Control simulation system for distributed multi-UAV swarm collaborative space search scheduling

Abstract

The present invention relates to a control simulation system for coordinated spatial search and scheduling of distributed multi-UAV clusters, comprising: a coordinated search and scheduling unit for updating a shared probability occupancy map based on a dynamic scene depth map of each simulated UAV, searching for a boundary set for distinguishing unknown and known areas of the map and the best observation point position corresponding to each boundary in the boundary set; performing observation point scheduling to assign an optimal observation point position to each simulated UAV as the next target point of the simulated UAV; the simulated UAV is used to receive the corresponding target point position to perform trajectory planning to generate a continuous expected trajectory; and performing simulated flight in a virtual simulation environment according to the continuous expected trajectory to verify the stability and safety of the distributed cluster operation. The present invention provides a complete simulation platform that can realize environmental perception, algorithm verification, and maneuverable flight control.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) intelligent control, and in particular relates to a control simulation system for coordinated spatial search and scheduling of a distributed multi-UAV cluster. Background Art

[0002] The main method of existing multi-UAV search is the formation method, which uses multiple UAVs to form a certain shape of formation to expand the perception range of the UAVs, so that the entire formation can quickly complete the search task of the area. However, this method is not suitable for collaborative search in complex spaces, because there are often many obstacles in complex spaces. In this case, maintaining the formation and avoiding obstacles are contradictory requirements. To meet both requirements at the same time, it is necessary to find reasonable parameter settings, which requires a long period of debugging. At the same time, the advantage of the formation is to expand the perception range. Due to the various structures of complex spaces, the expansion of the perception range is limited, and the advantages of the formation cannot be fully utilized, which may cause the method to run slowly in this environment and fail to meet the needs of rapid collaborative traversal of space.

[0003] At the same time, the existing multi-UAV collaborative search simulation verification platforms are mostly concentrated in MATLAB / SIMULINK and other systems with poor visualization, modularity and portability. And they only focus on the simulation verification of the algorithm itself, ignoring the constraints of the real environment space complexity, sensor perception ability, data communication ability and flight control ability. If the UAV space search algorithm is verified without considering the above multiple capability constraints, then the verified search algorithm will inevitably fail when it is verified in the real environment. Summary of the invention

[0004] In view of the above analysis, the present invention aims to disclose a control simulation system for coordinated spatial search scheduling of distributed multi-UAV clusters to solve the simulation verification problem of coordinated spatial search scheduling of multiple UAVs in complex indoor environments.

[0005] The present invention discloses a distributed multi-UAV cluster collaborative space search and scheduling control simulation system, which is characterized by comprising: a collaborative search and scheduling unit and a plurality of simulated UAVs connected thereto;

[0006] The collaborative search scheduling unit is used to perform collaborative space search scheduling according to the received dynamic scene depth map output frame by frame by each simulated UAV during simulated flight; in the collaborative space search scheduling, the shared probability occupancy map is updated based on the dynamic scene depth map of each simulated UAV, and a boundary set for distinguishing unknown and known areas of the map and the best observation point position corresponding to each boundary in the boundary set are searched; observation point scheduling is performed to assign an optimal observation point position to each simulated UAV as the next target point of the simulated UAV;

[0007] The simulated UAV is used to receive the corresponding target point position to perform trajectory planning to generate a continuous expected trajectory; and perform simulated flight in a virtual simulation environment according to the continuous expected trajectory to verify the stability and safety of the distributed cluster operation.

[0008] Further, the collaborative search scheduling unit includes a shared map processing subunit, a boundary search division subunit and a cluster scheduling allocation subunit;

[0009] A shared map processing subunit, used to share maps based on scene depth maps of respective locations output by multiple drones, and generate a shared probability occupancy map;

[0010] The boundary search division subunit is used to occupy the map information in the map according to the shared probability, perform boundary search to obtain a boundary set and a boundary optimal viewpoint set corresponding to the boundary combination;

[0011] The cluster scheduling allocation subunit is used to perform operations optimization allocation based on the number of observation points and the number of drones in the boundary optimal observation point set, as well as the cost from the current position of the drone to the observation point, and allocate each drone to the next target point.

[0012] Further, the shared map processing subunit includes a mask determination module, a mask operation module, a shared probability update module and a shared map generation module;

[0013] The obstruction determination module is used to determine mutual obstruction between multiple drones based on the scene depth map of each simulated drone at its location, and determine the drones that are obstructed;

[0014] The mask operation module is used to perform a mask operation on the depth information photographed by the obscured drone to obtain a processed depth map;

[0015] The shared probability occupancy map module is used to convert the depth map into an obstacle point cloud based on the processed depth information, and generate a shared probability occupancy map based on the obstacle point clouds observed simultaneously by all drones;

[0016] The shared probability occupancy map is composed of a dynamic array, in which each data unit stores the observation probability obtained by multiple drones and the state corresponding to the map element determined by the observation probability; and the observation probability and observation state are updated according to the obstacle point cloud updated frame by frame.

[0017] Further, the boundary search division subunit includes a boundary generation module and an optimal observation point generation module;

[0018] The boundary generation module is used to search the current state of each data unit in the shared probability occupation map to find a boundary set for distinguishing unknown and known areas of the map;

[0019] The data unit at the intersection of the unknown state and the free state in the shared probability occupation map belongs to the point on the boundary, its own state is the free state, and the state of its neighboring unit is the unknown state;

[0020] The optimal observation point generation module is used to search for the observation point position that maximizes the observation efficiency of each boundary in the boundary set;

[0021] During the search process, the optimal observation point position is determined by searching the mean point of the boundary, the positions of the two spatial points farthest apart at both ends of the boundary, the normal line of the center of the boundary, and the center point position calculated based on the positions of the two spatial points farthest apart at both ends of the boundary and the perception field of view FOV of the depth camera.

