Information acquisition method of sensing area and related equipment

Abstract

According to the information acquisition method of the sensing area and the related equipment provided by the embodiment of the invention, the sensing area comprises a plurality of sensor nodes and at least one obstacle, the method is applied to the unmanned aerial vehicle, and the method comprises the steps of obtaining a starting position and an ending position of the unmanned aerial vehicle, obtaining the node position of each sensor node and the overall obstacle information of at least one obstacle; generating a plurality of safe operation spaces from the initial position to the final position based on the node position and the overall obstacle information; acquiring a smooth optimization model, acquiring operation parameters of the unmanned aerial vehicle in each safe operation space one by one according to the flight sequence of the unmanned aerial vehicle, acquiring data interaction parameters between the unmanned aerial vehicle and the sensor nodes, and solving the smooth optimization model to obtain stage optimization control points; and the unmanned aerial vehicle is controlled to carry out data acquisition according to the stage optimization control point, so that the reliability, the operation safety and the smoothness of information acquisition of the unmanned aerial vehicle in the sensing area are effectively improved.

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 of physical movement and flexibility of 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. Many researchers from all over the world have conducted extensive research on this.

[0003] In the related art, the control method of the UAV for the UAV to operate and collect information between multiple communication nodes usually plans the operation of the UAV based on the information collection efficiency and flight efficiency between the multiple communication nodes, and controls the UAV to collect information from multiple communication nodes according to the operation planning data. However, in actual applications, there are usually multiple obstacles, so the information collection by the UAV operation planning method in the related art is prone to cause safety problems such as collisions with obstacles during the operation of the UAV. Summary of the invention

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

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for collecting information in a sensing area, wherein 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:

[0006] 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 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 smooth optimization model, wherein the smooth optimization model includes a data acquisition constraint and a smooth 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 the 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] Generate a Bezier operation trajectory function based on the operation variable parameters and the control point parameters, and generate the smoothing optimization objective function based on the Bezier operation trajectory function;

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

[0018] The smooth optimization model is generated based on the control point parameters, the smooth 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 the curve scale factor corresponding to each of the stages and the initial time of each stage;

[0021] Based on the difference between the running time and the initial time of the stage, and divided by the curve scale factor, a Bezier curve parameter is obtained;

[0022] The running curve of each of the stages 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 smooth optimization objective function based on the Bessel motion trajectory function includes:

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

[0025] The operation curve is subjected to a third-time derivative processing, a square processing, and then an integration processing 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 the generating of the channel capacity function based on the data interaction variable parameter and the Bessel operation trajectory function includes:

[0030] Determine 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] Generate a large-scale fading function based on the distance function and the path loss index, 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;

[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 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, and generating the data acquisition constraints based on the channel capacity function and the data interaction variable parameters includes:

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

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

[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-mentioned purpose, a third aspect of an embodiment of the present application proposes an information collection device for a sensing area, wherein the sensing area includes a plurality of sensor nodes and at least one obstacle, and the device is applied to a drone, and the device includes:

[0041] A node position acquisition module, used 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 end position;

[0042] A space generation module, used 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, which acquires a smooth optimization model, wherein the smooth optimization model includes a data acquisition constraint and a smooth 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-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the information collection method of the perception area as described in the first aspect.

[0046] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the information collection method of the perception area described in the first aspect above is implemented.

[0047] The information collection method and related equipment of the perception area proposed in the embodiment of the present application, the perception area includes multiple sensor nodes and at least one obstacle, the method is applied to a drone, and the method includes: first, obtaining the starting position and the end position of the drone, and obtaining the node position of each sensor node and the overall obstacle information of at least one obstacle; then, based on the node position and the overall obstacle information, generating multiple safe operation spaces from the starting position to the end position; next, obtaining a smoothing optimization model, the smoothing optimization model includes data acquisition constraints and a smoothing optimization objective function; finally, according to the flight sequence of the drone, in each safe operation space, the operating parameters of the drone are obtained one by one, and the data interaction parameters between the drone and the sensor node are obtained; 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 drone is controlled to collect data according to the stage optimization control points. 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 from data acquisition constraints and a smooth optimization objective function 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 in the perception area and the safety of the drone's operation in the operation area.

[0048] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the structure of the sensing area provided in one embodiment of the present application.

