CN114815894A - Path optimization method and device, electronic equipment, unmanned aerial vehicle and storage medium - Google Patents
Path optimization method and device, electronic equipment, unmanned aerial vehicle and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a path optimization method, a path optimization device, electronic equipment, an unmanned aerial vehicle and a storage medium, and relates to the technical field of unmanned aerial vehicles, wherein the method comprises the following steps: acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period; predicting the motion state of a dynamic obstacle corresponding to the dynamic obstacle position data under the condition that the dynamic obstacle position data exists in each obstacle position data to obtain predicted motion data of the dynamic obstacle; acquiring environmental wind parameters and attitude data of the unmanned aerial vehicle, wherein the environmental wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located; and optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm. The invention can at least partially solve the problem of how to avoid dynamic obstacles during the flight of the unmanned plane.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a path optimization method and device, electronic equipment, an unmanned aerial vehicle and a storage medium.
Background
Unmanned aerial vehicle technical development is different day by day, and nowadays, unmanned aerial vehicle can both be used in each field and some tasks are carried out.
The unmanned aerial vehicle path planning problem is an important component of an unmanned aerial vehicle dispatching system framework, and during existing unmanned aerial vehicle path planning, some static obstacles are usually considered and avoided during path planning.
When the unmanned aerial vehicle flies, how to avoid dynamic obstacles is still a problem to be solved.
Disclosure of Invention
The invention provides a path optimization method, a path optimization device, electronic equipment, an unmanned aerial vehicle and a storage medium, which can at least partially solve the technical problems.
Embodiments of the invention may be implemented as follows:
in a first aspect, the present invention provides a path optimization method applied to an unmanned aerial vehicle, where the unmanned aerial vehicle includes a laser radar, and the method includes:
acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period;
predicting the motion state of a dynamic obstacle corresponding to the dynamic obstacle position data under the condition that the dynamic obstacle position data exists in each obstacle position data to obtain predicted motion data of the dynamic obstacle;
acquiring environmental wind parameters and attitude data of the unmanned aerial vehicle, wherein the environmental wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located;
and optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
Optionally, the drone further comprises a visible light camera, the method further comprising:
acquiring a plurality of continuous image data of a target area shot by the visible light camera in the preset time period, wherein the target area is an area where an obstacle corresponding to the dynamic obstacle position data is located;
predicting the motion state of the dynamic obstacle according to a plurality of continuous image data to obtain auxiliary motion data of the dynamic obstacle;
optimizing the predicted movement data based on the auxiliary movement data to obtain optimized predicted movement data;
the optimizing the initial flight path of the unmanned aerial vehicle comprises: and optimizing the initial flight path of the unmanned aerial vehicle according to the optimized predicted motion data, the environment wind parameter and the attitude data based on the locust algorithm.
Optionally, when the number of the dynamic obstacles corresponding to the dynamic obstacle position data is two or more, the method further includes:
respectively acquiring optimized predicted movement data corresponding to each dynamic obstacle;
merging a plurality of optimized predicted motion data to obtain cooperative motion data;
the optimizing the initial flight path of the unmanned aerial vehicle comprises: and optimizing the initial flight path of the unmanned aerial vehicle according to the cooperative motion data, the environmental wind parameter and the attitude data based on the locust algorithm.
Optionally, before the optimizing the initial flight path of the drone, the method further comprises:
and eliminating the error of the cooperative motion data based on the Lyapunov function.
Optionally, the method further comprises the step of obtaining the initial flight path, the step comprising:
acquiring an unmanned aerial vehicle map, wherein the unmanned aerial vehicle map comprises a target point location, a starting point location and a static barrier point location marked by a user;
and generating the initial flight path according to the starting point position, the target point position and the static obstacle point position.
Optionally, the method further comprises:
judging whether the position data corresponding to the static obstacle point position exists in the obstacle position data or not;
and if so, screening the obstacle position data except the position data corresponding to the static obstacle point position as the dynamic obstacle position data.
