional.

[0082] Through the above steps 210 to 230, the obstacle surface position obtained from the overall obstacle information and the position of each node are used to generate the shortest path that does not collide with the obstacle, and based on the shortest path, a safe operation space that does not collide with the obstacle is further expanded to facilitate the subsequent operation planning of the drone in these safe operation spaces, thereby improving the safety of the drone's information collection.

[0083] Step 300: Obtain a smooth optimization model.

[0084] Step 300 is described in detail below.

[0085] In some embodiments, after obtaining multiple safe operating spaces, in order to effectively collect sufficient information in the perception area and improve the smoothness of the UAV during operation, thereby reducing the energy consumption of controlling the UAV, it is necessary to obtain a smooth optimization model mainly composed of data acquisition constraints and smooth optimization objective functions, so as to facilitate the subsequent use of the smooth optimization model for UAV operation planning in each safe operating space.

[0086] Wherein, obtaining the smooth optimization model includes the following steps 310 to 340.

[0087] Step 310: Obtain operating variable parameters, data interaction variable parameters, and control point parameters.

[0088] Step 320: Generate a Bezier motion trajectory function based on the motion variable parameters and the control point parameters, and generate a smoothing optimization objective function based on the Bezier motion trajectory function.

[0089] Steps 310 to 320 are described in detail below.

[0090] In some embodiments, in order to generate a suitable smooth optimization model, it is first necessary to obtain the operating variable parameters, data interaction variable parameters and control point parameters c i The operational variables include the UAV's operational time t and direction in three-dimensional space during operational planning. The direction includes data in three dimensions, namely μ∈{x,y,z}. The data interaction variables include the sensor node's transmission power, the amount of data collected by the UAV during each phase, and the time interval.

[0091] Next, a Bezier trajectory function for planning the UAV's path is generated based on the running variable parameters and the control point parameters, so that the smooth optimization objective function can be generated using the Bezier trajectory function. j (t) is a smooth curve drawn based on the coordinates of a finite number of points (also called "control points") at any position, which can be expressed as the following formula (1).

[0092]

[0093] Among them, n is the order of the Bezier curve, [c 0 ,c 1 ,......,c n ] is the set of control points for this Bezier curve. Bezier curves have the following special properties: They always start at the first control point and end at the last control point, never passing through any other control points; the parameters of a Bezier curve are defined within a fixed scale, i.e., t∈[0,1]; all positions on a Bezier curve are confined to the convex hull formed by the control points; and the derivative curve of a Bezier curve is still a Bezier curve. Next, based on the principles of this Bezier curve, we will further describe in detail how to generate a Bezier trajectory function based on the running variable parameters and control point parameters.

[0094] The Bezier running trajectory function is generated based on the running variable parameters and the control point parameters, including the following steps 321 to 323.

[0095] Step 321: Obtain the curve scale factor corresponding to each stage and the initial time of each stage.

[0096] Step 322: Obtain Bezier curve parameters based on the difference between the running time and the initial time of the stage and divided by the curve scale factor.

[0097] Step 323: Generate a running curve for each stage based on the running direction, the curve scale factor, the control point parameters, the Bezier curve, and the Bezier curve parameters.

[0098] Steps 321 to 323 are described in detail below.

[0099] In some embodiments, the running trajectory of the UAV in each safe running space is initialized as a Bezier curve. Due to the particularity of the Bezier curve, it is necessary to allocate time in advance to the UAV in the Bezier curve.

[0100] This solution focuses on the trapezoidal allocation method to allocate time for each trajectory within the safe operation space. After clarifying the starting and ending positions of each trajectory segment, the required flight time is calculated based on the maximum acceleration and speed limit of the flight. According to the trapezoidal principle, the safe operation space is divided into different flight periods, namely the acceleration flight period, the deceleration flight period, and the possible uniform speed flight period. Using the principle of trapezoidal speed distribution, the length of the jth safe operation space is d j , the time in the safe operating space is divided into the drone acceleration flight time Drone deceleration flight time And the drone's uniform flight time Can get The acceleration and deceleration time Substituting into the above formula we can get Therefore, the total time allocated to the corresponding trajectory in the jth flight corridor is as shown in the following formula (2).

[0101]

[0102] Therefore, the operating time of the UAV in the safe operating space can be determined as shown in the following formula (3).

[0103]

[0104] Next, get the curve scale factor s corresponding to each stage j , and the initial time of each stage (such as T0, T1, ..., T J-1 Then, based on the difference between the running time and the initial time of the stage, and divided by the curve scale factor, the Bezier curve parameters (such as (t-T0) / s1) are obtained. Next, based on the running direction μ, the curve scale factor s j , control point parameters The Bezier curve and Bezier curve parameters generate the running curve of each stage, and the Bezier running trajectory function is obtained by combining multiple running curves as shown in the following formula (4).

[0105]

[0106] Where, multiply the curve scale factor s j It is used to obtain better numerical stability in practical applications.

[0107] Through the above steps 321 to 323, the Bezier operation trajectory function of the UAV's operation planning trajectory is constructed using the Bezier curve, so that the optimization parameters can be further accurately controlled to the control point to facilitate the control. The Bezier curve can provide continuous and smooth characteristics, so that when the smooth optimization objective function generated by the Bezier operation trajectory function is used for trajectory optimization, the operation smoothness of the UAV can be effectively improved, thereby effectively reducing energy consumption.