[0022] Furthermore, the cluster scheduling and allocation subunit includes an optimization problem model, an observation point and drone quantity determination module, and a model optimal solution seeking module;

[0023] The optimization problem model is used to convert M observation points VP m ∈{VP 1 ,…,VP M}Assigned to N UAVs n ∈{UAV 1 ,…,UAV N}, a UAV is assigned only one observation point, and after the assignment, all UAVs reach their respective observation points at the minimum cost;

[0024] The observation point and drone number determination module is used to determine the number of observation points M and the number of drones N;

[0025] The model optimal solution seeking module is used to solve the optimization problem model according to the judgment results of the observation point and drone number judgment module, and assign an observation point to each drone as the next target point;

[0026] Specifically, when M<N, the optimal solution is sought:

[0027]

[0028]

[0029] x mn ≤y n ,

[0030]

[0031] x mn ∈{0,1},

[0032] y n ∈{0,1},

[0033] When M>N, the optimal solution is sought:

[0034]

[0035]

[0036] x mn ≤y n ,

[0037]

[0038] x mn ∈{0,1},

[0039] y n ∈{0,1},

[0040] y n ∈{0,1} represents UAV n Whether to participate in the search; x mn ∈{0,1} means that the observation point VP m Assigned to UAV n Represented as x mn ∈{0,1}; among them, UAV n To observation point VP m The cost is denoted as d mn , the total cost of all drones reaching their respective observation points is expressed as The total cost is minimized through allocation optimization.

[0041] Furthermore, UAV n To observation point VP m The cost is the total observation cost d consisting of distance cost, speed cost and speed change cost mn .

[0042] Furthermore, the simulated UAV is a simulated quad-rotor UAV, comprising a trajectory planning module, a first type of simulated sensor, a second type of simulated sensor, a nonlinear simulated controller and four simulated motors;

[0043] The trajectory planning module is used to perform trajectory planning according to the assigned target points, obtain a continuous expected trajectory that takes into account obstacle avoidance and collision avoidance between drones, and output it to the nonlinear simulation controller;

[0044] The first type of simulated sensor includes a simulated depth camera; it is used to obtain front-end sensor data including a dynamic scene depth map from the scene information of the virtual environment where the simulated drone is located, and output it to the collaborative search scheduling unit;

[0045] The second type of simulation sensors include posture measurement sensors including simulated IMU, GPS module, barometer and magnetometer, which are used to perceive the posture information of the simulated drone from the natural environment simulation data of the virtual environment in which the simulated drone is located;

[0046] The nonlinear simulation controller is used to perform nonlinear control according to the expected trajectory and the simulated UAV posture information sensed by the second type of simulation sensor, and output the motor speed corresponding to the simulated motor;

[0047] The simulation motor is used to simulate the thrust, air resistance, rotational torque and air resistance torque generated by the drone using a brushless DC motor during the rotation of the propeller according to the motor speed output by the nonlinear simulation controller; so as to obtain the resultant force and resultant torque exerted on the simulated four-rotor drone in the simulation environment under the action of four simulation motors.

[0048] Further, the trajectory planning module includes a primary trajectory planning module, a secondary trajectory planning module and a trajectory output module;

[0049] The primary trajectory planning module is used to perform path planning according to the obtained next target point position to generate a continuous expected trajectory for each simulated UAV;

[0050] The secondary trajectory planning module is used to determine the collision between drones based on the continuous expected trajectories of each simulated robot in the same time period; for the two simulated drones that are judged to have collided, secondary path planning is performed to generate a continuous expected trajectory to avoid the collision of the drones;

[0051] The trajectory output module is used to output the continuous expected trajectory of each simulated UAV to the flight controller of each UAV for flight simulation.

[0052] Furthermore, the secondary trajectory planning module includes a time sampling module, a sampling position point calculation module, a collision judgment module, a planning module and a trajectory output module;

[0053] The time sampling module is used for multiple drones to transmit trajectory curves in the same time period to each other, sample the time period, and obtain multiple sampling moments;

[0054] The sampling position point calculation module is used to calculate the sampling position point of each UAV at each sampling time for the continuous expected trajectory of all UAVs;

[0055] The collision judgment module is used to judge whether the distance between the position points of any two UAVs at the same sampling time is within the set safety distance; if yes, the expected trajectory of the UAV is input into the planning module;

[0056] The quadratic planning module is used to determine two Bezier curve control points with optimized positions in the respective expected trajectories of the two UAVs; the two Bezier curve control points are the two Bezier curve control points closest to the position point within the safe distance; the two Bezier curve control points are updated, and the connection line between the two Bezier curve control points is extended in a direction away from the expected trajectory of the other party, so that the distance between the position points on the connection line at the same time exceeds the safe distance;

[0057] The trajectory output module is used to reconstruct the Bezier trajectory curve according to the updated control points to obtain the continuous expected trajectory of multiple UAVs that meets the safety obstacle avoidance requirements.

[0058] Furthermore, it also includes a virtual simulation environment unit; the virtual simulation environment unit communicates data with the simulated drone; the simulated virtual scene information is output to the first type of simulation sensor of the simulated drone, so that the first type of simulation sensor can perceive the scene information and obtain front-end sensor data including dynamic scenes; according to the resultant force and torque exerted on the four-rotor drone in the simulation environment by the simulated motor output, the simulated natural environment simulation data including simulated force field, atmospheric field and magnetic field are output to the second type of simulation sensor of the simulated drone, so that the second type of simulation sensor can perceive the posture information of the drone according to the natural environment simulation data.

[0059] The present invention can achieve one of the following beneficial effects:

[0060] The simulation system of the present invention overcomes the problem of lack of a fast, complete, concise and clear collaborative spatial search scheduling and simulation verification environment for multiple UAVs in complex spaces in the field of intelligent control of UAVs; it provides researchers in this field with a complete simulation platform that can realize environmental perception, algorithm verification, and maneuverable flight control, and solves the problem that the influence of other module factors cannot be considered in the collaborative spatial search scheduling of UAVs.