In a second aspect, the present invention provides a path optimization device, applied to an unmanned aerial vehicle, where the unmanned aerial vehicle includes a laser radar, and the device includes:
the obstacle position data acquisition unit is used for acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period;
the prediction unit is used for predicting the motion state of the dynamic obstacle corresponding to the dynamic obstacle position data under the condition that the dynamic obstacle position data exists in each obstacle position data to obtain the predicted motion data of the dynamic obstacle;
the environment parameter acquiring unit is used for acquiring environment wind parameters and attitude data of the unmanned aerial vehicle, wherein the environment wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located;
and the path optimization unit is used for optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
In a third aspect, the invention provides an unmanned aerial vehicle, which comprises a radar, a wind sensor, a gyroscope and a controller, wherein the controller is respectively in communication connection with the radar, the wind sensor and the gyroscope;
the radar is used for acquiring and feeding back position data of the obstacle to the controller;
the wind power sensor is used for acquiring and sending environmental wind parameters to the controller;
the gyroscope is used for acquiring and sending attitude data of the unmanned aerial vehicle to the controller;
the controller is configured to perform the path optimization method of any one of the above.
In a fourth aspect, the present invention provides an electronic device comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the program.
In a fifth aspect, the present invention provides a storage medium, where the storage medium includes a computer program, and the computer program controls, when running, an electronic device in which the computer-readable storage medium is located to implement the steps of any one of the above methods.
The beneficial effects of the embodiment of the invention include, for example:
when unmanned aerial vehicle runs into dynamic barrier, through predicting the motion state to dynamic barrier, optimize unmanned aerial vehicle's initial flight route, and then make unmanned aerial vehicle can avoid dynamic barrier effectively when the flight, prevent that unmanned aerial vehicle from taking place the condition appearance of unmanned aerial vehicle damage because of hitting dynamic barrier.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a path optimization method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating steps of an initial flight path obtaining method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an unmanned aerial vehicle map according to an embodiment of the present invention;
fig. 5 is a block diagram of a path optimization apparatus according to an embodiment of the present invention.
Icon: 100-an electronic device; 110-a memory; 120-a processor; 130-a communication module; 300-path optimization means; 301-obstacle position data acquisition unit; 302-a prediction unit; 303-an environmental parameter acquisition unit; 304-path optimization unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
Referring to fig. 1, a block diagram of an electronic device 100 provided in the present application is shown, including a memory 110, a processor 120 and a communication module 130. The memory 110, the processor 120, and the communication module 130. The various elements are directly or indirectly electrically connected to one another to enable data transfer or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 110 is used to store programs or data. The Memory 110 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 120 is used to read/write data or programs stored in the memory and perform corresponding functions.
The communication module 130 is configured to establish a communication connection between the electronic device and another communication terminal through the network, and to transmit and receive data through the network.
It should be understood that the structure shown in fig. 1 is only a schematic structural diagram of the electronic device 100, and the electronic device 100 may further include more or less components than those shown in fig. 1, or have a different configuration than that shown in fig. 1, for example, the electronic device 100 may further include a path optimization unit, a prediction unit, and the like. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The existing technical scheme is that the position of an obstacle is obtained through a radar mounted by an unmanned aerial vehicle, then the next route of the unmanned aerial vehicle is planned according to the obtained position, so that the obstacle is avoided, or the position of the obstacle is obtained by processing an image shot by a mounted camera, or the position precision of the obstacle is improved by combining the two. The obstacle avoidance instruction is made in a passive mode.
However, in actual flight missions, many obstacles are not static and must be avoided when approaching; for the obstacle far away, the original flight path of the unmanned aerial vehicle is not required to be changed sometimes; for what may happen to intersect the drone on the flight path at the next time, a prediction in advance is needed. Therefore, it is very necessary to evaluate the unmanned aerial vehicle path whether there is a collision risk, and optimize the unmanned aerial vehicle path in advance.
Referring to fig. 2, a path optimization method provided for an embodiment of the present specification is applied to a drone, where the drone includes a laser radar, and the method includes the following steps:
step S120: and acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period.
Step S130: and under the condition that dynamic obstacle position data exist in each obstacle position data, predicting the motion state of a dynamic obstacle corresponding to the dynamic obstacle position data to obtain predicted motion data of the dynamic obstacle.
Step S140: acquiring environmental wind parameters and attitude data of the unmanned aerial vehicle, wherein the environmental wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located.
Step S150: and optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
The above steps are explained below.
In step S120, a plurality of continuous obstacle position data scanned by the laser radar within a preset time period are obtained.
The preset time period may be a period of time after the laser radar starts scanning the obstacle position data, or a period of time when the laser radar starts timing.