[0108] Next, a smooth optimization objective function is generated based on the Bessel trajectory function, including the following steps 324 to 328 .

[0109] Step 324: Obtain the transformation matrix of each stage.

[0110] Step 325: Perform a cubic derivation process on the operating curve, perform a square process, and then perform an integration process to obtain a smooth integral function for each stage.

[0111] Step 326: Obtain a smooth transition matrix based on the smooth integral function and the control matrix.

[0112] Step 327: Based on the smooth transition matrix, obtain a smooth integral matrix.

[0113] Step 328: The smooth optimization objective function of each stage is obtained based on multiplying the smooth transition matrix by the smooth integral matrix and then multiplying by the transposed matrix of the smooth transition matrix.

[0114] Steps 324 to 328 are described in detail below.

[0115] In some embodiments, Jerk is used as a measure of drone jitter in drone motion planning research, and its numerical value is the cubic time derivative of the trajectory. The smaller the integral of the square of jerk, that is, the smaller the jerk of the trajectory, the greater the smoothness of the trajectory, the smoother the trajectory, the smaller the jitter of the drone during flight, the smoother the flight, and the smoother the trajectory reflects the flight energy consumption of the drone to a certain extent. Using smoothness to measure the quality of robot trajectories is very common in the field of automatic control. In order to consider the quality of the drone trajectory, this solution uses the integral of the square of the trajectory jerk to measure the trajectory quality, let it be the smoothness cost, and minimize it. Further details are described below.

[0116] First, obtain the transformation matrix Λ of each stage n, and all control points in x, y, z three-dimensional space The control matrix P is obtained by vector.

[0117] Next, the running curve w(t) is derivatized three times, squared, and integrated to obtain the smooth integral function of each stage as shown in the following formula (5).

[0118]

[0119] Among them, the formula (5) is the quadratic programming P T In QP form, Q is the Hessian matrix of the objective function. The value of the j-th trajectory is w j (t), each Bezier trajectory corresponds to a different time allocation. Considering the characteristics of the Bezier curve, let (tT j-1 ) / s j =τ, and t∈[T j-1 ,T j ] is mapped to τ∈[0,1]. At the same time, in order to ensure the stability of numerical optimization, each curve is multiplied by the scalar coefficient s j The objective function of the j-th trajectory can be deduced as shown in the following formula (6).

[0120]

[0121] The unscaled Bessel trajectory is g j (τ),τ∈[0,1].

[0122] Compared with the traditional polynomial minimization smoothness cost problem, the objective function expression of minimizing the smoothness cost based on the Bezier curve is relatively complex, which is not conducive to constructing the standard QP form. To this end, we use the traditional polynomial coefficient P and the control point of the Bezier trajectory The linear transformation relationship of is used to transform the objective function of minimizing the smoothness cost based on the Bessel trajectory. The above formula (6) is simplified to the following formula (7).

[0123]

[0124] in, is the normalized Bessel trajectory. Based on the definition of the Bessel trajectory and the transformation matrix, it can be obtained as shown in the following formula (8).

[0125]

[0126] For each trajectory, since the order n of the trajectory is the same, there is a transformation matrix Λ n , based on the smooth integral function and the control matrix, the smooth transition matrix is ​​obtained. The transformation matrix Λn The details are shown in the following formula (9).

[0127]

[0128] Afterwards, based on the smooth transition matrix Taking the third-order derivative of the time parameter τ in the above formula (8) yields the following formula (10).

[0129]

[0130] Then, the smoothness cost of the polynomial trajectory is the integral after squared, as shown in the following formula (11).

[0131]

[0132] Among them aa T As shown in the following formula (12).

[0133]

[0134] Based on this aa T , and further obtain the Hessian matrix Q as shown in the following formula (13).

[0135]

[0136] Finally, based on the multiplication of the smooth transition matrix by the smooth integral matrix and then by the transposed matrix of the smooth transition matrix, the smooth optimization objective function of each stage is obtained as shown in the following formula (14).

[0137]

[0138] Through the above steps 324 to 328, each segment of the running curve of the Bessel running trajectory function is converted and processed to obtain a smooth optimization objective function for optimizing the acceleration in each stage, and combined with the characteristic that the smaller the acceleration, the greater the smoothness, the subsequent use of the smooth optimization objective function to optimize the running trajectory of the UAV can effectively improve the running smoothness of the UAV, thereby reducing the running energy consumption of the UAV.

[0139] Step 330: Generate a channel capacity function based on the data interaction variable parameters and the Bessel trajectory function, and generate data acquisition constraints based on the channel capacity function and the data interaction variable parameters.

[0140] Step 330 is described in detail below.

[0141] In some embodiments, while generating a smooth optimization objective function, in order to further ensure the reliability of information collection by the drone within the perception area, that is, to ensure that the drone obtains sufficient information from multiple communication nodes within the perception area, it is necessary to generate corresponding data acquisition constraints, and then regenerate a smooth optimization model based on the data acquisition constraints.