[0061] The collaborative spatial search scheduling of the present invention solves the problem of independent mutual perception of distributed cluster systems, improves the system's ability to perceive environmental consistency, and effectively avoids interference from invalid perception between machines. In addition, a method of operations optimization is used to allocate tasks for distributed clusters, solving the problem of lack of planning in distributed cluster operations, and minimizing the time and energy consumption of the entire system tasks on a global scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0063] Figure 1 It is a principle block diagram of a control simulation system for distributed multi-UAV cluster collaborative space search scheduling in an embodiment of the invention;

[0064] Figure 2 It is a principle block diagram of a collaborative search scheduling unit in an embodiment of the invention;

[0065] Figure 3 It is a principle block diagram of a shared map processing subunit in an embodiment of the invention;

[0066] Figure 4 It is a principle block diagram of the boundary search division subunit in an embodiment of the invention;

[0067] Figure 5 It is a principle block diagram of a cluster scheduling allocation subunit in an embodiment of the invention;

[0068] Figure 6 This is a block diagram of the connection principle of the simulated drone components in the embodiment of the invention;

[0069] Figure 7 It is a principle block diagram of the trajectory planning module in an embodiment of the invention. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0071] The embodiment of the present invention discloses a distributed multi-UAV cluster collaborative space search and scheduling control simulation system, such as Figure 1 As shown, it includes: a collaborative search scheduling unit and multiple simulated drones connected thereto, and a virtual simulation environment unit;

[0072] The collaborative search scheduling unit is used to perform collaborative space search scheduling according to the received dynamic scene depth map output frame by frame by each simulated UAV during simulated flight; in the collaborative space search scheduling, the shared probability occupancy map is updated based on the dynamic scene depth map of each simulated UAV, and a boundary set for distinguishing unknown and known areas of the map and the best observation point position corresponding to each boundary in the boundary set are searched; observation point scheduling is performed to assign an optimal observation point position to each simulated UAV as the next target point of the simulated UAV;

[0073] The simulated UAV is used to receive the corresponding target point position to perform trajectory planning to generate a continuous expected trajectory; and perform simulated flight in a virtual simulation environment according to the continuous expected trajectory to verify the stability and safety of the distributed cluster operation;

[0074] The virtual simulation environment unit communicates data with the simulated drone; outputs the simulated virtual scene information to the simulated depth camera of the simulated drone, so that the simulated depth camera can perceive the scene information and obtain front-end sensor data including the depth map of the scene at the location; outputs the simulated force field, atmospheric field, magnetic field and other natural environment simulation data to the simulated IMU, GPS module, barometer and magnetometer in the simulated drone, so that the simulated IMU, GPS module, barometer and magnetometer can perceive the posture information of the drone according to the natural environment simulation data.

[0075] Among them, in the collaborative search scheduling unit, multi-UAV space exploration requires the UAVs to share the perceived scene information to generate and update the shared map. Since the depth cameras of the UAVs can perceive each other, when sharing the map, each UAV needs to mask the neighboring UAVs within its field of view.

[0076] Specifically, Figure 2 As shown, the collaborative search scheduling unit includes: a shared map processing subunit, a boundary search division subunit and a cluster scheduling allocation subunit;

[0077] A shared map processing subunit, used to share maps based on scene depth maps of respective locations output by multiple drones, and generate a shared probability occupancy map;

[0078] The boundary search division subunit is used to occupy the map information in the map according to the shared probability, perform boundary search to obtain a boundary set and a boundary optimal viewpoint set corresponding to the boundary combination;

[0079] The cluster scheduling allocation subunit is used to perform operations optimization allocation based on the number of observation points and the number of drones in the boundary optimal observation point set, as well as the cost from the current position of the drone to the observation point, and allocate each drone to the next target point.

[0080] like Figure 3 As shown, the shared map processing subunit includes a mask determination module, a mask operation module, a shared probability update module and a shared map generation module;

[0081] The obstruction determination module is used to determine mutual obstruction between multiple drones based on the scene depth map of each simulated drone at its location, and determine the drones that are obstructed;

[0082] The determination of the existence of a concealed drone includes two determinations;

[0083] First judgment: when obtaining the updated depth map and global position of each drone, the distance between any two drones is judged. If the distance between the two drones is greater than the perception distance of the depth camera, it is determined that there is no mutual perception between the two drones; otherwise, the second judgment is entered;

[0084] Secondary judgment: determine whether one of the drones is within the field of view of the other drone based on the relative position vector of the two drones and the viewing angle vector of the onboard depth camera.

[0085] Specifically, in the secondary determination of UAV i Is it present in UAV j Within the field of view, it is determined by the following formula:

[0086] Result = minf(sign(dir ij dir r )),r∈0,1,2,3;

[0087] in, dir r For UAV j The viewing angle vector of dir ij UAV i and UAVs j The relative position vector of ; sign is a mathematical symbol function;

[0088] UAV i 、UAV j Global location data of

[0089] UAV j The perspective vector dir r for:

[0090]

[0091]

[0092]

[0093]

[0094] in, UAV i The rotation matrix from the camera coordinate system to the global coordinate system, angle top 、angle bottom 、angle left and angle right UAV i The camera's upward, downward, left and right viewing angles.

[0095] If Result is 0, it means UAV i Not in UAV j If it is within the field of view, it means it appears within the field of view and affects the estimation of the depth camera.

[0096] The mask operation module is used to perform a mask operation on the depth information photographed by the obscured drone to obtain a processed depth map;

[0097] During the mask operation, the UAV i The spatial position is converted to UAV j In the camera coordinate system:

[0098]

[0099]

[0100] in, From global coordinate system to UAV j The transformation matrix of the camera coordinate system, For UAV i In UAV j The coordinates in the camera coordinate system, is the intrinsic parameter matrix of the UAV depth camera;

[0101] Thus obtaining UAV i The projected pixel coordinates [u ij ,v ij ,1] T , its projection shape on the depth map is simplified to a circle, the radius of which is the UAV i Wheelbase L i The equivalent projection length on the pixel plane is r i , expressed as:

[0102]

[0103] In the pixel plane, it satisfies (uu ij ) 2 +(vv ij ) 2 ≤r i The depth of the pixel is set to 0, thus completing the mask operation.

[0104] The shared probability occupancy map module is used to convert the depth map into an obstacle point cloud based on the processed depth information, and generate a shared probability occupancy map based on the obstacle point clouds observed simultaneously by all drones;

[0105] The shared probability occupancy map is composed of a dynamic array, in which each data unit stores the observation probability obtained by multiple drones and the state corresponding to the map element determined by the observation probability; and the observation probability and observation state are updated according to the obstacle point cloud updated frame by frame.

[0106] Specifically, in the process of converting obstacle point cloud,

[0107] According to the intrinsic and extrinsic data of the depth camera, the processed depth information is converted into the depth information corresponding to the two-dimensional pixel coordinates in the depth map into a three-dimensional obstacle space point cloud in the global coordinate system;

[0108] Among them, according to the camera's intrinsic parameter data, the pixel plane is mapped to the camera coordinate system as follows:

[0109]

[0110] Among them, u and v are the two-dimensional pixel coordinates of the image; X, Y, and Z are the coordinates of the camera coordinate system; is the internal parameter matrix, f x 、f y 、c x 、c y is the internal parameter of the camera;

[0111] According to the camera's external parameter data, the coordinates in the camera coordinate system are transferred to the global coordinate system as follows:

[0112]

[0113] Among them, X w , Y w , Z w is the three-dimensional coordinate in the global coordinate system; R is the rotation matrix of the camera extrinsic parameter, and t is the translation vector of the camera extrinsic parameter.

