CN115657726A - Control switching method for multiple unmanned aerial vehicles - Google Patents
Control switching method for multiple unmanned aerial vehicles Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a control switching method of multiple unmanned aerial vehicles, which comprises the following steps: acquiring position information, motion information and authority information of the unmanned aerial vehicle cluster to determine a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster, and establishing an initial motion model of the unmanned aerial vehicle cluster; acquiring communication information of the unmanned aerial vehicle group, and constructing a topological communication network of the unmanned aerial vehicle group; performing area search on a preset area to be monitored, and when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object, performing path planning by using a target pilot according to the target detection object to obtain a pilot path; and reorganizing and forming the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and forming result to obtain a standard motion model, and controlling the unmanned aerial vehicle group to operate according to the standard motion model. The invention also provides a control switching device, electronic equipment and a storage medium for the multiple unmanned aerial vehicles. The invention can improve the efficiency of the multiple unmanned aerial vehicles to execute tasks.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a control switching method and device for multiple unmanned aerial vehicles, electronic equipment and a computer readable storage medium.
Background
With the increasing maturity of unmanned aerial vehicle technology, more and more fields begin to use unmanned aerial vehicles to perform production operations, for example, using unmanned aerial vehicles to perform military reconnaissance, power inspection, cargo transportation, logistics distribution and other production activities, and in order to implement complex operations, an unmanned aerial vehicle cluster formed by a plurality of unmanned aerial vehicles is often required to perform resultant force cooperation, but when the unmanned aerial vehicle cluster executes a task, the control mode of the unmanned aerial vehicle cluster needs to be switched immediately to meet the working requirements of different task stages.
For example, in the process of flying an unmanned aerial vehicle group from a first area to a second area to execute a task, the control authority of the unmanned aerial vehicle group needs to be handed over from the ground base station in the first area to the ground base station in the second area.
Disclosure of Invention
The invention provides a control switching method and device for multiple unmanned aerial vehicles and a computer readable storage medium, and mainly aims to solve the problem of low efficiency when the multiple unmanned aerial vehicles execute tasks.
In order to achieve the above object, the present invention provides a method for controlling and switching multiple drones, comprising:
acquiring position information, motion information and authority information of an unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
acquiring communication information of the unmanned aerial vehicle group, and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by using a preset topological communication model;
carrying out area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object;
when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and when the unmanned aerial vehicle is the piloting unmanned aerial vehicle, controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model.
Optionally, the establishing an initial motion model of the unmanned aerial vehicle fleet using the position information and the motion information includes:
extracting the positioning coordinate, course angle and pitch angle of the unmanned aerial vehicle cluster from the position information;
extracting the flight speed, pitch angle speed, yaw angle speed and execution delay of the unmanned aerial vehicle cluster from the motion information;
establishing the following initial motion model by using the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw rate, the pitch rate and the execution delay:
wherein, [ x ] i -x 0 i ,y i -y 0 i ,z i -z 0 i ,δ,γ,v,w,u] T Is the state vector of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, i refers to the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, x i Is the real-time cross-axis coordinate, x, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the cross axis coordinate, y, of the ith unmanned aerial vehicle in the positioning coordinates i Real-time vertical axis coordinate, y, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the vertical axis coordinate, z, of the ith unmanned aerial vehicle in the positioning coordinates i Is the real-time longitudinal axis coordinate, z, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i The coordinate of the longitudinal axis of the ith unmanned aerial vehicle in the positioning coordinate is shown, delta is the course angle, gamma is the pitch angle, v is the flight speed, w is the yaw angular velocity, u is the pitch angular velocity, T is a transposition symbol, tau v Performing a delay for a flight speed of said performance delays, r v For control commands corresponding to said flight speed, τ w Performing a delay for yaw rate of said performance delay, r w For control commands corresponding to said yaw rate, τ u Performing a delay for a pitch angle rate of said performance delays, r u And the pitch angle speed corresponding to the flying speed.
Optionally, the constructing a topology communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model by using a preset topology communication model includes:
selecting unmanned aerial vehicles in the unmanned aerial vehicle cluster one by one as target unmanned aerial vehicles, and extracting target positions of the target unmanned aerial vehicles from the initial motion model;
determining a communication unmanned aerial vehicle of the target unmanned aerial vehicle according to the communication information, and extracting communication delay between the target unmanned aerial vehicle and the communication unmanned aerial vehicle from the communication information;
and taking the target position as a graph node, connecting the graph node corresponding to the target unmanned aerial vehicle with the graph node corresponding to the communication unmanned aerial vehicle to obtain a node edge, and taking the communication delay as an edge weight corresponding to the node edge to obtain the topological communication network.