[0142] In order to generate appropriate data acquisition constraints, it is necessary to first generate an appropriate channel capacity function based on the data interaction variable parameters and the Bessel trajectory function, and then generate the appropriate data acquisition constraints based on the channel capacity function and the data interaction variable parameters.

[0143] The channel capacity function is generated based on the data interaction variable parameters and the Bessel trajectory function, including the following steps 331 to 333.

[0144] Step 331: Determine the distance function between the UAV and the node position based on the running time, the Bessel running trajectory function and the node position.

[0145] Step 332: Generate a large-scale fading function based on the distance function and the path loss exponent, and generate a channel gain function between the UAV and the sensor node based on the large-scale fading function, the Rayleigh fading function, and the Rice fading function.

[0146] Step 333: Obtain the channel capacity function based on the product of the channel gain function and the transmit power.

[0147] Steps 331 to 333 are described in detail below.

[0148] In some embodiments, due to the limitations of the drone’s own detection equipment during actual drone operation, it is impossible to directly obtain global map information (including all obstacle information). Therefore, when there is a line-of-sight link between the drone and the communication node, the Rice channel is used as the channel model; if there is no line-of-sight link between the drone and the communication node, or when the specific information of the obstacle between the current position and the communication node is unknown, this embodiment takes into account the relatively poor channel conditions and sets the channel between the drone and the communication node to the Rayleigh channel. Therefore, in the local map corresponding to each safe flight space, during the running time At time t, based on the Bessel trajectory function and the node position γ of the sensor node that the UAV is closest to at that time, the distance function between the node positions corresponding to the UAV and the sensor node can be expressed as the following formula (15).

[0149]

[0150] in, for The three-dimensional position of the UAV at time t is the Bessel trajectory function. The corresponding position.

[0151] Then, a large-scale fading function is generated based on the distance function (15) and the path loss exponent α as shown in the following formula (16).

[0152]

[0153] Where β0 is the average channel power gain at the reference distance d0=1m, and α is usually a path loss exponent between 2 and 6.1.

[0154] Next, considering the influence of small-scale fading, based on the large-scale fading function (16), the Rayleigh fading function and the Rice fading function Lace, the channel gain function between the UAV and the sensor node is generated as shown in the following formula (17).

[0155]

[0156] Among them, h LOS =1,h NLOS ~CN(0,1), Rayleigh flag and Lace flag are the flags of the Rayleigh channel and the Rice channel, respectively, and l is the Rice factor. Next, the transmission power of the sensor node to send information is defined as p. In wireless communication channels, the channel gain is usually slow-changing, and for the convenience of calculation and processing, the position of the drone is set to be constant in each time slot, and the channel gain between the drone and the sensor node in the same time slot is also constant. Based on the product of the channel gain function (17) and the transmission power, the signal-to-noise ratio of the data information transmitted by the sensor node to the drone in the kth time slot of the jth trajectory can be further obtained as shown in the following formula (18).

[0157]

[0158] Then, the signal-to-noise ratio (18) is used to further obtain the real-time channel function of the data information transmitted by the sensor node to the drone in the kth time slot of the jth trajectory, as shown in the following formula (19).

[0159] R j (k) = Blog2(1+ρ j [k]) (19)

[0160] Where B represents the channel bandwidth in Hertz (Hz), σ 2 is the noise power of the UAV’s receiver.

[0161] In some embodiments, the solution provided by this application also considers the prediction phase for the next time period during the UAV's operation. The impact of obstacles on the trajectory is not considered during this prediction phase. However, to ensure the completion of the actual communication task, the prediction phase considers the channel under worse conditions, that is, the communication model adopts a Rayleigh channel. Based on this, the predicted trajectory corresponding to the prediction phase can be expressed as shown in the following formula (20).

[0162]

[0163] in, represents the kth time slot of the predicted trajectory L, K L =Te / δe,K L is the number of time slots of the predicted trajectory L. Next, based on the predicted position corresponding to the predicted trajectory The distance between the UAV and the sensor node in the time slot during the prediction phase can be obtained, and then the prediction channel function of the prediction phase can be obtained as shown in the following formula (21).

[0164]

[0165] Where ρ[k] is the signal-to-noise ratio of the transmitted data in the k-th time slot during the prediction phase; H[k] is the channel gain in the k-th time slot during the prediction phase; h[k] is the channel gain associated with large-scale fading in the k-th time slot during the prediction phase; H[k] = h[k]gh NLOS ,

[0166] Next, data acquisition constraints are generated based on the channel capacity function and the data interaction variable parameters, including the following steps 334 to 336 .

[0167] Step 334: Accumulate all data collection amounts before the current moment to obtain the first information data collection amount.

[0168] Step 335: Obtain a second information data collection amount based on the product of the real-time channel function and the time interval, and obtain a third information data collection amount based on the product of the predicted channel function and the time interval.

[0169] Step 336: Accumulate the first information data collection amount, the second information data collection amount, and the third information data collection amount to obtain the collectible data amount, and generate data acquisition constraints based on the relationship between the collectible data amount and the minimum data collection amount.

[0170] Steps 334 to 336 are described in detail below.