[0114] The shared probability occupancy map is composed of a dynamic array, in which each data unit stores the observation probability obtained by multiple drones and the state corresponding to the map element determined by the observation probability; and the observation probability and observation state are updated according to the obstacle point cloud updated frame by frame.

[0115] Specifically, when the shared probability occupancy map is initialized, an initial value is assigned to each data unit, indicating that the initial state of all data units with respect to the map element is an unknown state;

[0116] When the current observation state of a map element in the updated 3D obstacle point cloud is occupied, the updated observation probability S in the corresponding data unit of the shared probability occupation map is new =S old +loUAV;

[0117] Among them, S old is the probability of the previous observation; loUAV is the numerical increment of the state occupied in the current observation; p hit is the pre-set probability of observing an occupied state, 0.5 <p hit <1.

[0118] For shared maps among multiple drones, the map unit is in the UAV i In the UAV j It may be free in one drone, and it may be in another state in other drones, so the state of map units in the shared map is determined by all drones.

[0119] So the probability of the current observation updated in the shared probability occupancy map is determined by all drones:

[0120]

[0121] Among them, S old,share is the previous observation probability; loUAV i UAV i The current observation is the numerical increment of the occupied state; N is the number of drones.

[0122] By updating, when the shared probability occupies the data unit of the map, the observation probability S new,share When a certain threshold is reached, the map unit storing the value is marked as occupied; when it is lower than the threshold, it is marked as free; when it is equal to the initialization value, it is marked as unknown.

[0123] The threshold value can be preset according to actual conditions.

[0124] like Figure 4As shown, the boundary search division subunit includes a boundary generation module and an optimal observation point generation module;

[0125] The boundary generation module is used to search the current state of each data unit in the shared probability occupation map to find a boundary set for distinguishing unknown and known areas of the map;

[0126] The data unit at the intersection of the unknown state and the free state in the shared probability occupation map belongs to the point on the boundary, its own state is the free state, and the state of its neighboring unit is the unknown state;

[0127] The optimal observation point generation module is used to search for the observation point position that maximizes the observation efficiency of each boundary in the boundary set;

[0128] During the search process, the optimal observation point position is determined by searching the mean point of the boundary, the positions of the two spatial points farthest apart at both ends of the boundary, the normal line of the center of the boundary, and the center point position calculated based on the positions of the two spatial points farthest apart at both ends of the boundary and the perception field of view FOV of the depth camera.

[0129] Among them, in the boundary generation module, the data unit at the intersection of the unknown state and the free state in the shared probability occupation map is classified as a boundary, its own state is a free state, and its neighboring unit state is an unknown state. Specifically, it can be expressed as:

[0130] F = {cell 0,free ,…,cell l,free ,…,cell L,free};

[0131] Neighbor(cell l,free )∈Unknown;

[0132] Among them, Unknown represents the set of unknown states.

[0133] There are multiple boundaries on the map. 0 ,…,F m ,…,F M} constitutes a boundary set, and the drone searches the boundaries in the set in turn to achieve the purpose of traversing the space.

[0134] Among them, in the optimal observation point generation module, during the search process of the optimal observation point, the mean point of the boundary, the positions of the two spatial points farthest from each other at both ends of the boundary, and the normal line of the center of the boundary are searched, and the optimal observation point position is determined based on the center point position calculated from the positions of the two spatial points farthest from each other at both ends of the boundary and the perception field of view FOV of the depth camera.

[0135] Specifically, the search process for the position of the boundary m observation points includes:

[0136] 1) Obtaining the mean point of the boundary m;

[0137]

[0138] 2) Starting from the boundary mean point, a traversal search is performed to obtain the positions of the two spatial points at both ends of the boundary that are the farthest apart;

[0139] cell max,m =length max dir PCA +cell mean,m

[0140] cell min,m =length min dir PCA +cell mean,m

[0141] Among them, length max is the maximum distance between the searched boundary point and the mean point, lenght min is the minimum distance between the searched boundary point and the mean point, dir PCA is the principal component direction of all spatial points within the boundary.

[0142] The target point cell that meets the conditions will be obtained by searching in the boundary max,m and cell min,m , determined as the spatial point with the farthest distance between the two ends of the boundary.

[0143] 3) Determine the normal line of the center of the boundary according to the positions of the two spatial points farthest from each other at both ends of the boundary;

[0144] The center normal direction is expressed as:

[0145]

[0146] z w is the z-axis in the global coordinate system.

[0147] 4) The best observation point position is determined based on the center point position calculated from the two spatial points at both ends of the boundary and the perception field of view FOV of the depth camera.

[0148] The center point position calculated from the spatial points at both ends of the boundary is:

[0149]

[0150] Among them, s m It is expressed as the distance between the best observation point VP and the center point Mid on the center normal:

[0151]

[0152] Therefore, the optimal observation point VP can be expressed as:

[0153] VP m =Mid m +s m dir normal,m ;

[0154] The best observation points on each boundary constitute the boundary best viewpoint set.

[0155] Specifically, Figure 5 As shown, the cluster scheduling and allocation subunit includes an optimization problem model, an observation point and drone quantity determination module, and a model optimal solution seeking module;

[0156] The optimization problem model is used to convert M observation points VP m ∈{VP 1 ,…,VP M}Assigned to N UAVs n ∈{UAV 1 ,…,UAV N}, one drone is assigned only one observation point, and after the assignment, all drones reach their respective observation points at the minimum cost; the following allocation optimization mathematical model is established:

[0157] UAV n Whether to participate in the search is indicated by y n ∈{0,1}. Whether to set the observation point VP m Assigned to UAV n Represented as x mn ∈{0,1}. Among them, UAV n To observation point VP m The cost is denoted as d mn , the total cost of all drones reaching their respective observation points is expressed as The optimal solution of the model is used to find the allocation optimization of modules to minimize the total cost.