[0050] Figure 2 This is a flowchart of a method for collecting information of a perception 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 It is a structural diagram of an information collection device for a sensing area provided in yet another embodiment of the present application.

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

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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 may 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 those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] First, some nouns involved in this application are analyzed:

[0059] UAV communication refers to the transmission and exchange of information between UAVs or between UAVs and ground stations. UAV communication can be achieved through radio waves, satellite communications, optical fiber communications, etc. UAV communication allows UAVs to collect information sent by ground nodes during flight.

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

[0061] A Bezier curve is a mathematical curve whose shape is determined by several control points, where 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 of physical movement and flexibility of 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. Many researchers from all over the world have conducted extensive research on this.

[0063] In the related art, the control method of the UAV for the UAV to operate and collect information between multiple communication nodes usually plans the operation of the UAV based on the information collection efficiency and flight efficiency between the multiple communication nodes, and controls the UAV to collect information from multiple communication nodes according to the operation planning data. However, in actual applications, there are usually multiple obstacles, so the information collection by the UAV operation planning method in the related art is prone to cause safety problems such as collisions with obstacles during the operation of the UAV.

[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 the smooth optimization model obtained by the data acquisition constraints and the smooth optimization objective function 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 in the perception area and the safety of the drone's operation in the operation area.

[0065] In order to better describe the information collection method of the perception area provided by the present application, the perception area applied to the information collection method of the perception area is first described below. Figure 1 , is a schematic diagram of a sensing area structure provided by an embodiment of the present application. Figure 1 As shown, the communication system includes a starting position and an end position of the drone, a plurality of sensor nodes and at least one obstacle. In collecting information in the sensing area, the drone starts running at the starting position, passes by each sensor node in turn, and finally reaches the end position. During the running process, the drone interacts with the sensor nodes to realize information collection.

[0066] Based on the above perception area, the information collection method and related devices 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 a drone or a control node connected to the drone.

[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 of the perception area provided in the embodiment of the present application, Figure 2 The method may include but is not limited to steps 100 to 400. It can also be understood that this embodiment Figure 2 The order of step 100 to step 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 end position of the UAV, and obtain the node position of each sensor node and the overall obstacle information of at least one obstacle.

[0069] The following is a detailed description of step 100.

[0070] In some embodiments, after responding to the information collection requirements of multiple sensor nodes in the sensing area, in order to effectively operate safely in the complex environment of the sensing area and collect information from all sensor nodes that need to collect information, the drone needs to pre-acquire the node positions of all sensor nodes that need to collect information in the sensing area, and globally acquire the overall obstacle information of at least one obstacle. It is understandable that the overall obstacle information acquired at the beginning is relatively rough, and there may be incomplete acquisition and recognition. Therefore, it is necessary to acquire local area obstacle information in real time during the subsequent operation of the drone to supplement the relevant obstacle information.

[0071] In addition, the drone will run from the starting position to the end position. It can be understood that the starting position and the end position are a spatial node, and no constraints are imposed on them in this embodiment. At the same time, the starting position and the end position can also be located at any sensor node, or an additional spatial node. In addition, the obstacle position and the node position are three-dimensional space ranges including volume, not just a simple position 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] The following is a detailed description of step 200.

[0074] In some embodiments, in order for the drone to effectively acquire information in a complex perception area, it is necessary to first generate multiple safe operation spaces without collision points from the starting position to the end position based on the pre-acquired node information and overall obstacle information, so as to facilitate the subsequent operation planning of the drone in these multiple safe operation spaces to ensure the operation safety of the drone. The construction process of the safe operation space will be described in detail below.

[0075] Wherein, generating multiple safe operation spaces from the starting position to the end position based on the node position and the overall obstacle information includes the following steps 210 to 230.

[0076] Step 210: Determine an 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: Use each path point as the expansion center point of the expansion rectangle one by one, and expand the rectangle based on the expansion center point until the expansion rectangle reaches the obstacle surface position, and use the expansion rectangle 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 end position Q2, multiple obstacle positions and multiple node positions (G1, G2 and G3), multiple shortest paths are generated for the drone to reach the end position Q2 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 expansion 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 of the path points needs to be retained. Each rectangle in the figure corresponds to a corridor, and a series of corridors are strung together to form a feasible channel. Figure 4 , is a schematic diagram of a safe operation space in a sensing area provided by an embodiment of the present application. Figure 4 As shown in , taking the shortest path from the starting position Q1 to the node position G1 of the first sensor node as an example, multiple path points in the shortest path are used as extension center points, and rectangular extension is performed until the extended rectangle touches the surface position of the obstacle, and these extended 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. The actual safe operating space should be a three-dimensional space.