[0171] In some embodiments, the solution provided by this application involves performing local optimization within each safe operating space during actual drone operation. Within each local area, the drone plans a trajectory to the global destination using only a known local map. Since the drone will pass through multiple local areas during flight, multiple planning steps are required. The drone plans a trajectory only once within a local area. The starting point of the first local plan is the global starting point, and the starting point of each subsequent local plan is the intersection of the previously planned trajectory and the previous local map.

[0172] Therefore, use It represents the amount of data that the drone has collected at the starting point of the current safe operation space (i.e., the first information data collection amount), the amount of data that can be collected in the currently known safe operation space (i.e., the second information data collection amount), and the predicted remaining collectible data (i.e., the third information data collection amount). The predicted remaining collectible data is the amount of data that can be collected in the unexplored area, which is obtained by the intersection of the trajectory of the current safe operation space and the next safe operation space and the obstacle-free trajectory of the end point. For the first safe operation space, For the last safe operating space, For the qth safe operation space, all data collection amounts from the current moment forward are accumulated to obtain the first information data collection amount as shown in the following formula (22).

[0173]

[0174] Then, based on the product of the real-time channel function and the time interval, the second information data collection amount is obtained, and based on the product of the predicted channel function and the time interval, the third information data collection amount is obtained, as shown in the following formula (23).

[0175]

[0176] Finally, the first information data collection amount is accumulated Second information data collection amount and the amount of third-party information data collected Get the amount of data that can be collected, and based on the amount of data that can be collected and the minimum amount of data collection D min The data acquisition constraints are generated based on the size relationship of . Therefore, the data acquisition constraints collected by the drone in the data acquisition task corresponding to the qth safe operation space are shown in the following formula (24).

[0177]

[0178] By the above steps 331 to 333, and steps 334 to 336, the running position of the UAV in each time slot determined by the Bezier running trajectory function is used to further construct the real-time channel function between the UAV and the sensor nodes and the predicted channel function in the prediction phase by using the relevant Rayleigh fading channel modeling and Rician fading channel modeling. Then, the second data information collection amount generated by the real-time channel function, the third data information collection amount generated by the predicted channel function, and the first data information collection amount obtained from the second data information collection amount are used to generate a collectable data amount, and the data acquisition constraint generated according to the size relationship between the collectable data amount and the minimum data collection amount can effectively ensure that the UAV acquires sufficient information from multiple communication nodes in the perception area.

[0179] Step 340: generating a smooth optimization model based on the control point parameters, the smooth optimization objective function, and the data acquisition constraint.

[0180] Step 340 will be described in detail below.

[0181] In some embodiments, in addition to obtaining the control point parameters, the smooth optimization objective function, and the data acquisition constraint, other constraint conditions need to be constructed to ensure the safe and reliable operation of the UAV in the perception area. The details are described as follows.

[0182] Since the high-order derivatives of the Bezier curve can be linearly represented by the coefficients of its low-order expressions, where l represents the order of the derivative to be solved, and n is the degree of freedom of the Bezier curve. This feature will be used in setting the speed and acceleration constraints, as shown in the following formula (25).

[0183]

[0184] Based on this, for the l-th derivative of the j-th trajectory, the corresponding relationship of the trajectory position, velocity, and acceleration is shown in the following formula (26).

[0185]

[0186] In addition, the UAV needs to pass through certain path points, and these certain path points in the present application include the starting point and the target point of each safe running space. The position, velocity, and acceleration at these certain points need to be constrained. Since the first and last control points of the Bezier curve in each safe running space are the trajectory points at the beginning and end of the trajectory of the UAV in the safe running space, the path point constraint can be directly represented by the control points as shown in the following formula (27).

[0187]

[0188] wherein l represents the starting point of the j-th trajectory th For the endpoint, replace 0 with n. This formula shows that for trajectory points, you need to multiply by the scaling factor, but not for velocity, and for acceleration, you need to divide by the scaling factor.

[0189] Because each trajectory within the safe operating space is initialized as a Bezier curve, to ensure the continuity of the overall trajectory, the first and last control points of the Bezier curves corresponding to each two adjacent safe operating spaces must be equal. This continuity constraint is an equality constraint. For the jth and j+1th trajectories, the φ-order derivative between them satisfies the following formula (28).

[0190]

[0191] In addition, due to the convex hull property of the Bezier curve, it can be seen that the trajectory points must be within the geometric convex hull formed by the control points. Therefore, as long as the convex hull is restricted to the safe operation space, the trajectory points will also be restricted to the safe operation space. Therefore, in order to ensure that each control point falls within the flight corridor, it is necessary to formulate a safety constraint as shown in the following formula (29).

[0192]

[0193] in are the upper and lower boundaries of the safe operating space, respectively. At the same time, the UAV dynamics also requires that the speed and acceleration are within their constraints. Combined with the high-order characteristics of the Bezier curve, the dynamic constraints can be expressed as shown in the following formula (30).

[0194]

[0195] Based on all the above relevant constraints (including path point constraints, drone safety constraints, drone dynamics constraints and data acquisition constraints), smooth optimization objective function and control point parameters, a smooth optimization model can be constructed in each (such as the qth) safe operation space as shown in the following formula (31).