Optionally, the performing, by the unmanned aerial vehicle group, area search on a preset area to be monitored includes:
selecting one unmanned aerial vehicle in the unmanned aerial vehicle cluster as a target unmanned aerial vehicle, and extracting a three-dimensional position coordinate of the target unmanned aerial vehicle from the initial motion model as a target position coordinate;
calculating the monitoring width and the monitoring length of the target unmanned aerial vehicle according to the target position coordinates, and calculating the investigation width of the unmanned aerial vehicle cluster according to the monitoring width and the monitoring length corresponding to all the target unmanned aerial vehicles;
and planning a monitoring path of the region to be monitored according to the investigation width to obtain a monitoring path, and searching the region to be detected according to the monitoring path.
Optionally, the calculating the monitoring width and the monitoring length of the target drone according to the target position coordinates includes:
extracting a longitudinal axis coordinate of the target unmanned aerial vehicle from the target position coordinate;
acquiring monitor information of the target unmanned aerial vehicle, and extracting a longitudinal monitoring angle and a transverse detection angle from the monitor information;
calculating the monitoring width and the monitoring length according to the longitudinal axis coordinate, the longitudinal monitoring angle and the transverse detection angle by using a monitoring width algorithm as follows:
wherein R is 1 Is the monitored width, R 2 Means the monitoring length, ceiling means an upward rounding symbol, theta means the longitudinal monitoring angle, alpha means the roll angle of the target unmanned aerial vehicle, beta means the transverse detection angle, and d means the target unmanned aerial vehicleAnd the longitudinal axis coordinate of the target unmanned aerial vehicle is described.
Optionally, the performing path planning according to the target probe by using the control authority of the target pilot machine to obtain a pilot path includes:
acquiring three-dimensional point cloud information of the surrounding environment of the target detection object according to the control authority of the target navigator and the real-time transverse axis coordinate, the real-time vertical axis coordinate and the real-time longitudinal axis coordinate of the target navigator;
adding coordinate information of the target detection object in the three-dimensional point cloud information to obtain primary point cloud information, and adding coordinate information of the target navigator in the primary point cloud information to obtain secondary point cloud information;
acquiring obstacle information in the surrounding environment of the target detection object by using a monitor of the target navigator, and updating the secondary point cloud information according to the obstacle information to obtain standard point cloud information;
and planning a path according to the standard point cloud information by using a preset optimal path algorithm to obtain the pilot path.
Optionally, the regrouping the unmanned aerial vehicle group according to the pilot path by using the topological communication network includes:
generating a communication topology matrix of the target navigator according to the topology communication network;
carrying out topological sorting on the unmanned aerial vehicle group according to the communication topological matrix, and taking a topological serial number after the topological sorting as a recombination serial number of the unmanned aerial vehicle group;
and serially grouping the unmanned aerial vehicle group on the pilot path according to the reorganization number to complete reorganization and grouping.
In order to solve the above problem, the present invention further provides a control switching device for multiple drones, including:
the motion model module is used for acquiring position information, motion information and authority information of the unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
the communication network module is used for acquiring communication information of the unmanned aerial vehicle group and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by utilizing a preset topological communication model;
the control switching module is used for carrying out area search on a preset area to be monitored by utilizing the unmanned aerial vehicle group, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle group monitors a preset target detection object; when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
the detection operation module is used for utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and the default operation module is used for controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model when the unmanned aerial vehicle is the piloting unmanned aerial vehicle.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of controlling a handover of multiple drones as described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, where at least one computer program is stored, where the at least one computer program is executed by a processor in an electronic device to implement the method for controlling switching of multiple drones described above.
According to the embodiment of the invention, the initial motion model is established by utilizing the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw angular velocity, the pitch angular velocity and the execution delay, so that the motion relation of the unmanned aerial vehicle cluster can be accurately characterized, the subsequent unmanned aerial vehicle searching and path planning are assisted, and the communication state of each unmanned aerial vehicle in the unmanned aerial vehicle cluster can be mathematically expressed by utilizing a preset topological communication model to establish a topological communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model, so that the subsequent control switching is facilitated; the method comprises the steps of carrying out area search on a preset area to be monitored by utilizing the unmanned aerial vehicle cluster, accurately determining a monitoring total matrix of the unmanned aerial vehicle cluster, planning a path for target search according to the monitoring total matrix, ensuring the improvement of the searching efficiency of the unmanned aerial vehicle, carrying out path planning according to a target detection object by utilizing the target pilot to obtain a pilot path, and simulating the shortest moving path of the unmanned aerial vehicle cluster so as to improve the operation efficiency of the unmanned aerial vehicle. Therefore, the control switching method and device for the multiple unmanned aerial vehicles, the electronic device and the computer readable storage medium provided by the invention can solve the problem of low efficiency when the multiple unmanned aerial vehicles execute tasks.