[0158] The observation point and drone number determination module is used to determine the number of observation points M and the number of drones N;

[0159] The model optimal solution seeking module is used to solve the optimization problem model according to the judgment results of the observation point and drone number judgment module, so as to assign an observation point to each drone as the next target point;

[0160] Specifically, when M<N, the optimal solution is sought:

[0161]

[0162]

[0163] x mn ≤y n ,

[0164]

[0165] x mn ∈{0,1},

[0166] y n ∈{0,1},

[0167] When M>N, the optimal solution is sought:

[0168]

[0169]

[0170] x mn ≤y n ,

[0171]

[0172] x mn ∈{0,1},

[0173] y n ∈{0,1},

[0174] Preferably, UAV n To observation point VP m The cost is the total observation cost d consisting of distance cost, speed cost and speed change cost mn .

[0175] Specifically, the total observation cost includes:

[0176] 1) Distance cost; consider UAV n Current location to observation point VP m The reachable distance of the location is obtained by using the global A-star to plan the path length from the current location of the drone to the point:

[0177] cost reach_dist =length(A * path);

[0178] 2) Speed ​​cost: Consider UAVn Path end speed and observation point VP m The degree of coincidence of the center normal of the position. This value is the angle between the vector direction of the path point at the end of the path planned by the global A star and the normal vector of the center of the boundary:

[0179] cost vel_normal =cos -1 (dir normal dir path_end );

[0180] 3) Speed ​​change cost; consider UAV n The current speed reaches the observation point VP m The change in position is the angle between the current velocity vector direction of the drone and the normal vector at the center of the boundary:

[0181] cost vel_change =cos -1 (dir vel_current dir path_end );

[0182] The total observation cost of the observation point obtained by combining the above three observation costs is:

[0183] d mn =cost sum =cost reach_dist +cost vel_normal +cost vel_change .

[0184] Through the above allocation optimization mathematical model, a suitable observation point can be found as the next target point for each UAV participating in the search.

[0185] Specifically, Figure 6 As shown, the simulated UAV is a simulated quad-rotor UAV, including a trajectory planning module, a first type of simulated sensor, a second type of simulated sensor, a nonlinear simulation controller and four simulated motors;

[0186] The trajectory planning module is used to perform trajectory planning according to the assigned target points, obtain a continuous expected trajectory that takes into account obstacle avoidance and collision avoidance between drones, and output it to the nonlinear simulation controller;

[0187] The first type of simulated sensor includes a simulated depth camera; it is used to obtain front-end sensor data including a dynamic scene depth map from the scene information of the virtual environment where the simulated drone is located, and output it to the collaborative search scheduling unit;

[0188] The second type of simulation sensors include posture measurement sensors including simulated IMU, GPS module, barometer and magnetometer, which are used to perceive the posture information of the simulated drone from the natural environment simulation data of the virtual environment in which the simulated drone is located;

[0189] The nonlinear simulation controller is used to perform nonlinear control according to the expected trajectory and the simulated UAV posture information sensed by the second type of simulation sensor, and output the motor speed corresponding to the simulated motor;

[0190] The simulation motor is used to simulate the thrust, air resistance, rotational torque and air resistance torque generated by the drone using a brushless DC motor during the rotation of the propeller according to the motor speed output by the nonlinear simulation controller; so as to obtain the resultant force and resultant torque exerted on the simulated four-rotor drone in the simulation environment under the action of four simulation motors.

[0191] Specifically, Figure 7 As shown, the trajectory planning module includes a primary trajectory planning module, a secondary trajectory planning module and a trajectory output module;

[0192] The primary trajectory planning module is used to perform path planning according to the obtained next target point position to generate a continuous expected trajectory for each simulated UAV;

[0193] The secondary trajectory planning module is used to determine the collision between drones based on the continuous expected trajectories of each simulated robot in the same time period; for the two simulated drones that are judged to have collided, secondary path planning is performed to generate a continuous expected trajectory to avoid the collision of the drones;

[0194] The trajectory output module is used to output the continuous expected trajectory of each simulated UAV to the flight controller of each UAV for flight simulation.

[0195] Among them, in a trajectory planning module, the process of trajectory planning includes:

[0196] 1) Perform one-time planning based on global A-star path planning to generate discrete paths;

[0197] The global A-star algorithm is used to find a discrete safe path from the current position to the target position in the current shared probability occupation map. The minimum node of the path is the minimum volume unit of the map. In the A-star algorithm, the total cost of each node in the map is expressed as the sum of the cost from the starting point and the cost from the end point:

[0198] f(n)=g(n)+h(n);

[0199] Starting from the starting point, the A-star algorithm searches for the adjacent nodes with the minimum total cost in turn until the end point is found. The total cost of the node is stored in a priority queue structure to ensure that the algorithm can quickly extract the node with the minimum total cost. In the end, a safe path with the minimum unit of map resolution is obtained, which is represented by path points of consecutive adjacent map unit voxels.

[0200] 2) Down-sampling is performed at the set path distance to generate sampled path points;

[0201] The discrete path calculated by the global A star is downsampled at an appropriate path distance, and continuous adjacent path voxel points are sampled at appropriate distance intervals to form more dispersed path point data.

[0202] 3) Using the downsampled path points as Bezier curve control points, a parameterized trajectory curve equation with respect to time is established;

[0203] Create a parametric trajectory curve equation with respect to time:

[0204]

[0205]

[0206] Among them, w i represents the weight of the i-th item, Indicates the number of combinations.

[0207] 4) Generate the continuous expected trajectory at each moment according to the parameterized trajectory curve equation.

[0208] By using parameterized Bezier curves to represent safe discrete paths as continuous expected trajectories over time, the expected state of the UAV at each moment can be obtained.