[0196]

[0197] The objective function is a smooth optimization objective function, and the optimization variables are the control point parameters. Constraints include constraints on the position, velocity, and acceleration of the necessary points, continuity constraints on the control points, safety constraints, velocity and acceleration boundary constraints, and constraints on the amount of data collected.

[0198] In this embodiment, the problem is solved for each safe operating space, and then only the trajectory within that local safe operating space is executed. Since the optimization objective is a standard quadratic function, all constraints except the data volume constraint are affine. However, since the distance between the drone and the sensor node in the data acquisition constraint is not known in advance, this constraint is non-convex. This problem is a non-convex quadratic programming problem, which can be converted to a convex problem using SCA and then solved iteratively using the Mosek solver. The specific conversion is as follows.

[0199] Since the data acquisition constraint in (31) is non-convex, this optimization problem is a non-convex QCQP problem. Expressed in the form of control points, the data transmission rate can be obtained as shown in the following formula (32).

[0200]

[0201] Where C is a variable related to the channel type. When the channel is a Rayleigh channel, the value of C follows the Rayleigh distribution; otherwise, it follows the Rice distribution. Next, based on the definition of the Bezier curve trajectory, we can obtain the following formula (33).

[0202]

[0203] Substituting formula (33) into the data transmission rate formula (32), we can obtain the following formula (34).

[0204]

[0205] However, the new transformed constraint (34) is non-convex. But for any given local control point at iteration r The available transmission rate R j Global concave lower bound for (k) The specific expression is shown in the following formula (35).

[0206]

[0207] And the following formula (36).

[0208]

[0209] The R found by (35) j The global lower bound of (k) Replace R in data acquisition constraints j (k), You can also Similar processing is performed, so the (P1-q) in the original smooth optimization model can be transformed into problem (P2-q) as shown in the following formula (37), forming a new smooth optimization model.

[0210]

[0211] When given At the rth iteration of The problem is convex, so the SCA technique can be used to obtain the local optimal solution of this QCQP problem.

[0212] Let r be the number of iterations, for The r+1th iteration of has the following inequality as shown in formula (38).

[0213]

[0214] on the right is the transmission rate R j (k) has a global concave lower bound, so the smooth optimization model (37) can be solved iteratively using the solver.

[0215] Step 400: According to the flight sequence of the UAV, the operating parameters of the UAV and the data interaction parameters between the UAV and the sensor node are obtained one by one in each safe operation space. Based on the operating parameters and data interaction parameters, the smoothing optimization model is solved to obtain the stage optimization control points, and the UAV is controlled to collect data according to the stage optimization control points.

[0216] Step 400 is described in detail below.

[0217] In some embodiments, after obtaining the smooth optimization model of each safe operation space, starting from the starting position, in the safe operation space of each result, the drone's own binocular camera or laser radar is used in combination with the inertial conduction unit and other sensors to obtain environmental information within a certain range and the drone's own state information, including a local map and its own related position, speed, acceleration, attitude and other information parameters (including the drone's operating parameters and the data interaction parameters between the drone and the sensor node). Then, the smooth operation model (38) of the safe operation space is solved based on these information parameters to obtain the stage optimization control point in the safe operation space, and the drone is controlled to operate in the safe operation space according to the stage optimization control point until the drone has collected information from multiple sensor nodes in sequence and moved to the end position.

[0218] Wherein, before solving the smooth optimization model based on the operating parameters and the data interaction parameters to obtain the stage optimization control points, the information collection method of the perception area further includes the following steps 410 to 420.

[0219] Step 410: Obtain regional obstacle information, and determine updated obstacle surface positions of multiple obstacles close to the drone based on the regional obstacle information.

[0220] Step 420: Update the safe operating space based on the updated obstacle surface position.

[0221] Steps 410 to 420 are described in detail below.

[0222] In some embodiments, since the overall obstacle information initially obtained by the drone may contain incomplete data information on all obstacles in the perception area, it is necessary to obtain obstacle information near the drone again after each time the drone enters a new safe operation space to obtain regional obstacle information, and determine the updated obstacle surface positions of multiple obstacles close to the drone based on the regional obstacle information, and then update the current safe operation space and subsequent safe operation spaces according to the updated obstacle surface positions to improve the safety of the drone during operation.