The laser radar who sets up on unmanned aerial vehicle starts when unmanned aerial vehicle starts to fly according to established initial route, starts real-time scanning monitoring promptly and whether has the barrier. The laser radar is a radar system that emits a laser beam to detect a characteristic quantity such as a position, a velocity, and the like of a target. When there is an obstacle, a detection signal (laser beam) is transmitted to the obstacle, and then a received signal (target echo) reflected from the obstacle is compared with the transmitted signal, and after appropriate processing is performed, the relevant information of the obstacle, such as target distance, direction, height, speed, attitude, even shape and other parameters, can be obtained, so that the obstacle is identified, and the data, namely the data of the obstacle position, is obtained.
Taking a preset time period as an example of a time period after the laser radar scans the obstacle position data, scanning is performed every 0.5 second within 5 seconds to obtain a plurality of continuous obstacle position data, and then the obstacle position data are sent to the processor.
Optionally, as shown in fig. 3, the method further includes a step of obtaining the initial flight path, which includes:
step S111: acquiring an unmanned aerial vehicle map, wherein the unmanned aerial vehicle map comprises a target point location, a starting point location and a static barrier point location marked by a user;
step S112: and generating the initial flight path according to the starting point position, the target point position and the static obstacle point position.
As shown in fig. 4, the map of the unmanned aerial vehicle may be a map of a user importing from an external device into the unmanned aerial vehicle, and the map of the unmanned aerial vehicle may include a starting point location where the unmanned aerial vehicle starts, a target point location where the unmanned aerial vehicle needs to arrive, and a static obstacle point location marked by the user on the map of the unmanned aerial vehicle.
After the processor obtains the unmanned aerial vehicle map imported by the user, a path capable of avoiding the static barrier point position, namely an initial flight path, can be automatically generated according to the target point position, the starting point position and the static barrier point position marked by the user.
Optionally, the method further comprises:
judging whether the position data corresponding to the static obstacle point position exists in the obstacle position data or not;
and if so, screening the obstacle position data except the position data corresponding to the static obstacle point position as the dynamic obstacle position data.
The obstacles scanned by the laser radar can comprise dynamic obstacles and static obstacles, and in order to screen out the dynamic obstacles in the obstacles scanned by the laser radar, as an optional embodiment, the obstacle position data can be judged from the obstacle position data in a mode of combining an unmanned aerial vehicle map and the obstacle position data scanned by the laser radar, and the obstacle position data is marked as the position data corresponding to the static obstacle point position on the unmanned aerial vehicle map.
In step S130, when there is dynamic obstacle position data in each obstacle position data, the motion state of the dynamic obstacle corresponding to the dynamic obstacle position data is predicted, and the predicted motion data of the dynamic obstacle is obtained.
The obstacles scanned by the laser radar can comprise dynamic obstacles and static obstacles, and when dynamic obstacle position data exist in the obstacle position data, the processor can perform data processing on the radar data acquired by the laser radar so as to predict the next motion state of the dynamic obstacles. For example, the laser radar obtains the position information of the position of the dynamic obstacle a every 0.5 second within 5 consecutive seconds, and then the processor may calculate the speed and direction of the movement of the dynamic obstacle a according to the position information of the dynamic obstacle a, and further predict the next movement speed and movement direction of the dynamic obstacle a, so as to obtain the predicted movement data of the dynamic obstacle a.
Optionally, the drone further comprises a visible light camera, the method further comprising:
acquiring a plurality of continuous image data of a target area shot by the visible light camera in the preset time period, wherein the target area is an area where an obstacle corresponding to the dynamic obstacle position data is located;
predicting the motion state of the dynamic obstacle according to a plurality of continuous image data to obtain auxiliary motion data of the dynamic obstacle;
and optimizing the predicted movement data based on the auxiliary movement data to obtain optimized predicted movement data.
In order to make the prediction of the unmanned aerial vehicle on the motion of the dynamic obstacle more accurate, a visible light camera can be carried on the unmanned aerial vehicle. The visible light camera can be a camera which is used with a laser radar, can lock a shooting target and can shoot 360 degrees.