[0209] Specifically, in the secondary trajectory planning module, the trajectory planning process includes:

[0210] 1) The trajectory curves in the same time period are transmitted between multiple UAVs, and the time period is downsampled to obtain multiple sampling moments;

[0211] The sampling time set composed of multiple sampling time is T = {0, Δt, 2Δt, 3Δt, ..., nΔt}; n is the number of sampling points, Δt is the sampling interval;

[0212] 2) For the continuous expected trajectory of all UAVs, the sampling position points of each UAV are calculated at each sampling time;

[0213] After sampling, the set of sampling position points on the n-segment Bezier curve of the k-th drone is:

[0214]

[0215] 3) Determine whether the distance between the position points of any two UAVs at the same sampling time is within the set safety distance; if yes, proceed to the secondary trajectory planning of step 4), otherwise end step S3;

[0216] 4) In the secondary trajectory planning, two Bezier curve control points with optimized positions are determined in the respective expected trajectories of the two UAVs; the two Bezier curve control points are the two Bezier curve control points closest to the position point within the safe distance; the two Bezier curve control points are updated, and the connection line between the two Bezier curve control points is extended in the direction away from the other party's expected trajectory, so that the distance between the position points on the connection line at the same time exceeds the safe distance;

[0217] Specifically, on the m-segment Bezier curve, the distance between the two sampling position points of drones i and j at the same time is expressed as:

[0218]

[0219] exist When f is the safe distance; in the secondary trajectory planning, two control points {Q m ,Q m+1};

[0220] For control points {Q m ,Q m+1} Move the distance distance along the sampling position difference vector dir; the updated control point obtains the updated control point {Q m,new ,Q m+1,new};in,

[0221]

[0222]

[0223]

[0224]

[0225] In the formula, λ m , m+1 ≥1 is the pull-off coefficient.

[0226] 5) Reconstruct the Bezier trajectory curve based on the updated control points to obtain the trajectory of multiple UAVs that meets the safety obstacle avoidance requirements.

[0227] According to the updated control point {Q 0 ,Q1 ,…,Q m,new ,Q m+1,new ,…,Q n}Reconstruct the Bezier trajectory curve to obtain the continuous expected trajectory of multiple UAVs that meets the safety obstacle avoidance requirements.

[0228] The continuous expected trajectory of each UAV is output to the flight controller of each UAV for flight control.

[0229] Specifically, the nonlinear simulation controller includes a trajectory solver, a position loop controller, a nonlinear angle converter, a nonlinear attitude mapper and a hybrid controller;

[0230] The trajectory solver is used to convert the inputted UAV expected trajectory simulation data into the expected state quantity of the system at the current moment;

[0231] The position loop controller is used to perform position loop PID control according to the expected state quantity of the system and output the total position-speed control error;

[0232] The nonlinear angle converter is used to perform nonlinear SE (3) space angle conversion on the total error of the position and speed control to obtain the desired spatial rotation matrix;

[0233] The nonlinear attitude mapper is used to perform nonlinear attitude SO(3) space mapping on the current desired spatial rotation matrix and the measured rotation matrix, and output an attitude control error;

[0234] The hybrid controller is used to control the motor speed and output the motor speed to the simulation motor according to the posture control error and the total position-speed control error.

[0235] Specifically, the input signal of the trajectory solver, the desired trajectory of the drone, is a polynomial equation about time t:

[0236]

[0237] in, represents the position that the drone should reach at time t, C 3×5 Represents the fifth-order coefficient matrix of a three-dimensional polynomial, [t 4 ,t 3 ,t 2 ,t,1] T represents a higher-order independent variable with respect to time t.

[0238] Specifically, the trajectory solver converts the expected trajectory into the current time t k The system state quantity:

[0239]

[0240] Among them, P des Indicates time t k Expected location V des Indicates time t k Expected speed A des Indicates time t k The expected acceleration At time t k At , take the 0th, 1st, and 2nd derivatives of the desired trajectory respectively:

[0241] P des =F (0) (t k )

[0242] V des =F (1) (t k )

[0243] A des =F (2) (t k )

[0244] At the same time, considering that there may be obstacles on the route, the trajectory solver will expect the yaw angle attitude ψ des Adjust to face forward along the track:

[0245] [Δx,Δy] T =F (0) (t k )-F (0) (t k-1 )

[0246]

[0247] Therefore, the trajectory solver solves the desired trajectory into the desired state quantity of the input flight control

[0248] Specifically, in the position loop controller, the desired state quantity output by the trajectory solver is The input position loop PID is converted into the total error A of position and speed control input .

[0249] e P =P des -P now

[0250] e V =V des -V now

[0251] A input =K P eP +K V e V +K Vi ∫e V +A des +g

[0252] Among them, e P 、e V represents the error between the expected position and the expected velocity, g represents the acceleration due to gravity, A input Indicates the total error of position and speed control; K P Represents the proportional gain of the position error, K V Represents the proportional gain of the speed error, K Vi Indicates the integral gain of the speed error, P now Indicates the current time t of the simulated drone k The position of V now Indicates the current time t of the simulated drone k speed.

[0253] Specifically, in the nonlinear angle converter, the total error A of the input position speed control is input Perform nonlinear SE (3) space angle transformation to obtain the desired space rotation matrix R des The spatial expected rotation matrix R des By [x B,des ,y B,des ,z B,des ] indicates that, among them,

[0254] z B,des The vector direction representing the acceleration PID value:

[0255]

[0256] y B,des Indicates z B,des With the vector [cosψ des ,sinψ des ,0] T The normal vectors that make up the plane are:

[0257]

[0258] x B,des Represents y B,des With z B,des The normal vectors that make up the plane are:

[0259] x B,des =y B,des × B,des

[0260] Thus, the desired rotation matrix R is obtained des .

[0261] Specifically, the spatial desired rotation matrix R is converted into des and the measured rotation matrix R B Perform nonlinear attitude SO(3) space mapping and output attitude control error [e p ,e q ,e r ] T ;

[0262] The measured rotation matrix R B =[x B y B z B ] The three orthogonal axes x in the measured body coordinate system B ,y B ,z B To characterize.

[0263] The desired rotation matrix R des and the current rotation matrix R B By mapping vee to SO(3) space, the attitude angle error e R It is expressed as:

[0264]

[0265] The superscript “∨” represents the vee mapping of SO(3); R B =[x B y B z B ] By the orthogonal three-axis directions x on the body coordinate system B ,y B ,z B To characterize;

[0266] In this embodiment, the attitude angle error e R PID control is performed to output the attitude control error [e p ,e q ,e r ] T :

[0267] [e p ,e q ,e r ] T =K R e R +K Ri ∫e R

[0268] K R Represents the proportional gain of the attitude angle error, K Ri Indicates the integral gain of the attitude angle error.

[0269] Preferably, the simulated drone at the current time t k Position P now , speed V now and the measured rotation matrix R B =[x B y B z B ] is the pose measurement sensor including the simulated IMU, GPS module, barometer and magnetometer included in the simulated drone, which is obtained by sensing the simulated data of the natural environment including the simulated force field, atmospheric field and magnetic field in the virtual environment.