[0223] The information collection method and related device of the sensing area proposed in the embodiments of the present application, the sensing area includes a plurality of sensor nodes and at least one obstacle, the method is applied to a UAV, and the method includes the following steps: firstly, obtaining the starting position and the end position of the UAV, and obtaining the node position of each sensor node and the overall obstacle information of at least one obstacle; then, determining the obstacle surface position of at least one obstacle based on the overall obstacle information, generating the shortest path between each two adjacent node positions one by one, the shortest path including a plurality of path points, taking each path point as an expansion center point of an expansion rectangle one by one, and performing rectangular expansion based on the expansion center point until the expansion rectangle reaches the obstacle surface position, and taking the expansion rectangle as a safe running space; next, obtaining running variable parameters, data interaction variable parameters and control point parameters, obtaining the curve scale factor corresponding to each stage, and the stage initial time of each stage, obtaining the Bezier curve parameter based on the difference between the running time and the stage initial time and divided by the curve scale factor, generating the running curve of each stage based on the running direction, the curve scale factor, the control point parameter, the Bezier curve and the Bezier curve parameter, and obtaining the conversion matrix of each stage, performing three times of derivative processing on the running curve, performing square processing, and then performing integral processing to obtain the smooth integral function of each stage, obtaining the smooth transition matrix based on the smooth integral function and the control matrix, obtaining the smooth integral matrix based on the smooth transition matrix, multiplying the smooth transition matrix by the smooth integral matrix, and then multiplying the transpose matrix of the smooth transition matrix to obtain the smooth optimization target function of each stage, determining the distance function of the UAV and the node position based on the running time, the Bezier running trajectory function and the node position, generating the large-scale fading function based on the distance function and the path loss index, generating the channel gain function between the UAV and the sensor node based on the large-scale fading function, the Rayleigh fading function and the Rician fading function, obtaining the channel capacity function based on the product of the channel gain function and the sending power, and accumulating all data collection amounts before the current time to obtain the first information data collection amount, obtaining the second information data collection amount based on the product of the real-time channel function and the time interval, and obtaining the third information data collection amount based on the product of the predicted channel function and the time interval, accumulating the first information data collection amount, the second information data collection amount and the third information data collection amount to obtain the collectable data amount, and generating the data acquisition constraint based on the size relationship between the collectable data amount and the minimum data collection amount, and generating the smooth optimization model based on the control point parameter, the smooth optimization target function and the data acquisition constraint.Finally, following the drone's flight sequence, the system obtains the drone's operating parameters, the data exchange parameters between the drone and the sensor nodes, and regional obstacle information in each safe operating space. Based on this regional obstacle information, the updated obstacle surface positions of multiple obstacles approaching the drone are determined. The safe operating space is then updated based on the updated obstacle surface positions. Based on the operating parameters and data exchange parameters, the smoothing optimization model is solved to obtain the stage-by-stage optimized control points. The drone is then controlled to collect data according to these stage-by-stage optimized control points.

[0224] The embodiment of the present application utilizes the obstacle surface position obtained from the overall obstacle information and the position of each node to generate the shortest path that does not collide with the obstacle, and further expands the shortest path based on the shortest path to generate a safe operation space that does not collide with the obstacle, thereby facilitating the subsequent operation planning of the drone in these safe operation spaces to improve the safety of the drone in collecting information; and, utilizes the Bezier curve to construct a Bezier operation trajectory function of the drone's operation planning trajectory, thereby facilitating the optimization parameters to be further refined to the control point for the drone control, so as to facilitate control, and utilizes the continuous and smooth characteristics that can be provided by the Bezier curve, thereby facilitating the subsequent use of the smooth optimization objective function generated by the Bezier operation trajectory function for trajectory optimization, which can effectively improve the operation smoothness of the drone and thereby effectively reduce energy consumption; and, utilizes the operation position of the drone in each time slot determined by the Bezier operation trajectory function, thereby utilizing the relevant Rayleigh fading channel modeling and Ricean fading channel modeling to further construct the real-time channel function between the drone and the sensor node and the predicted channel function of the prediction stage. Then, the second data information collection amount generated by the real-time channel function, the third data information collection amount generated by the predicted channel function, and the first data information collection amount obtained from the second data information collection amount are used to generate the collectible data amount. The data acquisition constraints generated according to the relationship between the collectible data amount and the minimum data collection amount can effectively ensure that the drone obtains sufficient information from multiple communication nodes within the perception area.

[0225] The present application also provides a device for collecting information about a sensing area, which can implement the above-mentioned method for collecting information about a sensing area. Figure 5 , the apparatus 500 comprises:

[0226] A node position acquisition module 510 is used to acquire the node position of each sensor node and overall obstacle information of at least one obstacle, where the node position includes a starting position and an ending position;

[0227] A space generation module 520 is used to generate multiple safe operation spaces from a starting position to an end position based on the node position and the overall obstacle information;

[0228] A model acquisition module 530 acquires a smoothing optimization model, where the smoothing optimization model includes data acquisition constraints and a smoothing optimization objective function;

[0229] The operation control module 540 is used to obtain the operation parameters of the drone and the data interaction parameters between the drone and the sensor node in each safe operation space according to the flight sequence of the drone. Based on the operation parameters and data interaction parameters, the smoothing optimization model is solved to obtain the stage optimization control points, and the drone is controlled to collect data according to the stage optimization control points.

[0230] In some embodiments, the space generation module 520 is further configured to:

[0231] determining an obstacle surface position of at least one obstacle based on the overall obstacle information;

[0232] Generate the shortest path between every two adjacent node positions one by one, and the shortest path includes multiple path points;

[0233] Each path point is used as the extension center point of the extension rectangle one by one, and the rectangle is expanded based on the extension center point until the extension rectangle reaches the obstacle surface position, and the extension rectangle is used as the safe operating space.

[0234] In some embodiments, the model acquisition module 530 is further configured to:

[0235] Obtain running variable parameters, data interaction variable parameters and control point parameters;

[0236] Generate a Bezier running trajectory function based on the running variable parameters and the control point parameters, and generate a smooth optimization objective function based on the Bezier running trajectory function;

[0237] generating a channel capacity function based on the data interaction variable parameters and the Bessel trajectory function, and generating a data acquisition constraint based on the channel capacity function and the data interaction variable parameters;

[0238] A smooth optimization model is generated based on control point parameters, smooth optimization objective function and data acquisition constraints.