Drawings
Fig. 1 is a schematic flowchart of a control switching method for multiple drones according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of performing area search according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of path planning according to an embodiment of the present invention;
fig. 4 is a functional block diagram of a control switching device of multiple drones according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the method for controlling switching among multiple drones according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The embodiment of the application provides a control switching method for multiple unmanned aerial vehicles. The execution subject of the control switching method for multiple drones includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server, a terminal, and the like. In other words, the control switching method of the multiple drones may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a control switching method for multiple drones according to an embodiment of the present invention. In this embodiment, the method for controlling and switching multiple drones includes:
s1, acquiring position information, motion information and authority information of an unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
in the embodiment of the invention, the position information of the unmanned aerial vehicle cluster can be acquired by using positioning components such as a GPS (global positioning system) positioner or a Beidou positioner, and the position information comprises the longitude, the latitude, the horizontal height and the like of the unmanned aerial vehicle cluster.
In detail, motion information of the unmanned aerial vehicle cluster may be acquired by using sensors such as an acceleration sensor and a gyroscope, where the motion information includes a flight speed, a flight acceleration, a deflection angle, and the like of the unmanned aerial vehicle cluster.
Specifically, the authority information includes a pilot authority and a following-navigation authority, and only one unmanned aerial vehicle in the unmanned aerial vehicle cluster has the unmanned aerial vehicle with the pilot authority and is the unmanned aerial vehicle with the following-navigation authority.
In detail, the determining of the piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information means that the unmanned aerial vehicle with the piloting authority in the unmanned aerial vehicle cluster is used as the piloting unmanned aerial vehicle.
In an embodiment of the present invention, the establishing an initial motion model of the drone swarm using the position information and the motion information includes:
extracting the positioning coordinate, the course angle and the pitch angle of the unmanned aerial vehicle cluster from the position information;
extracting the flight speed, pitch angle speed, yaw angle speed and execution delay of the unmanned aerial vehicle cluster from the motion information;
establishing the following initial motion model by using the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw rate, the pitch rate and the execution delay:
wherein, [ x ] i -x 0 i ,y i -y 0 i ,z i -z 0 i ,δ,γ,v,w,u] T A state vector of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, wherein i refers to the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, and x i Is the real-time cross-axis coordinate, x, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the cross axis coordinate, y, of the ith unmanned aerial vehicle in the positioning coordinates i Real-time vertical axis coordinate, y, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the vertical axis coordinate, z, of the ith unmanned aerial vehicle in the positioning coordinates i Is the real-time longitudinal axis coordinate, z, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the longitudinal axis coordinate of the ith unmanned aerial vehicle in the positioning coordinate, delta is the course angle, gamma is the pitch angle, v is the flight speed, w is the yaw angular velocity, u is the pitch angular velocity, T is a transposition symbol, tau is v Performing a delay for a flight speed of said performance delays, r v For the control command corresponding to said flight speed, τ w Performing a delay for yaw rate of said performance delay, r w For control commands corresponding to said yaw rate, τ u Performing a delay for a pitch angle rate of said performance delays, r u And the pitch angle speed corresponding to the flying speed.
In detail, the heading angle refers to an included angle between the longitudinal axis of the aircraft and the space shuttle and the north pole of the earth.
Specifically, the pitch angle refers to an included angle between an x-axis of the engine body coordinate system and a horizontal plane, and when the x-axis of the engine body coordinate system is above an XOY plane of the inertial coordinate system, the pitch angle is positive, otherwise, the pitch angle is negative.
In the embodiment of the invention, the initial motion model is established by utilizing the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw rate, the pitch angle rate and the execution delay, so that the motion relation of the unmanned aerial vehicle cluster can be accurately represented, and the follow-up unmanned aerial vehicle searching and path planning are assisted.
S2, acquiring communication information of the unmanned aerial vehicle cluster, and constructing a topological communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model by using a preset topological communication model;
in the embodiment of the present invention, the communication information refers to a communication structure between the unmanned aerial vehicles, and includes the serial number, communication delay, and relative structure of the unmanned aerial vehicles in the unmanned aerial vehicles.
In an embodiment of the present invention, the constructing a topology communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model by using a preset topology communication model includes:
selecting unmanned aerial vehicles in the unmanned aerial vehicle cluster one by one as target unmanned aerial vehicles, and extracting target positions of the target unmanned aerial vehicles from the initial motion model;
determining a communication unmanned aerial vehicle of the target unmanned aerial vehicle according to the communication information, and extracting communication delay between the target unmanned aerial vehicle and the communication unmanned aerial vehicle from the communication information;
and taking the target position as a graph node, connecting the graph node corresponding to the target unmanned aerial vehicle with the graph node corresponding to the communication unmanned aerial vehicle to obtain a node edge, and taking the communication delay as an edge weight corresponding to the node edge to obtain the topological communication network.
In detail, the target position refers to a relative position of the target drone in the drone swarm.
Specifically, the communication unmanned aerial vehicle is an unmanned aerial vehicle that communicates with the target unmanned aerial vehicle, for example, unmanned aerial vehicle a communicates with unmanned aerial vehicle B and unmanned aerial vehicle C, and then the communication unmanned aerial vehicle of unmanned aerial vehicle a is unmanned aerial vehicle B and unmanned aerial vehicle C.