[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, it is further expanded 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's information collection.

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

[0084] The following is a detailed description of step 300.

[0085] In some embodiments, after acquiring 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 that the smooth optimization model can be used to plan the operation of the UAV 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 operation trajectory function based on the operation variable parameters and the control point parameters, and generate a smooth optimization objective function based on the Bezier operation 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 operation variable parameters include the operation time t and operation direction in three-dimensional space in the operation planning of the UAV, where the operation direction includes data in three dimensions, namely μ∈{x,y,z}. The data interaction variable parameters include the transmission power of the sensor node, the amount of data collected by the UAV at each stage, 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 arbitrary positions, which can be expressed as the following formula (1).

[0092]

[0093] Where n is the order of the Bezier curve, [c 0 ,c 0 ,......,c n ] is the set of control points of this Bezier curve. Bezier curve has the following special properties: Bezier curve always starts from the first control point and ends at the last control point, and never passes through any other control points; the parameters of Bezier curve are defined in a fixed scale, that is, t∈[0,1]; all position points on a Bezier curve are restricted to the convex hull formed by the control points; the derivative curve of Bezier curve is still a Bezier curve. Next, based on the principle of Bezier curve, we will further describe in detail how to generate Bezier running trajectory function based on 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: 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 are obtained.

[0097] Step 323: Generate the running curve of 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 drone in each safe running space is initialized as a section of Bezier curve. Due to the particularity of the Bezier curve, it is necessary to allocate time in advance to the drone in the section of the Bezier curve.

[0100] This solution focuses on the trapezoidal allocation method to allocate time for each trajectory in 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 UAV acceleration flight time Drone deceleration flight time And the drone's uniform flight time Can get The time of acceleration and deceleration 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, and 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 Bezier trajectory function, including the following steps 324 to 328 .

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

[0110] Step 325: The running curve is subjected to a third-order derivative processing, a square processing, and then an integration processing to obtain a smooth integral function of 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, a smooth integral matrix is ​​obtained.

[0113] Step 328: 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.

[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 value is the third 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 scheme uses the integral of the square of the trajectory jerk to measure the trajectory quality, let it be the smoothness cost, and minimize it. The following is a further detailed description.

[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 using the vector of

[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, 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, a scalar coefficient sj is multiplied on each segment of the curve. The objective function of the jth segment trajectory can be deduced as shown in the following formula (6).

[0120]

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

[0122] Compared with the traditional polynomial minimization smoothness cost problem, the objective function expression based on Bezier curve minimization smoothness cost is relatively complex, which is not conducive to constructing the standard QP form. Therefore, we use the traditional polynomial coefficient P and the control point of the Bezier trajectory The linear transformation relationship transforms the objective function of minimizing the smoothness cost based on the Bessel trajectory.

[0123] The above formula (6) is simplified into the following formula (7).

[0124]

[0125] in, is the normalized Bessel trajectory. According to the definition of the Bessel trajectory and through the transformation matrix, it can be obtained as shown in the following formula (8).

[0126]

[0127] 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, we get the smooth transition matrix. The transformation matrix Λ n The details are shown in the following formula (9).

[0128]

[0129] Afterwards, based on the smooth transition matrix By taking the third-order derivative of the time parameter τ in the above formula (8), the following formula (10) is obtained.

[0130]

[0131] Then, the smoothness cost of the polynomial trajectory is the integral of the square as shown in the following formula (11).

[0132]

[0133] Among them aa T As shown in the following formula (13).

[0134]

[0135] Based on this aa T , and further obtain the Hessian matrix Q as shown in the following formula (14).

[0136]

[0137] Finally, based on multiplying 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 (15).

[0138]

[0139] Through the above steps 324 to 328, each running curve of the Bessel running trajectory function is converted 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.

[0140] Step 330: Generate a channel capacity function based on the data interaction variable parameters and the Bessel motion trajectory function, and generate data acquisition constraints based on the channel capacity function and the data interaction variable parameters.