[0239] In some embodiments, the model acquisition module 530 is further configured to:

[0240] Get the curve scale factor corresponding to each stage and the initial time of each stage;

[0241] The Bezier curve parameters are obtained based on the difference between the running time and the initial time of the stage and divided by the curve scale factor;

[0242] A running curve of each stage is generated based on the running direction, the curve scale factor, the control point parameters, the Bezier curve, and the Bezier curve parameters.

[0243] In some embodiments, the model acquisition module 530 is further configured to:

[0244] Get the transformation matrix of each stage;

[0245] The running curve is derivatized three times, squared, and then integrated to obtain the smooth integral function of each stage;

[0246] A smooth transition matrix is ​​obtained based on the smooth integral function and the control matrix;

[0247] Based on the smooth transition matrix, a smooth integral matrix is ​​obtained;

[0248] The smooth optimization objective function of each stage is obtained by multiplying the smooth transition matrix by the smooth integral matrix and then by the transposed matrix of the smooth transition matrix.

[0249] In some embodiments, the model acquisition module 530 is further configured to:

[0250] Determine the distance function between the UAV and the node position based on the running time, the Bessel running trajectory function and the node position;

[0251] Generate a large-scale fading function based on the distance function and the path loss exponent. Generate a channel gain function between the UAV and the sensor node based on the large-scale fading function, the Rayleigh fading function, and the Rice fading function.

[0252] The channel capacity function is obtained based on the product of the channel gain function and the transmit power.

[0253] In some embodiments, the model acquisition module 530 is further configured to:

[0254] Accumulate all data collection amounts before the current moment to obtain the first information data collection amount;

[0255] Obtaining a second information data collection amount based on a product of a real-time channel function and a time interval, and obtaining a third information data collection amount based on a product of a predicted channel function and a time interval;

[0256] The first information data collection amount, the second information data collection amount, and the third information data collection amount are accumulated to obtain a collectible data amount, and a data acquisition constraint is generated based on a size relationship between the collectible data amount and a minimum data collection amount.

[0257] In some embodiments, the operation control module 540 is further configured to:

[0258] Obtaining regional obstacle information and determining updated obstacle surface positions of a plurality of obstacles approaching the UAV based on the regional obstacle information;

[0259] The safe operating space is updated based on the updated obstacle surface position.

[0260] In the above embodiments, the description of each embodiment has its own focus. For the part that is not described in detail in a certain embodiment, the specific implementation of the information collection device of the perception area is basically the same as the specific implementation of the information collection method of the perception area, and will not be repeated here.

[0261] In an embodiment of the present application, the information collection device in the perception area uses the obstacle surface position obtained from the overall obstacle information and the position of each node to generate the shortest path that does not collide with the obstacle, and further expands the shortest path based on the shortest path to generate a safe operation space that does not collide with the obstacle, so as to facilitate the subsequent operation planning of the drone in these safe operation spaces to improve the safety of the drone in collecting information; and, uses the Bezier curve to construct the Bezier operation trajectory function of the drone's operation planning trajectory, so as to facilitate the optimization parameters to be further refined to the control point for the control of the drone, so as to facilitate control, and uses the Bezier curve to provide continuous and smooth characteristics, so as to facilitate the subsequent use of the smooth optimization objective function generated by the Bezier operation trajectory function for trajectory optimization, which can effectively improve the operation smoothness of the drone and thus effectively reduce energy consumption; and, uses the Bezier operation trajectory function to determine the operation position of the drone in each time slot, so as to further construct the real-time channel function between the drone and the sensor node and the predicted channel function of the prediction stage by using the relevant Rayleigh fading channel modeling and Ricean fading channel modeling. Then, the second data information collection amount generated by the real-time channel function, the third data information collection amount generated by the predicted channel function, and the first data information collection amount obtained from the second data information collection amount are used to generate the collectible data amount. The data acquisition constraints generated according to the relationship between the collectible data amount and the minimum data collection amount can effectively ensure that the drone obtains sufficient information from multiple communication nodes within the perception area.

[0262] An embodiment of the present application further provides an electronic device, including:

[0263] at least one memory;

[0264] at least one processor;

[0265] at least one program;

[0266] The program is stored in the memory, and the processor executes at least one program to implement the above-mentioned method for collecting information about the sensing area. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0267] See also Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0268] The processor 601 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0269] The memory 602 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 602 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602, and the processor 601 calls and executes the information collection method of the sensing area of ​​the embodiment of this application;

[0270] Input / output interface 603, used to implement information input and output;

[0271] Communication interface 604, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0272] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0273] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0274] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned method for collecting information of the perception area is implemented.

[0275] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0276] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0277] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0278] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0279] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0280] The terms "first", "second", "third", "fourth", and the like in the description of this application and in the claims hereof, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed herein is solely for the convenience of the reader and does not limit the scope of the application. It is also to be understood that the description and examples in this application do not limit the scope of the application, and that methods involved in the application can comprise, consist of, or consist essentially of, any of the described steps, components, elements, and the like, in any suitable order, in any suitable combination, and / or in any suitable number.