The target area can be an area where a dynamic obstacle scanned by the laser radar is located, when the processor judges the dynamic obstacle according to obstacle position data acquired by the laser radar, the visible light camera can determine the target area according to the dynamic obstacle position data, and the visible light camera can shoot continuous image data in the target area within a preset time period. By processing the continuous images, the motion state of the dynamic obstacle can be predicted in the same way, and then the auxiliary motion data of the dynamic obstacle can be obtained. For example, the image data captured by the visible light camera is an image of the target area, and the image data is processed to establish a spatial coordinate system in the image, so as to determine the position of the dynamic obstacle in the image through the spatial coordinate system. Furthermore, the speed and direction of the motion of the dynamic obstacle in a preset time period are calculated according to the positions of the dynamic obstacle in the images in the continuous image data, so that the motion state of the dynamic obstacle is predicted, and the auxiliary motion data of the dynamic obstacle is obtained.
After the auxiliary motion data are obtained, the predicted motion data can be optimized through the auxiliary motion data, the optimized predicted motion data are obtained, and the accuracy of the predicted motion data is improved.
The kalman filter is a filter for estimating the target azimuth by recursion, and can estimate and predict the position information of the target system at the next moment from a series of measurements containing noise, predict the track of the target, and correct the prediction result by using the tracking result with high certainty, that is, the estimation value of the current state can be calculated by acquiring the estimation value of the state at the previous moment and the observation value of the current state, so that it is not necessary to record the historical information of observation or estimation. Therefore, as an alternative embodiment, the motion state of the dynamic obstacle can be predicted by filtering the image captured by the visible camera by using kalman filtering.
Setting a nonlinear function f for describing the current state vector
One state vector ahead
To (3) is performed. An estimate of the state of the basic structure. Previous state vector
By using
Expressed as:
companion condition for characterizing Kalman filtering
Is a jacobian matrix that derives the partial derivatives from f to x,
is a jacobian matrix of partial derivatives from h to w,
is a jacobian matrix of partial derivatives from f to v,
is a Jacobian matrix for the derivation of the partial derivatives from the f to w. Here, the
Is from having covariance
Is obtained from a zero-mean multivariate normal distribution of (A) and also has covariance
Zero mean white gaussian noise.
The kalman filtering process can be conceptualized as two phases: "prediction" and "correction". The kalman filter calculation step is as follows:
computing state evaluation propagation
Wherein
Non-linear state transfer function, x k-1 Position and orientation, u, representing a drone k-1 Representing simple movement of the drone, 0 representing noise interference.
Obtaining error covariance propagation
Calculation of Kalman gain matrix
The state estimate and the error covariance are updated to estimate an updated drone environment location. Kalman hereinFilter gain
Is used for correcting state
And its covariance
Updated unmanned aerial vehicle state vector
See the following equation:
wherein
And
representing new independent random variables with zero mean and covariance matrices. This filtering may result in an accurate location of an obstacle in the drone path tracking area.
Optionally, when the number of the dynamic obstacles corresponding to the dynamic obstacle position data is two or more, the method further includes:
respectively acquiring optimized predicted movement data corresponding to each dynamic obstacle;
and combining a plurality of optimized predicted motion data to obtain cooperative motion data.
When there are more than two dynamic obstacles, the optimized predicted movement data corresponding to each dynamic obstacle can be respectively obtained, and then the optimized predicted movement data corresponding to each dynamic obstacle is combined to obtain the cooperative movement data of all the dynamic obstacles.
Optionally, the method further comprises:
and eliminating the error of the cooperative motion data based on the Lyapunov function.
The lyapunov function is used to determine the reliability of the framework at equilibrium. The error dynamic model is characterized in that:
wherein, theta di Representing the path angle with respect to an ideal point, the derivative of the above equation can be described in terms of the path boundary. The objective of the collaborative path tracking process is to characterize the error dynamic model e of each drone k ＝[p ek ，q ek ，θ ek ]Is eliminated to [ 000 ]]. The ultimate goal is that drones take advantage of their situation to minimize following errors to predefined paths and each other's positions to maintain cooperative movement.
From the above formula, the input of each drone contains the ground speed V gk Course angle rate omega k And assumed speeds of other drones. At this time, when a group of drones is traveling on a route, the drones that we track are considered as a target point, which is looking for an ideal route, P d(sd) ＝(p d(sd) ，q d(sd) ) Whereins d Which represents the boundary of the length of the curve,
the velocity of the hypothetical drone representing the focus of development.
Let p e0 ＝p d -p 0 ,q e0 ＝q d -q 0 Error of
Is the tracking error of the predicted target point. Now that the user has finished the process,
can be obtained by the following formula:
wherein the content of the first and second substances,
indicating the basic speed
Is continuous and Ke is a control parameter. It can be known that when there is an error
When the error is small, the speed of the unmanned aerial vehicle is coordinated to the maximum direction
When bigger, the unmanned aerial vehicle speed is coordinated to minimum direction.