In the embodiment of the invention, the communication state of each unmanned aerial vehicle in the unmanned aerial vehicle cluster can be mathematically expressed by utilizing a preset topological communication model to construct the topological communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model, so that the subsequent control switching is facilitated.
S3, performing area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object;
in the embodiment of the present invention, referring to fig. 2, the performing area search on a preset area to be monitored by using the group of unmanned aerial vehicles includes:
s21, selecting one unmanned aerial vehicle in the unmanned aerial vehicle cluster as a target unmanned aerial vehicle, and extracting a three-dimensional position coordinate of the target unmanned aerial vehicle from the initial motion model as a target position coordinate;
s22, calculating the monitoring width and the monitoring length of the target unmanned aerial vehicle according to the target position coordinates, and calculating the investigation width of the unmanned aerial vehicle cluster according to the monitoring width and the monitoring length corresponding to all the target unmanned aerial vehicles;
and S23, planning a monitoring path of the region to be monitored according to the investigation width to obtain a monitoring path, and searching the region to be monitored according to the monitoring path.
Specifically, the monitoring width refers to an actual width of a monitoring area obtained when the monitor of the target unmanned aerial vehicle monitors the ground.
In detail, the monitoring length refers to an actual length of a monitoring area part obtained when a monitor of the target unmanned aerial vehicle detects the ground.
In detail, the calculating the monitoring width and the monitoring length of the target unmanned aerial vehicle according to the target position coordinates includes:
extracting a longitudinal axis coordinate of the target unmanned aerial vehicle from the target position coordinate;
acquiring monitor information of the target unmanned aerial vehicle, and extracting a longitudinal monitoring angle and a transverse detection angle from the monitor information;
calculating the monitoring width and the monitoring length according to the longitudinal axis coordinate, the longitudinal monitoring angle and the transverse detection angle by using a monitoring width algorithm as follows:
wherein R is 1 Is the monitored width, R 2 The monitoring length is referred to, ceiling is an upward rounding symbol, theta is the longitudinal monitoring angle, alpha is the roll angle of the target unmanned aerial vehicle, beta is the transverse detection angle, and d is the longitudinal axis coordinate of the target unmanned aerial vehicle.
In the embodiment of the invention, the monitoring width of the target unmanned aerial vehicle is calculated by utilizing the monitoring width algorithm according to the longitudinal axis coordinate, the longitudinal monitoring angle and the transverse detection angle, the monitoring width of the unmanned aerial vehicle can be determined by combining the real-time height and the roll angle of the target unmanned aerial vehicle, and the irregular deformation of a monitoring area caused by the motion of the unmanned aerial vehicle is accurately represented, so that the planning of a subsequent monitoring path is facilitated, and the efficiency of area search of the unmanned aerial vehicle is improved.
In detail, the monitor may be a monitoring device such as a camera or a radar transmitter.
Specifically, the longitudinal monitoring angle refers to an included angle between an upper visual field and a lower visual field of the monitor.
In detail, the transverse detection angle refers to an included angle between left and right visual fields of the monitor.
In detail, the investigation width is a unit width when the unmanned aerial vehicle cluster performs a region search.
Specifically, the calculating the investigation width of the unmanned aerial vehicle cluster according to the monitoring width and the monitoring length includes:
constructing a monitoring matrix by using the monitoring width and the monitoring length, and constructing a monitoring total matrix of the unmanned aerial vehicle cluster according to the initial motion model and the monitoring matrix;
and extracting a total monitoring length and a total monitoring width from the total monitoring matrix, and selecting one item with the largest numerical value from the total monitoring length and the total monitoring width as the investigation width.
In detail, the constructing of the monitoring matrix by using the monitoring width and the monitoring length means generating a matrix region with the monitoring width as the width and the monitoring length as the length and using the matrix region as the monitoring matrix.
In detail, a scanning line investigation mode can be used for planning a monitoring path of the region to be monitored according to the investigation width to obtain the monitoring path.
Specifically, the performing the area search on the area to be detected according to the monitoring path includes:
acquiring monitoring pictures shot by the unmanned aerial vehicle group under the monitoring path, and gathering all the monitoring pictures into a monitoring picture set;
selecting monitoring pictures in the monitoring picture set one by one as target pictures, and extracting a picture characteristic set of the target pictures by using a preset target object identification model;
selecting the picture features in the picture feature set one by one as target picture features, and calculating the similarity between the target picture features and preset picture features of the target detection object by using the following target similarity algorithm:
wherein D is the similarity, ρ is a preset similarity coefficient, arccos is an inverse cosine function, n is a total number of columns of feature vectors in the target picture feature, the total number of columns of feature vectors in the target picture feature is equal to the total number of columns of feature vectors in the picture feature of the target probe, j is a jth column in the feature vectors, and C is a preset similarity coefficient j Means thatA column vector of the jth column in the target picture feature vector is a vector dot product algorithm, B j The method comprises the steps of referring to a j-th column vector of a characteristic vector in the picture characteristic of the target detection object;
and when the similarity is greater than a preset similarity threshold value, determining that the region corresponding to the target picture feature contains the target detection object.