[0141] Step 330 is described in detail below.

[0142] In some embodiments, while generating a smooth optimization objective function, in order to further ensure the reliability of information collection by the UAV within the perception area, that is, to ensure that the UAV 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.

[0143] 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 motion trajectory function, and then generate the appropriate data acquisition constraints based on the channel capacity function and the data interaction variable parameters.

[0144] The channel capacity function is generated based on the data interaction variable parameters and the Bessel operation trajectory function, including the following steps 331 to 333.

[0145] Step 331: Based on the running time, the Bessel running trajectory function and the node position, determine the distance function between the UAV and the node position.

[0146] Step 332: Generate a large-scale fading function based on the distance function and the path loss index, 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.

[0147] Step 333: Obtain the channel capacity function based on the product of the channel gain function and the transmit power.

[0148] Steps 331 to 333 are described in detail below.

[0149] 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 considers 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 , 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 (16).

[0150]

[0151] in, for The three-dimensional position of the drone at time , which is the Bessel trajectory function in The corresponding position.

[0152] Then, based on the distance function (16) and the path loss exponent α, a large-scale fading function is generated as shown in the following formula (17).

[0153]

[0154] 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.

[0155] Next, considering the influence of small-scale fading, based on the large-scale fading function (17), 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 (18).

[0156]

[0157] Among them, h LOS =1,h NLOS ~CN(0,1), Rayleigh flag and Lace flag are the flags of Rayleigh channel and 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-varying, 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 (18) 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 (19).

[0158]

[0159] Then, the signal-to-noise ratio (19) 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 (20).

[0160] R j (k) = Blog2(1+ρ j [k]) (20)

[0161] Where B represents the channel bandwidth in Hertz (Hz), σ 2 is the noise power of the drone’s receiver.

[0162] In some embodiments, the solution provided by the present application also considers the prediction stage of the next time period of the UAV during operation. In this prediction stage, the influence of obstacles on the trajectory is not considered. However, in order to ensure the completion of the actual communication task, the prediction stage considers the channel under the worse condition, that is, the communication model adopts the Rayleigh channel. Based on this, the predicted trajectory corresponding to the prediction stage can be expressed as shown in the following formula (21).

[0163]

[0164] 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 (22).

[0165]

[0166] Wherein, ρ[k] is the signal-to-noise ratio of the transmission data of the kth time slot in the prediction stage; H[k] is the channel gain of the kth time slot in the prediction stage, h[k] is the channel gain related to large-scale fading of the kth time slot in the prediction stage, and H[k]=h[k]gh NLOS ,

[0167] 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 .

[0168] Step 334: Accumulate all data collection amounts from the current moment to the present moment to obtain the first information data collection amount.

[0169] Step 335: Based on the product of the real-time channel function and the time interval, a second information data collection amount is obtained, and based on the product of the predicted channel function and the time interval, a third information data collection amount is obtained.

[0170] 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.

[0171] Steps 334 to 336 are described in detail below.

[0172] In some embodiments, the solution provided by the present application is a solution for local optimization in each safe operation space during the actual operation of the drone. In each local area, the drone only relies on the known local map to plan the trajectory to the global end point. During the flight, it will pass through multiple local areas, so multiple planning is required. The drone only plans the trajectory once in a local area, where the starting point of the first local planning is the global starting point, and the starting point of each subsequent local planning is the intersection of the last planned trajectory and the last local map.

[0173] Therefore, use It indicates 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 through 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 collected from the current moment to the present moment are accumulated to obtain the first information data collection amount as shown in the following formula (23).

[0174]

[0175] 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 (24).

[0176]

[0177] 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 size relationship of generates data acquisition constraints, so the data acquisition constraints collected by the drone in the data collection task corresponding to the qth safe operation space are shown in the following formula (25).

[0178]

[0179] Through the above steps 331 to 333, and steps 334 to 336, the operating position of the UAV in each time slot is determined by the Bessel running trajectory function, so as to further construct the real-time channel function between the UAV and the sensor node and the predicted channel function in the prediction stage by using the relevant Rayleigh fading channel modeling and Rice 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 by the second data information collection amount are used to generate the collectable data amount, and the data acquisition constraints generated according to the size relationship between the collectable data amount and the minimum data collection amount can effectively ensure that the UAV obtains sufficient information from multiple communication nodes within the sensing area.