[0270] In addition, the simulated UAV current time t k Position P now , speed V now and the measured rotation matrix R B =[x B y B z B ] It can also be the current virtual physics engine that obtains the pose data of the simulated drone.

[0271] Specifically, in the hybrid controller, according to the attitude control error [e p ,e q ,e r ] T The total error A of the position speed control output by the position loop controller input , perform motor speed control and output the motor speed ω to the simulation motor.

[0272] Among them, the thrust generated by the i-th simulation motor satisfies The torque generated satisfies k F is the thrust coefficient; k M is the moment coefficient;

[0273] For the drones in the simulation system, there are:

[0274]

[0275] The obtained speeds of the four motors of the simulated quad-rotor drone are:

[0276]

[0277] Where L is the distance from each motor of the quadcopter to the center of mass of the drone; m is the mass of the drone.

[0278] The four motor speeds are input into the simulation motor so that the simulation motor can simulate the thrust F generated by the propeller rotation of the drone using the brushless DC motor.i , air resistance Rotational torque M i , air resistance moment

[0279]

[0280]

[0281]

[0282]

[0283] Among them, k D is the air resistance coefficient, μ D is the air resistance moment coefficient; represents the airflow speed on the propeller surface, dir turn Indicates the forward and reverse direction of the simulated motor, z B is the normal vector perpendicular to the propeller plane;

[0284] Under the action of four simulated motors, the resultant force and torque of the quad-rotor drone are expressed as:

[0285]

[0286]

[0287] Among them, m is the mass of the drone and g is the acceleration due to gravity.

[0288] Specifically, the virtual simulation environment unit communicates data with each simulated drone; outputs the simulated virtual scene information to the first type of simulation sensor of the simulated drone, so that the first type of simulation sensor can perceive the scene information and obtain front-end sensor data including dynamic scenes; according to the resultant force and torque exerted on the four-rotor drone in the simulation environment by the simulated motor output, the simulated natural environment simulation data including simulated force field, atmospheric field and magnetic field are output to the second type of simulation sensor of the simulated drone, so that the second type of simulation sensor can perceive the posture information of the drone according to the natural environment simulation data.

[0289] Preferably, the Gazebo simulation platform and the virtual physics engine are used to build a simulation scene environment in the virtual simulation environment unit.

[0290] Optionally, during the construction of the simulation scene environment, the main steps include:

[0291] 1) Simulation obstacle modeling;

[0292] By modeling in the Gazebo simulation platform, obstacles of different shapes and sizes are constructed to simulate typical structures in various scenes such as forests, buildings, and indoors. In order to verify the adaptability of multi-UAV obstacle avoidance capabilities in different environments, obstacle scenes of different densities are constructed to provide a variety of test environments for obstacle avoidance.

[0293] 2) Virtual physics engine configuration;

[0294] The virtual physics engine simulates the forces on the drone in the natural environment, applies external forces such as gravity, aerodynamics, and resistance to it, and updates its current kinematic state in each iteration through dynamic model calculation. At the same time, the physics engine applies simulated force fields, atmospheric fields, magnetic fields, etc. to the internal sensor modules of the flight control, such as IMU, barometer, and magnetometer, to provide the flight control with natural environment simulation data.

[0295] 3) Writing analog sensor interfaces;

[0296] Build a drone model in the simulated drone under the simulation environment and virtual physics engine.

[0297] Using the motor dynamics model in the simulated drone: the thrust generated by a single motor meets The torque generated satisfies Simulate the aerodynamic force and torque generated by the quadrotor UAV motor and write the UAV motor speed input interface.

[0298] 4) Writing of data communication interface.

[0299] Write a data communication interface to connect the data of sensors and motors in the drone model with the trajectory tracking flight control; based on the robot operating system ROS, set the UDP port of the flight control and simulation system, connect the data of the simulation sensors IMU, GPS, magnetometer and barometer to the flight control, and transmit the flight control output motor speed to the simulation motor through the topic mechanism to generate simulated aerodynamics.

[0300] To sum up, the simulation system of this embodiment overcomes the problem of the lack of a fast, complete, concise and clear collaborative spatial search scheduling and simulation verification environment for multiple UAVs in complex spaces in the field of intelligent control of UAVs; it provides researchers in this field with a complete simulation platform that can realize environmental perception, algorithm verification, and maneuverable flight control, and solves the problem that the influence of other module factors cannot be considered in the collaborative spatial search scheduling of UAVs.

[0301] The collaborative spatial search scheduling of this embodiment solves the problem of independent mutual perception of distributed cluster systems, improves the system's ability to perceive environmental consistency, and effectively avoids interference from invalid perception between machines. In addition, a method of operations optimization is used to allocate tasks for distributed clusters, solving the problem of lack of planning in distributed cluster operations, and minimizing the time and energy consumption of the entire system tasks on a global scale.

[0302] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A distributed multi-UAV cluster collaborative space search and scheduling control simulation system, characterized in that: include: Collaborative search dispatch unit and multiple simulated UAVs connected to it; The collaborative search scheduling unit is used to perform collaborative space search scheduling according to the received dynamic scene depth map output frame by frame by each simulated UAV during simulated flight; In the collaborative space search scheduling, the shared probability occupancy map is updated based on the dynamic scene depth map of each simulated UAV, and the boundary set used to distinguish the unknown and known areas of the map and the optimal observation point position corresponding to each boundary in the boundary set are searched; the observation point scheduling is performed to assign an optimal observation point position to each simulated UAV as the next target point of the simulated UAV; The simulated UAV is used to receive the corresponding target point position to perform trajectory planning to generate a continuous expected trajectory; and perform simulated flight in a virtual simulation environment according to the continuous expected trajectory to verify the stability and safety of the distributed cluster operation; The collaborative search scheduling unit includes a shared map processing subunit, a boundary search division subunit and a cluster scheduling allocation subunit; A shared map processing subunit, used to share maps based on scene depth maps of respective locations output by multiple drones, and generate a shared probability occupancy map; The boundary search division subunit is used to occupy the map information in the map according to the shared probability, perform boundary search to obtain a boundary set and a boundary optimal viewpoint set corresponding to the boundary combination; The cluster scheduling allocation subunit is used to allocate each drone to the next target point based on the number of observation points and the number of drones in the boundary optimal observation point set, as well as the cost from the current position of the drone to the observation point; The cluster scheduling and allocation subunit includes an optimization problem model, an observation point and drone quantity determination module, and a model optimal solution seeking module; The optimization problem model is used to Observation points Assigned to drones , one drone is assigned only one observation point, and after the assignment, all drones reach their respective observation points at the minimum cost; Observation point and drone number determination module, used to determine the number of observation points M and the number of drones N size; The model optimal solution seeking module is used to solve the optimization problem model according to the judgment results of the observation point and drone number judgment module, and assign an observation point to each drone as the next target point; Specifically, when When , the optimal solution is sought: ； ； ； ； ； ； when When , the optimal solution is sought: ； ； ； ； ； ； Indicates whether the drone is involved in the search; Indicates that the observation point Assign to drone Expressed as Among them, drones To the observation point The cost is expressed as , the total cost of all drones reaching their respective observation points is expressed as ; Through allocation optimization, the total cost Reach the minimum.