In detail, the target recognition model may be a YoLo network or an Anchor Based network trained with an atlas containing the target probe.
In detail, the similarity between the target picture characteristic and the preset picture characteristic of the target detection object is calculated by using the target similarity algorithm, and each characteristic vector of the target picture can be compared in a layered mode, so that the accuracy of similarity calculation is improved.
In the embodiment of the invention, the area search is carried out on the preset area to be monitored by utilizing the unmanned aerial vehicle cluster, the monitoring total matrix of the unmanned aerial vehicle cluster can be accurately determined, the path of target search is planned according to the monitoring total matrix, and the improvement of the searching efficiency of the unmanned aerial vehicle is ensured.
S4, when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
in the embodiment of the invention, the target pilot is an unmanned aerial vehicle for piloting the unmanned aerial vehicle cluster.
In detail, the switching the control authority of the unmanned aerial vehicle group to the target pilot is to update authority information of the unmanned aerial vehicle group, so that the control authority of the target pilot is changed to the pilot authority.
In detail, the pilot path is used for planning an effective path between a target pilot unmanned aerial vehicle and the target detection object.
In the embodiment of the present invention, the performing path planning according to the target probe by using the control authority of the target pilot to obtain the pilot path includes:
acquiring three-dimensional point cloud information of the surrounding environment of the target detection object according to the control authority of the target navigator and the real-time transverse axis coordinate, the real-time vertical axis coordinate and the real-time longitudinal axis coordinate of the target navigator;
adding coordinate information of the target detection object in the three-dimensional point cloud information to obtain primary point cloud information, and adding coordinate information of the target navigator in the primary point cloud information to obtain secondary point cloud information;
acquiring obstacle information in the surrounding environment of the target detection object by using a monitor of the target navigator, and updating the secondary point cloud information according to the obstacle information to obtain standard point cloud information;
and planning a path according to the standard point cloud information by using a preset optimal path algorithm to obtain the pilot path.
Specifically, the method for acquiring the obstacle information in the surrounding environment of the target detection object by using the monitor of the target navigator is consistent with the step of performing the area search on the area to be detected according to the monitoring path in the step S3, and is not described herein again.
In detail, referring to fig. 3, the performing path planning according to the standard point cloud information by using a preset optimal path algorithm to obtain the pilot path includes:
s31, acquiring coordinate information of the target detection object and coordinate information of the target pilot aircraft from the standard point cloud information, taking the coordinate information of the target detection object as an end point coordinate, taking the coordinate information of the target pilot aircraft as a start point coordinate, and adding the start point coordinate to a preset first coordinate set;
s32, generating effective node coordinates randomly according to the starting point coordinates and the end point coordinates, and adding all the effective node coordinates into a first coordinate set;
s33, selecting the coordinates in the first coordinate set one by one as target coordinates, and judging whether an obstacle exists in a straight-line path between the target coordinates and the end point coordinates;
s34, when an obstacle exists in a straight-line path between the target coordinate and the end point coordinate, calculating an effective path distance of the target coordinate, calculating a straight-line end point distance of the target coordinate according to the end point coordinate, and adding the straight-line end point distance and the effective path distance to obtain a target path distance;
s35, judging whether the target path distance is smaller than a preset initial path distance or not;
s36, when the target path distance is larger than or equal to the initial path distance, deleting the target coordinates from the first coordinate set, and returning to the step of selecting the coordinates in the first coordinate set one by one as the target coordinates;
s37, when the target path distance is smaller than the initial path distance, taking the target path distance as the initial path distance, and moving the target coordinate from the first coordinate set to a preset second coordinate set;
s38, taking an obstacle closest to the target coordinate as a target obstacle, acquiring effective node coordinates corresponding to the target obstacle, adding the effective node coordinates to the first coordinate set, and returning to the step of selecting the coordinates in the first coordinate set one by one as the target coordinate;
and S39, when no obstacle exists in a straight line path between the target coordinate and the end point coordinate, generating the pilot path according to the second coordinate set, the target coordinate and the end point coordinate.
In detail, a random forest algorithm or an ant colony algorithm can be used for randomly generating effective node coordinates according to the starting point coordinates and the end point coordinates, wherein the effective node coordinates refer to a node coordinate sequence starting from the starting point coordinates, and coordinates of obstacles do not exist between straight lines connecting nodes.
Specifically, the calculating of the effective path distance of the target coordinate refers to a distance of an effective path obtained by sequentially connecting effective nodes between the start point coordinate and the target coordinate.
In the embodiment of the invention, the target pilot is used for planning the path according to the target detection object to obtain the pilot path, so that the shortest moving path of the unmanned aerial vehicle cluster can be simulated, and the operation efficiency of the unmanned aerial vehicle is improved.