[0180] Step 340: Generate a smooth optimization model based on the control point parameters, the smooth optimization objective function and the data acquisition constraints.

[0181] Step 340 is described in detail below.

[0182] In some embodiments, in addition to obtaining control point parameters, smooth optimization objective functions, and data acquisition constraints, other constraints need to be constructed to ensure the safe and reliable operation of the drone in the sensing area. The details are described below.

[0183] Since the high-order derivatives of the Bezier curve can be linearly represented by the coefficients of its low-order expression. Where l represents the order of the derivative to be calculated, and n is the degree of freedom of the Bezier curve. This feature will be used in setting velocity and acceleration constraints, as shown in the following formula (26).

[0184]

[0185] Based on this, for the l-th order derivative of the j-th trajectory, the relationship between its corresponding trajectory position, velocity, and acceleration is shown in the following formula (27).

[0186]

[0187] In addition, the drone needs to pass through specific path points. These specific path points in this application include the starting point and target point of each safe operation space. The position, speed and acceleration at these specific points need to be constrained. Since the first and last control points of the Bezier curve in each safe operation space must be the first and last trajectory points of the drone's entry and exit trajectory in the safe operation space, the path point constraint can be directly expressed by the control point as shown in the following formula (28).

[0188]

[0189] in l represents the starting point of the jth trajectory th For the end point, replace 0 with n. This formula shows that for the trajectory point, it is necessary to multiply the scaling factor, but not for the velocity, and for the acceleration, it is necessary to divide the scaling factor.

[0190] Because the trajectory in each safe operation space is initialized as a Bezier curve, in order to ensure the continuity of the overall trajectory, it is necessary to make the first and last control points of the Bezier curves corresponding to each two adjacent safe operation spaces 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 (29).

[0191]

[0192] In addition, due to the convex hull property of the Bezier curve, it can be known that the trajectory point must be within the geometric convex hull formed by the control points. Therefore, as long as the convex hull is restricted within the safe operation space, the trajectory point will also be restricted within the safe operation space. Therefore, in order to make each control point fall within the flight corridor, it is necessary to formulate a safety constraint as shown in the following formula (30).

[0193]

[0194] 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 its constraints. Combined with the high-order characteristics of the Bezier curve, the dynamic constraint can be expressed as shown in the following formula (31).

[0195]

[0196] Based on all the above relevant constraints (including path point constraints, UAV safety constraints, UAV 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 (32).

[0197]

[0198] Among them, the objective function is a smooth optimization objective function, and the optimization variables are control point parameters. The constraints include the position, speed and acceleration constraints of the necessary points, the continuity constraints of the control points, the safety constraints, the boundary constraints of speed and acceleration, and the constraints of the amount of data collection.

[0199] In this embodiment, by solving the problem of each safe operation space, only the trajectory of the local safe operation space is executed. Since the optimization target 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 a non-convex constraint. This problem is a non-convex quadratic programming problem, which can be converted to a convex problem using SCA and then the Mosek solver is called for iterative solution. The specific conversion is as follows.

[0200] Since the data acquisition constraint in (32) is non-convex, this optimization problem is a non-convex QCQP problem. Therefore, D1 in the data acquisition constraint q Expressed in control point form, the data transmission rate can be obtained as shown in the following formula (33).

[0201]

[0202] 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, according to the definition of the Bezier curve trajectory, the following formula (34) can be obtained.

[0203]

[0204] Substituting formula (34) into the data transmission rate formula (33), the following formula (35) can be obtained.

[0205]

[0206] However, the new transformed constraint (35) is non-convex. But for any given local control point at the rth iteration The available transmission rate R j The global concave lower bound of (k) The specific expression is shown in the following formula (36).

[0207]

[0208] And the following formula (37).

[0209]

[0210] The R found by (36) j The global lower bound of (k) Replace R in the data acquisition constraint j (k) You can also The same 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 (38), forming a new smooth optimization model.

[0211]

[0212] 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.

[0213] Let r be the number of iterations, for The r+1th iteration of has the following inequality as shown in formula (39).

[0214]

[0215] Right is the transmission rate R j (k) has a global concave lower bound, so the solver can be used to iteratively solve the smooth optimization model (38).

[0216] 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 operating 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.

[0217] The following is a detailed description of step 400.