Step S150: and optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
The locust algorithm is derived from the activity characteristics of the locust, the food source is found to be an important behavior of a locust swarm, the main bionic principle is to map the small-range movement behavior of the larva to local development with short step length, the large-range movement behavior of the adult is mapped to global exploration with long step length, and optimization is carried out in a way similar to 'step cooperation'. The behavior cycle can be divided into two phases: and (5) detecting and developing. The numerical model of this behavior is as follows:
wherein X i Denotes the position of locust i, S i Representing social influence, G representing gravity influence, a is wind-horizontal flow influence.
In the above formula, s (r) fe -r /l-e -r And (3) representing a social influence coefficient, wherein f and l are an attraction strength parameter and an attraction scale parameter respectively. N denotes the population size. d ij ＝|X j -X i And | represents the distance between the individual i and the individual j.
The locust is influenced by gravity
Wherein g represents the attractive force of the gravity,
representing a unit vector pointing to the geocentric.
Influence of wind advection
u denotes a constant drift factor (meaning that flight motion shifts occur even in the absence of wind), e w Representing a unit vector pointing to the wind direction.
After a number of iterations, convergence was found to be poor, and the numerical model was optimized as:
wherein ub and lb are respectively the upper and lower limits of the current dimension,
is the position of the current optimal individual in the current dimension, and introduces a decreasing coefficient c for reducing the comfort zone, and the formula is as follows:
where cmax represents the most extreme value (approaching 1), cmin represents the minimum value, I represents the current iteration, and MaxI represents the maximum number of cycles. By introducing the decreasing coefficient c, the locust can better control the 'close circle' between the locust and others, thereby avoiding excessive aggregation and reducing the probability of the algorithm falling into local optimum.
Lay5 layer 5: it consists of a single node set to S' to perform a basic adder, the equivalent result being expressed as:
the algorithm outputs the collision avoidance motion of the unmanned aerial vehicle, so that the effect of optimizing the flight path of the unmanned aerial vehicle is realized.
Based on the same inventive concept, as shown in fig. 5, an embodiment of the present invention provides a path optimization apparatus 300, which is applied to an unmanned aerial vehicle, where the unmanned aerial vehicle includes a laser radar, and the path optimization apparatus 300 includes:
an obstacle position data obtaining unit 301, configured to obtain multiple continuous obstacle position data scanned by the laser radar within a preset time period;
a prediction unit 302, configured to, when there is dynamic obstacle position data in each obstacle position data, predict a motion state of a dynamic obstacle corresponding to the dynamic obstacle position data, and obtain predicted motion data of the dynamic obstacle;
an environment parameter obtaining unit 303, configured to obtain an environment wind parameter and attitude data of the unmanned aerial vehicle, where the environment wind parameter includes wind power and a wind direction of an environment where the unmanned aerial vehicle is located;
and a path optimization unit 304, based on the locust algorithm, for optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameter and the attitude data.
With regard to the above-mentioned path optimization device 300, the specific functions of each unit have been described in detail in the embodiments of the path optimization method provided in the present specification, and will not be elaborated herein.
Based on the same invention concept, the embodiment of the invention provides an unmanned aerial vehicle which comprises a radar, a wind sensor, a gyroscope and a controller, wherein the controller is in communication connection with the radar, the wind sensor and the gyroscope respectively; the radar is used for acquiring and feeding back position data of the obstacle to the controller; the wind sensor is used for acquiring and sending environmental wind parameters to the controller; the gyroscope is used for acquiring and sending attitude data of the unmanned aerial vehicle to the controller; the controller is configured to perform the steps of any of the foregoing path optimization methods.
Based on the same inventive concept, the present specification embodiments provide a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the steps of any one of the foregoing path optimization methods.
By adopting the scheme in the embodiment of the invention, the following effects can be at least partially achieved:
1. when unmanned aerial vehicle runs into dynamic barrier, through predicting the motion state to dynamic barrier, optimize unmanned aerial vehicle's initial flight route, and then make unmanned aerial vehicle can avoid dynamic barrier effectively when the flight, prevent that unmanned aerial vehicle from taking place the condition appearance of unmanned aerial vehicle damage because of hitting dynamic barrier.