[0281] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0282] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. The coupling or direct coupling or communication connection between the displayed or discussed objects can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0283] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the application.

[0284] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0285] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0286] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for collecting information of a sensing area, characterized in that: The sensing area includes a plurality of sensor nodes and at least one obstacle. The method is applied to a drone, and the method includes: Obtaining a starting position and an ending position of the UAV, as well as a node position of each sensor node and overall obstacle information of at least one obstacle; generating a plurality of safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information; Acquire a smoothing optimization model, wherein the smoothing optimization model includes a data acquisition constraint and a smoothing optimization objective function; According to the flight sequence of the UAV, the operating parameters of the UAV and the data interaction parameters between the UAV and the sensor node are obtained one by one in each of the safe operation spaces; based on the operating parameters and the data interaction parameters, the smoothing optimization model is solved to obtain a stage optimization control point; and the UAV is controlled to perform data collection according to the stage optimization control point; The obtaining of the smooth optimization model comprises: Obtain running variable parameters, data interaction variable parameters and control point parameters; generating a Bezier motion trajectory function based on the motion variable parameters and the control point parameters, and generating the smoothing optimization objective function based on the Bezier motion trajectory function; generating a channel capacity function based on the data interaction variable parameter and the Bessel motion trajectory function, and generating the data acquisition constraint based on the channel capacity function and the data interaction variable parameter; The smoothing optimization model is generated based on the control point parameters, the smoothing optimization objective function, and the data acquisition constraints.

2. The method for collecting information about a sensing area according to claim 1, wherein: The generating a plurality of safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information includes: determining an obstacle surface position of at least one of the obstacles based on the overall obstacle information; Generating a shortest path between every two adjacent node positions one by one, wherein the shortest path includes a plurality of path points; Each of the path points is used as the expansion center point of the expansion rectangle one by one, and the rectangle is expanded based on the expansion center point until the expansion rectangle reaches the surface position of the obstacle, and the expansion rectangle is used as the safe operation space.

3. The method for collecting information about a sensing area according to claim 1, wherein: The running variable parameters include running time and running direction, the Bezier running trajectory function includes running curves of multiple stages, and generating the Bezier running trajectory function based on the running variable parameters and the control point parameters includes: Obtaining a curve scale factor corresponding to each of the stages and a stage initial time of each stage; Obtaining Bezier curve parameters based on the difference between the running time and the initial time of the stage and dividing by the curve scaling factor; The running curve of each stage is generated based on the running direction, the curve scale factor, the control point parameters, the Bezier curve, and the Bezier curve parameters.

4. The method for collecting information about a sensing area according to claim 3, wherein: The generating of the smooth optimization objective function based on the Bessel motion trajectory function comprises: Obtaining the transformation matrix for each of the stages; Performing a cubic derivation process on the operation curve, performing a square process, and then performing an integration process to obtain a smooth integral function of each stage; Obtaining a smooth transition matrix based on the smooth integral function and the control matrix; Based on the smooth transition matrix, a smooth integral matrix is ​​obtained; The smooth optimization objective function of each stage is obtained based on multiplying the smooth transition matrix by the smooth integral matrix and then multiplying by the transposed matrix of the smooth transition matrix.

5. The method for collecting information of a sensing area according to claim 3, characterized in that: The data interaction variable parameter includes the transmission power of the sensor node, and generating the channel capacity function based on the data interaction variable parameter and the Bessel trajectory function includes: Determining a distance function between the UAV and the node position based on the running time, the Bessel running trajectory function, and the node position; generating a large-scale fading function based on the distance function and the path loss exponent, and generating a channel gain function between the UAV and the sensor node based on the large-scale fading function, the Rayleigh fading function, and the Rice fading function; The channel capacity function is obtained based on the product of the channel gain function and the transmit power.

6. The method for collecting information of a sensing area according to claim 3, characterized in that: The data interaction variable parameters also include the amount of data collected by the drone at each stage and the time interval. The channel capacity function includes a real-time channel function and a predicted channel function. Generating the data acquisition constraints based on the channel capacity function and the data interaction variable parameters includes: Accumulating all the data collection amounts before the current moment to obtain a first information data collection amount; obtaining a second information data collection amount based on a product of the real-time channel function and the time interval, and obtaining a third information data collection amount based on a product of the predicted channel function and the time interval; The first information data collection amount, the second information data collection amount, and the third information data collection amount are accumulated to obtain a collectible data amount, and the data acquisition constraint is generated based on a size relationship between the collectible data amount and a minimum data collection amount.

7. The method for collecting information about a sensing area according to claim 1, wherein: Before solving the smoothing optimization model based on the operating parameters and the data interaction parameters to obtain the stage optimization control points, the method further includes: Obtaining regional obstacle information, and determining updated obstacle surface positions of a plurality of obstacles close to the drone based on the regional obstacle information; The safe operating space is updated based on the updated obstacle surface position.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the information collection method of the perception area according to any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for collecting information of a perception area according to any one of claims 1 to 7 is implemented.

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