[0218] 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 combined with the inertial conduction unit and other sensors are used 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) in accordance with the flight order of the drone. Then, the smooth operation model (38) of the safe operation space is solved according to these information parameters to obtain the stage optimization control points in the safe operation space, and the drone is controlled to operate in the safe operation space according to the stage optimization control points until the drone collects information from multiple sensor nodes in turn and moves to the end position.

[0219] Wherein, based on the operation parameters and the data interaction parameters, before solving the smooth optimization model to obtain the stage optimization control points, the information collection method of the perception area also includes the following steps 410 to 420.

[0220] 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.

[0221] Step 420: Update the safe operating space based on the updated obstacle surface position.

[0222] Steps 410 to 420 are described in detail below.

[0223] In some embodiments, since the overall obstacle information initially acquired by the drone may contain incomplete data information on all obstacles in the sensing area, it is necessary to acquire obstacle information near the drone again to obtain regional obstacle information each time the drone enters a new safe operating space, and determine updated obstacle surface positions of multiple obstacles close to the drone based on the regional obstacle information, and then update the current safe operating space and subsequent safe operating spaces based on the updated obstacle surface positions to improve the safety of the drone during operation.

[0224] The information collection method and related equipment of the perception area proposed in the embodiment of the present application, the perception area includes multiple sensor nodes and at least one obstacle, the method is applied to a drone, and the method includes: first, obtaining the starting position and the end position of the drone, 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 every two adjacent node positions one by one, the shortest path includes multiple path points, each path point is used as the extension center point of the extension rectangle one by one, and the rectangle is extended based on the extension center point, Until the extended rectangle reaches the position of the obstacle surface, the extended rectangle is used as the safe operating space; next, the operating variable parameters, data interaction variable parameters and control point parameters are obtained, the curve scale factor corresponding to each stage and the initial time of each stage are obtained, based on the difference between the operating time and the initial time of the stage, and divided by the curve scale factor, the Bezier curve parameters are obtained, and the operating curve of each stage is generated based on the operating direction, curve scale factor, control point parameters, Bezier curve and Bezier curve parameters, and the conversion matrix of each stage is obtained, the operating curve is subjected to triple derivative processing, square processing, and then integration processing to obtain To the smooth integral function of each stage, based on the smooth integral function and the control matrix, the smooth transition matrix is ​​obtained, based on the smooth transition matrix, the smooth integral matrix is ​​obtained, based on the smooth transition matrix multiplied by the smooth integral matrix, and then multiplied by the transposed matrix of the smooth transition matrix, the smooth optimization objective function of each stage is obtained, based on the running time, the Bessel running trajectory function and the node position, the distance function between the UAV and the node position is determined, based on the distance function and the path loss exponent, the large-scale fading function is generated, based on the large-scale fading function, the Rayleigh fading function and the Rice fading function, the channel gain function between the UAV and the sensor node is generated, based on the channel gain The product of the benefit function and the transmission power is used to obtain the channel capacity function, and all the data collection amounts before the current moment are accumulated to obtain the first information data collection amount, 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, the first information data collection amount, the second information data collection amount and the third information data collection amount are accumulated to obtain the collectible data amount, and the data acquisition constraint is generated based on the size relationship between the collectible data amount and the minimum data collection amount, and the smooth optimization model is generated based on the control point parameters, the smooth optimization objective function and the data acquisition constraint;Finally, according to the flight sequence of the drone, the operating parameters of the drone are obtained one by one in each safe operation space, as well as the data interaction parameters between the drone and the sensor node, and the regional obstacle information is obtained. Based on the regional obstacle information, the updated obstacle surface positions of multiple obstacles close to the drone are determined, and the safe operation space is updated based on the updated obstacle surface positions. Based on the operating parameters and data interaction parameters, the smooth 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. ;

[0225] 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 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 UAV in these safe operation spaces to improve the safety of the UAV in collecting information; and, utilizes the Bessel curve to construct the Bessel operation trajectory function of the operation planning trajectory of the UAV, so as to facilitate the optimization parameters to be further refined to the control point for the control of the UAV, so as to facilitate the control, and utilizes the continuous and smooth characteristics of the Bessel curve, so as to facilitate the subsequent use of the smooth optimization objective function generated by the Bessel operation trajectory function for trajectory optimization, which can effectively improve the operation smoothness of the UAV and thus effectively reduce energy consumption; and, utilizes the operation position of the UAV in each time slot determined by the Bessel operation trajectory function, so as to further construct the real-time channel function between the UAV and the sensor node and the predicted channel function in the prediction stage by utilizing the relevant Rayleigh fading channel modeling and Rice 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. According to the data acquisition constraints generated by the relationship between the collectible data amount and the minimum data collection amount, it can be effectively ensured that the UAV obtains sufficient information from multiple communication nodes within the perception area.