2. And the visible light camera is arranged to obtain the auxiliary motion data, so that the motion state of the dynamic barrier is more accurately predicted.
3. The cooperative motion data are obtained, errors of the cooperative motion data are eliminated based on the Lyapunov function, and the obstacle avoidance capacity of the unmanned aerial vehicle on a plurality of dynamic obstacles is improved.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A path optimization method, applied to a drone including a lidar, the method comprising:
acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period;
predicting the motion state of a dynamic obstacle corresponding to the dynamic obstacle position data under the condition that the dynamic obstacle position data exists in each obstacle position data to obtain predicted motion data of the dynamic obstacle;
acquiring environmental wind parameters and attitude data of the unmanned aerial vehicle, wherein the environmental wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located;
and optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
2. The path optimization method of claim 1, wherein the drone further includes a visible light camera, the method further comprising:
acquiring a plurality of continuous image data of a target area shot by the visible light camera in the preset time period, wherein the target area is an area where an obstacle corresponding to the dynamic obstacle position data is located;
predicting the motion state of the dynamic obstacle according to a plurality of continuous image data to obtain auxiliary motion data of the dynamic obstacle;
optimizing the predicted movement data based on the auxiliary movement data to obtain optimized predicted movement data;
the optimizing the initial flight path of the unmanned aerial vehicle comprises: and optimizing the initial flight path of the unmanned aerial vehicle according to the optimized predicted motion data, the environment wind parameter and the attitude data based on the locust algorithm.
3. The method for optimizing a route according to claim 2, wherein when the number of the dynamic obstacles corresponding to the dynamic obstacle position data is two or more, the method further comprises:
respectively acquiring optimized predicted movement data corresponding to each dynamic obstacle;
merging a plurality of optimized predicted motion data to obtain cooperative motion data;
the optimizing the initial flight path of the unmanned aerial vehicle comprises: and optimizing the initial flight path of the unmanned aerial vehicle according to the cooperative motion data, the environmental wind parameter and the attitude data based on the locust algorithm.
4. The path optimization method of claim 3, wherein prior to said optimizing the initial flight path of the drone, the method further comprises:
and eliminating the error of the cooperative motion data based on the Lyapunov function.
5. The path optimization method of claim 1, further comprising the step of obtaining the initial flight path, the step comprising:
acquiring an unmanned aerial vehicle map, wherein the unmanned aerial vehicle map comprises a target point location, a starting point location and a static barrier point location marked by a user;
and generating the initial flight path according to the starting point position, the target point position and the static obstacle point position.
6. The path optimization method of claim 5, wherein the method further comprises:
judging whether the position data corresponding to the static obstacle point position exists in the obstacle position data or not;
and if so, screening the obstacle position data except the position data corresponding to the static obstacle point position as the dynamic obstacle position data.
7. A path optimisation device, characterized in that, is applied to a drone, the drone includes a lidar, the device includes:
the obstacle position data acquisition unit is used for acquiring a plurality of continuous obstacle position data scanned by the laser radar in a preset time period;
the prediction unit is used for predicting the motion state of the dynamic obstacle corresponding to the dynamic obstacle position data under the condition that the dynamic obstacle position data exists in each obstacle position data to obtain the predicted motion data of the dynamic obstacle;
the environment parameter acquiring unit is used for acquiring environment wind parameters and attitude data of the unmanned aerial vehicle, wherein the environment wind parameters comprise wind power and wind direction of the environment where the unmanned aerial vehicle is located;
and the path optimization unit is used for optimizing the initial flight path of the unmanned aerial vehicle according to the predicted motion data, the environmental wind parameters and the attitude data based on the locust algorithm.
8. An unmanned aerial vehicle is characterized by comprising a radar, a wind sensor, a gyroscope and a controller, wherein the controller is respectively in communication connection with the radar, the wind sensor and the gyroscope;
the radar is used for acquiring and feeding back position data of the obstacle to the controller;
the wind power sensor is used for acquiring and sending environmental wind parameters to the controller;
the gyroscope is used for acquiring and sending attitude data of the unmanned aerial vehicle to the controller;
the controller is used for executing the path optimization method of any claim 1-6.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the path optimization method according to any one of claims 1 to 6 when executing the program.
10. A storage medium comprising a computer program, wherein the computer program controls an electronic device in which the storage medium is located to execute the path optimization method according to any one of claims 1 to 6 when the computer program runs.
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