S5, regrouping the unmanned aerial vehicle group by using the topological communication network according to the pilot path, updating the initial motion model according to a regrouping result to obtain a standard motion model, and controlling the unmanned aerial vehicle group to operate the target detection object by using the target pilot according to the standard motion model;
in an embodiment of the present invention, the performing regrouping on the unmanned aerial vehicle group according to the pilot path by using the topology communication network includes:
generating a communication topology matrix of the target navigator according to the topology communication network;
carrying out topological sorting on the unmanned aerial vehicle group according to the communication topological matrix, and taking a topological serial number after the topological sorting as a recombination serial number of the unmanned aerial vehicle group;
and serially grouping the unmanned aerial vehicle group on the pilot path according to the reorganization number to complete reorganization and grouping.
In detail, a communication topology matrix of the target navigator can be generated according to the topology communication network by using a topology algorithm.
Specifically, the serial formation of the unmanned aerial vehicle group on the pilot path according to the reassembly number refers to serial formation in which the target pilot is taken as a head of the queue, the queue is sorted according to the reassembly number, and the queue follows the pilot path.
Specifically, the updating of the initial motion model according to the result of the reorganization and formation to obtain the standard motion model means that the arrangement sequence of the unmanned aerial vehicles in the unmanned aerial vehicle group in the initial motion model is adjusted according to the result of the reorganization and formation, and the flight speed execution delay, the yaw rate execution delay, and the pitch rate execution delay are adjusted according to the result of the reorganization and formation to obtain the standard motion model.
In detail, the controlling the unmanned aerial vehicle group to operate the target detection object according to the standard motion model by using the target pilot aircraft refers to moving along the pilot path according to the standard motion model, and operating the target detection object when the target detection object reaches the end point of the pilot path.
In the embodiment of the invention, the topological communication network is utilized to reorganize and form the unmanned aerial vehicle group according to the piloting path, the initial motion model is updated according to the reorganization and form result to obtain the standard motion model, the authority switching of the piloting machine of the unmanned aerial vehicle group can be realized, the unmanned aerial vehicle closest to the target detection object is used as a new piloting machine, and the other unmanned aerial vehicles in the unmanned aerial vehicle group can be formed according to the communication distance with the new piloting machine, so that the motion safety of the unmanned aerial vehicle group is ensured, the time consumption of forming the unmanned aerial vehicle group is reduced, and the target object searching efficiency of the unmanned aerial vehicle is improved.
And S6, when the unmanned aerial vehicle is the piloting unmanned aerial vehicle, controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model.
In the embodiment of the present invention, the method for controlling the group of unmanned aerial vehicles to operate on the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model is the same as the method for controlling the group of unmanned aerial vehicles to operate on the target detection object by using the target piloting machine according to the standard motion model in step S5, and details are not repeated here.
According to the embodiment of the invention, the initial motion model is established by utilizing the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw angular velocity, the pitch angular velocity and the execution delay, so that the motion relation of the unmanned aerial vehicle cluster can be accurately characterized, the subsequent unmanned aerial vehicle searching and path planning are assisted, and the communication state of each unmanned aerial vehicle in the unmanned aerial vehicle cluster can be mathematically expressed by utilizing a preset topological communication model to establish a topological communication network of the unmanned aerial vehicle cluster according to the communication information and the initial motion model, so that the subsequent control switching is facilitated; the method comprises the steps that a preset area to be monitored is searched by the unmanned aerial vehicle cluster, a monitoring total matrix of the unmanned aerial vehicle cluster can be accurately determined, a path for target searching is planned according to the monitoring total matrix, the searching efficiency of the unmanned aerial vehicle is guaranteed, a pilot path is obtained by planning the path according to a target detection object by using the target pilot, the shortest moving path of the unmanned aerial vehicle cluster can be simulated, and therefore the operation efficiency of the unmanned aerial vehicle is improved. Therefore, the control switching method for the multiple unmanned aerial vehicles can solve the problem of low efficiency when the multiple unmanned aerial vehicles execute tasks.
Fig. 4 is a functional block diagram of a control switching device for multiple drones according to an embodiment of the present invention.
The control switching device 100 of multiple drones according to the present invention can be installed in an electronic device. According to the realized functions, the control switching device 100 of the multiple drones can include a motion model module 101, a communication network module 102, a control switching module 103, a detection operation module 104 and a default operation module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the motion model module 101 is configured to obtain position information, motion information, and authority information of an unmanned aerial vehicle fleet, determine a piloted unmanned aerial vehicle of the unmanned aerial vehicle fleet according to the authority information, and establish an initial motion model of the unmanned aerial vehicle fleet by using the position information and the motion information;
the communication network module 102 is configured to obtain communication information of the unmanned aerial vehicle group, and construct a topology communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by using a preset topology communication model;
the control switching module 103 is configured to perform area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object, determine whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle; when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
the detection operation module 104 is configured to utilize the topological communication network to perform reorganization and formation on the unmanned aerial vehicle group according to the pilot path, update the initial motion model according to a result of reorganization and formation to obtain a standard motion model, and utilize the target pilot to control the unmanned aerial vehicle group to perform operation on the target detection object according to the standard motion model;
the default operation module 105 is configured to, when the unmanned aerial vehicle is the piloted unmanned aerial vehicle, control the unmanned aerial vehicle group to operate the target detection object according to the initial motion model by using the piloted unmanned aerial vehicle.