[0226] The present application also provides a sensing area information collection device, which can implement the sensing area information collection method. Figure 5 , the device 500 comprises:

[0227] A node position acquisition module 510 is used to acquire the node position of each sensor node and the overall obstacle information of at least one obstacle, wherein the node position includes a starting position and an end position;

[0228] A space generation module 520, for generating a plurality of safe operation spaces from a starting position to an end position based on the node position and the overall obstacle information;

[0229] A model acquisition module 530 is used to acquire a smooth optimization model, where the smooth optimization model includes data acquisition constraints and a smooth optimization objective function;

[0230] 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.

[0231] In some embodiments, the space generation module 520 is further configured to:

[0232] determining an obstacle surface position of at least one obstacle based on the overall obstacle information;

[0233] Generate the shortest path between every two adjacent node positions one by one, and the shortest path includes multiple path points;

[0234] 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.

[0235] In some embodiments, the model acquisition module 530 is further configured to:

[0236] Obtain running variable parameters, data interaction variable parameters and control point parameters;

[0237] Generate a Bezier running trajectory function based on the running variable parameters and the control point parameters, and generate a smoothing optimization objective function based on the Bezier running trajectory function;

[0238] Generate a channel capacity function based on the data interaction variable parameter and the Bessel operation trajectory function, and generate a data acquisition constraint based on the channel capacity function and the data interaction variable parameter;

[0239] A smooth optimization model is generated based on control point parameters, smooth optimization objective function and data acquisition constraints.

[0240] In some embodiments, the model acquisition module 530 is further configured to:

[0241] Get the curve scale factor corresponding to each stage and the initial time of each stage;

[0242] 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 are obtained;

[0243] 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.

[0244] In some embodiments, the model acquisition module 530 is further configured to:

[0245] Get the transformation matrix of each stage;

[0246] The running curve is derivatized three times, squared, and then integrated to obtain a smooth integral function at each stage.

[0247] A smooth transition matrix is ​​obtained based on the smooth integral function and the control matrix;

[0248] Based on the smooth transition matrix, a smooth integral matrix is ​​obtained;

[0249] 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.

[0250] In some embodiments, the model acquisition module 530 is further configured to:

[0251] Based on the running time, Bessel running trajectory function and node position, determine the distance function between the UAV and the node position;

[0252] 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;

[0253] The channel capacity function is obtained based on the product of the channel gain function and the transmission power.

[0254] In some embodiments, the model acquisition module 530 is further configured to:

[0255] Accumulate all data collection amounts before the current moment to obtain the first information data collection amount;

[0256] Based on the product of the real-time channel function and the time interval, a second information data collection amount is obtained, and based on the product of the predicted channel function and the time interval, a third information data collection amount is obtained;

[0257] The first information data collection amount, the second information data collection amount, and the third information data collection amount are accumulated to obtain the collectible data amount, and a data acquisition constraint is generated based on a size relationship between the collectible data amount and the minimum data collection amount.

[0258] In some embodiments, the operation control module 540 is further configured to:

[0259] Obtaining regional obstacle information, and determining updated obstacle surface positions of a plurality of obstacles approaching the UAV based on the regional obstacle information;

[0260] The safe operating space is updated based on the updated obstacle surface position.

[0261] In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, the specific implementation method of the information collection device of the perception area is basically the same as the specific implementation method of the information collection method of the perception area, and will not be repeated here.

[0262] 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 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, the Bessel curve is used to construct the Bessel operation trajectory function of the operation planning trajectory of the drone, 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 the control, and the Bessel curve can provide continuous and smooth characteristics, so as to facilitate the subsequent use of the smooth optimization objective function generated by the Bessel operation trajectory function for trajectory optimization, which can effectively improve the operation smoothness of the drone and thus effectively reduce energy consumption; and, the operation position of the drone in each time slot determined by the Bessel operation trajectory function is used 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 Rice 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. According to the data acquisition constraints generated by the relationship between the collectible data amount and the minimum data collection amount, it can be effectively ensured that the UAV obtains sufficient information from multiple communication nodes within the perception area.