2. The control simulation system for distributed multi-UAV cluster collaborative space search scheduling according to claim 1 is characterized in that: The boundary search division subunit includes a boundary generation module and an optimal observation point generation module; The boundary generation module is used to search the current state of each data unit in the shared probability occupation map to find a boundary set for distinguishing unknown and known areas of the map; The data unit at the intersection of the unknown state and the free state in the shared probability occupation map belongs to the point on the boundary, its own state is the free state, and the state of its neighboring unit is the unknown state; The optimal observation point generation module is used to search for the observation point position that maximizes the observation efficiency of each boundary in the boundary set; During the search process, the optimal observation point position is determined by searching the mean point of the boundary, the positions of the two spatial points farthest apart at both ends of the boundary, the normal line of the center of the boundary, and the center point position calculated based on the positions of the two spatial points farthest apart at both ends of the boundary and the perception field of view FOV of the depth camera.

3. The control simulation system for distributed multi-UAV cluster collaborative space search scheduling according to claim 1 is characterized in that: Drones To the observation point The cost is the total observation cost consisting of distance cost, speed cost and speed change cost .

4. The control simulation system for distributed multi-UAV cluster collaborative space search scheduling according to claim 1 is characterized in that: The simulated UAV is a simulated quad-rotor UAV, comprising a trajectory planning module, a first type of simulated sensor, a second type of simulated sensor, a nonlinear simulation controller and four simulated motors; The trajectory planning module is used to perform trajectory planning according to the assigned target points, obtain a continuous expected trajectory that takes into account obstacle avoidance and collision avoidance between drones, and output it to the nonlinear simulation controller; The first type of simulated sensors includes simulated depth cameras; Used to obtain front-end sensor data including dynamic scene depth map from the scene information of the virtual environment where the simulated UAV is located, and output it to the collaborative search scheduling unit; The second type of simulation sensors include posture measurement sensors including simulated IMU, GPS module, barometer and magnetometer, which are used to perceive the posture information of the simulated drone from the natural environment simulation data of the virtual environment in which the simulated drone is located; The nonlinear simulation controller; Used to perform nonlinear control according to the expected trajectory and the simulated UAV posture information sensed by the second type of simulation sensor, and output the motor speed corresponding to the simulated motor; The simulation motor is used to simulate the thrust, air resistance, rotational torque and air resistance torque generated by the brushless DC motor of the drone during the propeller rotation process according to the motor speed output by the nonlinear simulation controller; In order to obtain the resultant force and torque exerted on the simulated quad-rotor drone in the simulation environment under the action of four simulated motors.

5. The control simulation system for distributed multi-UAV cluster collaborative space search scheduling according to claim 4 is characterized in that: The trajectory planning module includes a primary trajectory planning module, a secondary trajectory planning module and a trajectory output module; The primary trajectory planning module is used to perform path planning according to the obtained next target point position to generate a continuous expected trajectory for each simulated UAV; The secondary trajectory planning module is used to determine the collision between drones based on the continuous expected trajectories of each simulated robot in the same time period; for the two simulated drones that are judged to have collided, secondary path planning is performed to generate a continuous expected trajectory to avoid the collision of the drones; The trajectory output module is used to output the continuous expected trajectory of each simulated UAV to the flight controller of each UAV for flight simulation.

6. The control simulation system for distributed multi-UAV cluster collaborative space search scheduling according to claim 5 is characterized in that: The secondary trajectory planning module includes a time sampling module, a sampling position point calculation module, a collision judgment module, a planning module and a trajectory output module; The time sampling module is used for multiple drones to transmit trajectory curves in the same time period to each other, sample the time period, and obtain multiple sampling moments; The sampling position point calculation module is used to calculate the sampling position point of each UAV at each sampling time for the continuous expected trajectory of all UAVs; The collision judgment module is used to judge whether the distance between the position points of any two drones at the same sampling time is within the set safety distance; If yes, the desired trajectory of the UAV is input into the planning module; The quadratic planning module is used to determine two Bezier curve control points with optimized positions in the respective expected trajectories of the two UAVs; the two Bezier curve control points are the two Bezier curve control points closest to the position point within the safe distance; the two Bezier curve control points are updated, and the connection line between the two Bezier curve control points is extended in a direction away from the expected trajectory of the other party, so that the distance between the position points on the connection line at the same time exceeds the safe distance; The trajectory output module is used to reconstruct the Bezier trajectory curve according to the updated control points to obtain the continuous expected trajectory of multiple UAVs that meets the safety obstacle avoidance requirements.

7. The control simulation system for distributed multi-UAV cluster collaborative spatial search scheduling according to any one of claims 1 to 6, characterized in that: It also includes a virtual simulation environment unit; the virtual simulation environment unit communicates data with the simulated unmanned aerial vehicle; the simulated virtual scene information is output to the first type of simulation sensor of the simulated unmanned aerial vehicle, so that the first type of simulation sensor can perceive the scene information and obtain front-end sensor data including dynamic scenes; according to the resultant force and torque exerted on the four-rotor unmanned aerial vehicle in the simulation environment by the simulated motor output, the simulated natural environment simulation data including simulated force field, atmospheric field and magnetic field are output to the second type of simulation sensor of the simulated unmanned aerial vehicle, so that the second type of simulation sensor can perceive the posture information of the unmanned aerial vehicle according to the natural environment simulation data.

Images