In detail, in the embodiment of the present invention, when the modules in the control switching device 100 for multiple drones are used, the same technical means as the control switching method for multiple drones described in fig. 1 to fig. 3 are used, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a control switching method of multiple drones according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a control switching program of multiple drones, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a Control switching program of multiple drones, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various data, such as codes of control switching programs of multiple drones, but also temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are commonly used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The control switching program of multiple drones stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, can implement:
acquiring position information, motion information and authority information of an unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
acquiring communication information of the unmanned aerial vehicle group, and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by using a preset topological communication model;
carrying out area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object;
when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and when the unmanned aerial vehicle is the piloting unmanned aerial vehicle, controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not repeated here.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring position information, motion information and authority information of an unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
acquiring communication information of the unmanned aerial vehicle group, and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by using a preset topological communication model;
carrying out area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object;
when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and when the unmanned aerial vehicle is the piloting unmanned aerial vehicle, controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be 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 the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A control switching method for multiple unmanned aerial vehicles is characterized by comprising the following steps:
acquiring position information, motion information and authority information of an unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
acquiring communication information of the unmanned aerial vehicle group, and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by using a preset topological communication model;
carrying out area search on a preset area to be monitored by using the unmanned aerial vehicle cluster, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle cluster monitors a preset target detection object;
when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting machine, switching the control authority of the unmanned aerial vehicle group to the target piloting machine, and planning a path according to the target detection object by using the control authority of the target piloting machine to obtain a piloting path;
utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and when the unmanned aerial vehicle is the piloting unmanned aerial vehicle, controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model.
2. The method of claim 1, wherein the establishing an initial motion model of the drone swarm using the position information and the motion information comprises:
extracting the positioning coordinate, course angle and pitch angle of the unmanned aerial vehicle cluster from the position information;
extracting the flight speed, pitch angle speed, yaw angle speed and execution delay of the unmanned aerial vehicle cluster from the motion information;
establishing the following initial motion model by using the positioning coordinates, the course angle, the pitch angle, the flight speed, the yaw rate, the pitch rate and the execution delay:
wherein, [ x ] i -x 0 i ,y i -y 0 i ,z i -z 0 i ,δ,γ,v,w,u] T Is the state vector of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, i refers to the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster, x i Real-time abscissa, x, of the ith drone in the drone swarm 0 i Is the cross axis coordinate, y, of the ith unmanned aerial vehicle in the positioning coordinates i Real-time vertical axis coordinate, y, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the vertical axis coordinate, z, of the ith unmanned aerial vehicle in the positioning coordinates i Is the real-time longitudinal axis coordinate, z, of the ith unmanned aerial vehicle in the unmanned aerial vehicle cluster 0 i Is the longitudinal axis coordinate of the ith unmanned aerial vehicle in the positioning coordinate, delta is the course angle, gamma is the pitch angle, v is the flight speed, w is the yaw angular velocity, u is the pitch angular velocity, T is a transposition symbol, tau is v Performing a delay for the flight speed among said performance delays, r v For control commands corresponding to said flight speed, τ w Performing a delay for yaw rate of said execution delay, r w For control commands corresponding to said yaw rate, τ u Performing a delay for a pitch angle rate of said performance delays, r u And the pitch angle speed corresponding to the flying speed.
3. The method of claim 1, wherein the establishing a topology communication network of the drone swarm according to the communication information and the initial motion model by using a preset topology communication model comprises:
selecting unmanned aerial vehicles in the unmanned aerial vehicle cluster one by one as target unmanned aerial vehicles, and extracting target positions of the target unmanned aerial vehicles from the initial motion model;
determining a communication unmanned aerial vehicle of the target unmanned aerial vehicle according to the communication information, and extracting communication delay between the target unmanned aerial vehicle and the communication unmanned aerial vehicle from the communication information;
and taking the target position as a graph node, connecting the graph node corresponding to the target unmanned aerial vehicle with the graph node corresponding to the communication unmanned aerial vehicle to obtain a node edge, and taking the communication delay as an edge weight corresponding to the node edge to obtain the topological communication network.
4. The method for switching control of multiple drones according to claim 1, wherein the area search for the preset area to be monitored by the drone group comprises:
selecting one unmanned aerial vehicle in the unmanned aerial vehicle cluster as a target unmanned aerial vehicle, and extracting a three-dimensional position coordinate of the target unmanned aerial vehicle from the initial motion model as a target position coordinate;
calculating the monitoring width and the monitoring length of the target unmanned aerial vehicle according to the target position coordinates, and calculating the investigation width of the unmanned aerial vehicle cluster according to the monitoring width and the monitoring length corresponding to all the target unmanned aerial vehicles;
and planning a monitoring path of the area to be monitored according to the investigation width to obtain a monitoring path, and searching the area to be detected according to the monitoring path.