[0263] The present application also provides an electronic device, including:

[0264] at least one memory;

[0265] at least one processor;

[0266] at least one program;

[0267] The program is stored in the memory, and the processor executes the at least one program to implement the above-mentioned sensing area information collection method implemented in this application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0268] See also Figure 6 , Figure 6 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0269] The processor 601 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (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;

[0270] 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 operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are 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;

[0271] Input / output interface 603, used to implement information input and output;

[0272] Communication interface 604, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0273] 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 );

[0274] 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 .

[0275] 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.

[0276] 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 disposed 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.

[0277] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present 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 the present application are also applicable to similar technical problems.

[0278] Those skilled in the art will appreciate 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.

[0279] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0280] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0281] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0282] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: 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 indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural 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, c can be single or multiple.

[0283] In the several embodiments provided in the present 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. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0284] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0285] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0286] 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, 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, and the computer software product is stored in a storage medium, including multiple instructions to enable 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 (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0287] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights 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 end position of the UAV, and obtaining 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 smooth optimization model, wherein the smooth optimization model includes a data acquisition constraint and a smooth 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 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.

2. The method for collecting information of a sensing area according to claim 1, characterized in that: 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 comprises: determining an obstacle surface position of at least one of the obstacles based on the overall obstacle information; Generating the 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 of a sensing area according to claim 1, characterized in that: The obtaining of the smooth optimization model comprises: Obtain running variable parameters, data interaction variable parameters and control point parameters; Generate a Bezier operation trajectory function based on the operation variable parameters and the control point parameters, and generate the smoothing optimization objective function based on the Bezier operation trajectory function; Generate a channel capacity function based on the data interaction variable parameter and the Bessel operation trajectory function, and generate the data acquisition constraint based on the channel capacity function and the data interaction variable parameter; The smooth optimization model is generated based on the control point parameters, the smooth optimization objective function and the data acquisition constraints.

4. The method for collecting information of a sensing area according to claim 3, characterized in that: The running variable parameters include running time and running direction, the Bezier running trajectory function includes running curves of multiple stages, and the generating of the Bezier running trajectory function based on the running variable parameters and the control point parameters includes: Obtaining the curve scale factor corresponding to each of the stages and the initial time of each stage; Based on the difference between the running time and the initial time of the stage, and divided by the curve scale factor, a Bezier curve parameter is obtained; The running curve of each of the stages is generated based on the running direction, the curve scale factor, the control point parameters, the Bezier curve, and the Bezier curve parameters.

5. The method for collecting information of a sensing area according to claim 4, characterized in that: The step of generating the smooth optimization objective function based on the Bessel motion trajectory function comprises: Obtaining the transformation matrix of each of said stages; The operation curve is subjected to a third-time derivative processing, a square processing, and then an integration processing 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.

6. The method for collecting information of a sensing area according to claim 4, characterized in that: The data interaction variable parameter includes the transmission power of the sensor node, and the generating of the channel capacity function based on the data interaction variable parameter and the Bessel operation trajectory function includes: Determine a distance function between the UAV and the node position based on the running time, the Bessel running trajectory function and the node position; Generate a large-scale fading function based on the distance function and the path loss index, 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; The channel capacity function is obtained based on the product of the channel gain function and the transmit power.

7. The method for collecting information of a sensing area according to claim 4, characterized in that: The data interaction variable parameters also include the data collection amount and time interval of the drone at each stage, the channel capacity function includes a real-time channel function and a predicted channel function, and the data acquisition constraints are generated based on the channel capacity function and the data interaction variable parameters, including: Accumulate all the data collection amounts before the current moment to obtain the first information data collection amount; Based on the product of the real-time channel function and the time interval, a second information data collection amount is obtained, and based on the product of the predicted channel function and the time interval, a third information data collection amount is obtained; 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.

8. The method for collecting information of a sensing area according to claim 1, characterized in that: Before solving the smooth 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.

9. 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 8 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the information collection method of the perception area according to any one of claims 1 to 8 is implemented.

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