5. The method of claim 4, wherein the calculating the monitoring width and the monitoring length of the target drone according to the target position coordinates comprises:
extracting a longitudinal axis coordinate of the target unmanned aerial vehicle from the target position coordinate;
acquiring monitor information of the target unmanned aerial vehicle, and extracting a longitudinal monitoring angle and a transverse detection angle from the monitor information;
calculating the monitoring width and the monitoring length according to the longitudinal axis coordinate, the longitudinal monitoring angle and the transverse detection angle by using a monitoring width algorithm as follows:
wherein R is 1 Is the monitoring width, R 2 The monitoring length is referred to, ceiling is an upward rounding symbol, theta is the longitudinal monitoring angle, alpha is the roll angle of the target unmanned aerial vehicle, beta is the transverse detection angle, and d is the longitudinal axis coordinate of the target unmanned aerial vehicle.
6. The method for controlling and switching among multiple drones according to claim 2, wherein the step of performing path planning according to the target probe by using the control authority of the target pilot machine to obtain a pilot path comprises:
acquiring three-dimensional point cloud information of the surrounding environment of the target detection object according to the control authority of the target navigator and the real-time transverse axis coordinate, the real-time vertical axis coordinate and the real-time longitudinal axis coordinate of the target navigator;
adding coordinate information of the target detection object in the three-dimensional point cloud information to obtain primary point cloud information, and adding coordinate information of the target navigator in the primary point cloud information to obtain secondary point cloud information;
acquiring obstacle information in the surrounding environment of the target detection object by using a monitor of the target navigator, and updating the secondary point cloud information according to the obstacle information to obtain standard point cloud information;
and planning a path according to the standard point cloud information by using a preset optimal path algorithm to obtain the pilot path.
7. The method of claim 6, wherein the utilizing the topological communication network to re-group the drone swarm according to the pilot path comprises:
generating a communication topology matrix of the target navigator according to the topology communication network;
carrying out topological sorting on the unmanned aerial vehicle group according to the communication topological matrix, and taking a topological serial number after the topological sorting as a recombination serial number of the unmanned aerial vehicle group;
and serially grouping the unmanned aerial vehicle group on the pilot path according to the reorganization number to complete reorganization and grouping.
8. A control auto-change over device of many unmanned aerial vehicle, its characterized in that, the device includes:
the motion model module is used for acquiring position information, motion information and authority information of the unmanned aerial vehicle cluster, determining a piloting unmanned aerial vehicle of the unmanned aerial vehicle cluster according to the authority information, and establishing an initial motion model of the unmanned aerial vehicle cluster by using the position information and the motion information;
the communication network module is used for acquiring communication information of the unmanned aerial vehicle group and constructing a topological communication network of the unmanned aerial vehicle group according to the communication information and the initial motion model by utilizing a preset topological communication model;
the control switching module is used for searching a preset area to be monitored by using the unmanned aerial vehicle group, and judging whether the unmanned aerial vehicle is the piloting unmanned aerial vehicle or not when one unmanned aerial vehicle in the unmanned aerial vehicle group monitors a preset target detection object; when the unmanned aerial vehicle is not the piloting unmanned aerial vehicle, taking the unmanned aerial vehicle as a target piloting vehicle, switching the control authority of the unmanned aerial vehicle group to the target piloting vehicle, and planning a path according to the target detection object by using the control authority of the target piloting vehicle to obtain a piloting path;
the detection operation module is used for utilizing the topological communication network to reorganize and form the unmanned aerial vehicle group according to the pilot path, updating the initial motion model according to the reorganization and form result to obtain a standard motion model, and utilizing the target pilot to control the unmanned aerial vehicle group to operate the target detection object according to the standard motion model;
and the default operation module is used for controlling the unmanned aerial vehicle group to operate the target detection object by using the piloting unmanned aerial vehicle according to the initial motion model when the unmanned aerial vehicle is the piloting unmanned aerial vehicle.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of control switching of multiple drones as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of controlling handover of multiple drones according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116185078A (en) * | 2023-04-28 | 2023-05-30 | 河北科技大学 | Self-adaptive command method, device, system and storage medium |
| CN117762166A (en) * | 2024-02-22 | 2024-03-26 | 杭州牧星科技有限公司 | multi-unmanned aerial vehicle cluster formation cooperative control method and system thereof |
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| CN116185078A (en) * | 2023-04-28 | 2023-05-30 | 河北科技大学 | Self-adaptive command method, device, system and storage medium |
| CN116185078B (en) * | 2023-04-28 | 2023-08-04 | 河北科技大学 | Self-adaptive command method, device, system and storage medium |
| CN117762166A (en) * | 2024-02-22 | 2024-03-26 | 杭州牧星科技有限公司 | multi-unmanned aerial vehicle cluster formation cooperative control method and system thereof |
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