CN117346793A - Unmanned plane path planning method, device, equipment and medium based on PG algorithm - Google Patents

Unmanned plane path planning method, device, equipment and medium based on PG algorithm Download PDF

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CN117346793A
CN117346793A CN202311644958.XA CN202311644958A CN117346793A CN 117346793 A CN117346793 A CN 117346793A CN 202311644958 A CN202311644958 A CN 202311644958A CN 117346793 A CN117346793 A CN 117346793A
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周文
肖蘅
穆震杰
张汇
包乃源
杨亚婷
吕志强
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National University of Defense Technology
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Abstract

The application belongs to the technical field of unmanned aerial vehicles, and relates to an unmanned aerial vehicle path planning method, device, equipment and medium based on a PG algorithm. The method comprises the following steps: acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information; calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat ball model; according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model; according to the three-dimensional map information, combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning; and solving the multi-objective optimization function by taking the threat ball model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point. The method and the device can improve the dynamic obstacle avoidance capability of unmanned plane path planning.

Description

Unmanned plane path planning method, device, equipment and medium based on PG algorithm
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle path planning method, device, equipment and medium based on a PG algorithm.
Background
The path planning problem is always a research hotspot in the unmanned aerial vehicle field. Unmanned aerial vehicle path planning refers to the comprehensive unmanned aerial vehicle performance parameters and flight scene factors, the topography is fully utilized, the planning and unmanned aerial vehicle flight cost is reduced as much as possible on the premise of guaranteeing flight safety and avoiding obstacles, and the comprehensive optimal flight path is calculated.
The path planning algorithm is widely studied in the unmanned aerial vehicle field, and the current path planning algorithm mainly comprises: sampling methods, intelligent optimization algorithms, machine learning methods, etc.
The sampling method comprises a random road map method, a random search tree method and the like, is often used for sampling in the early stage, but the planning process of the two methods has the obvious defect of high time consumption, and the method is applied to an environment which is only suitable for considering static obstacles and cannot consider the collision prevention problem of dynamic obstacles, so that the method obviously does not meet the actual needs.
The intelligent optimization algorithm, which can also be called as a planning method based on evolution calculation, has the main idea that the intelligent optimization algorithm is influenced by a natural selection mechanism of the biological evolution 'win-lose' and a transmission rule of genetic information, and the optimal solution is found out through natural evolution in a population formed by a plurality of alternative solutions by utilizing the iterative simulation of the algorithm to approximate the process. The intelligent optimization algorithm generally comprises an ant colony algorithm (Ant Colony Optimization, ACO), a particle swarm algorithm (Particle Swarm Optimization, PSO), an artificial bee colony algorithm (Artificial Bee Colony Algorithm, ABC) and the like, and each of the methods has a plurality of branches and an improved optimization algorithm. Most of intelligent optimization algorithms are generated by scientists through researching group behaviors of natural organisms and simulating hunting, social, foraging and other behaviors of the organisms. However, when these intelligent algorithms are used for processing a large amount of sample data, more computer memory is required, and the computing power of the computer is very high, so that problems of local optimum, low algorithm convergence rate and the like are easy to occur.
The machine learning method can enable the unmanned aerial vehicle to obtain the perception capability and the data input capability of the environment through training, and select effective control decisions to avoid the obstacle, but the method generally only plays a role in a specific task due to the huge training amount, and is difficult to expand to other complex environments.
The PG (plant growth) algorithm is used as a sampling-based incremental rapid search algorithm, so that the defects of low path search speed and difficulty in considering constraint conditions in the algorithm can be overcome. However, the basic PG algorithm adopts a random path-finding strategy, so that dynamic obstacles are difficult to process, guide information is lacked, the path cost marked by algorithm rules is too high, and the algorithm calculation time is relatively long under a complex dynamic obstacle distribution environment.
Disclosure of Invention
Based on the above, it is necessary to provide a method, a device, equipment and a medium for planning an unmanned aerial vehicle path based on a PG algorithm, which can improve the dynamic obstacle avoidance capability in unmanned aerial vehicle path planning, improve the algorithm operation efficiency, reduce the path planning cost, obtain global optimum for complex environment convergence, and have less time consumption, high efficiency and fast searching speed.
The unmanned aerial vehicle path planning method based on the PG algorithm comprises the following steps:
acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information, and setting a starting point and a target point of path planning;
calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat sphere model of each dynamic obstacle;
according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model;
according to the three-dimensional map information, combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning;
and solving the multi-objective optimization function by taking the threat ball model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point.
In one embodiment, according to three-dimensional map information, in combination with the path length, mobility cost, threat level and/or flight height of the unmanned aerial vehicle, a multi-objective optimization function for unmanned aerial vehicle path planning is established, comprising:
according to the three-dimensional map information, a path length cost function is built by combining the path length of the unmanned aerial vehicle, a mobility cost function is built by combining the mobility cost of the unmanned aerial vehicle, a threat degree cost function is built by combining the threat degree of the unmanned aerial vehicle, and a flying height cost function is built by combining the flying height of the unmanned aerial vehicle;
and establishing a multi-objective optimization function of unmanned plane path planning according to the path length cost function, the maneuverability cost function, the threat degree cost function and/or the flying height cost function.
In one embodiment, the threat ball model includes:
；
in the method, in the process of the invention,for dynamic uncertainty, ++>Is a unit time->For dynamic obstacle speed, +.>For dynamic obstacle radius +.>Radius of the threat ball model;
the obstacle avoidance range cone model comprises:
；
in the method, in the process of the invention,for the adaptive safety distance between the unmanned aerial vehicle and the dynamic obstacle, < > a->Is the speed of the unmanned aerial vehicle;
with unmanned aerial vehicle position asConnecting the unmanned plane with the threat ball model, and crossing +.>Point tangential line->And->Tangent to the threat sphere model, and constructing an obstacle avoidance range cone model.
In one embodiment, in combination with the path length, mobility cost, threat level, and/or flight altitude of the drone, a multi-objective optimization function for drone path planning is established, comprising: establishing a path length cost function by combining the path length of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Path length cost function of>For the number of waypoints the unmanned path passes, +.>Each waypoint corresponds to a node coordinate in the environment.
In one embodiment, in combination with the path length, mobility cost, threat level, and/or flight altitude of the drone, a multi-objective optimization function for drone path planning is established, comprising: establishing a mobility cost function by combining the mobility cost of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Mobility cost function of>For the number of waypoints the unmanned path passes, +.>For the corner cost between every two adjacent nodes in the unmanned plane path, +.>As a factor of the cost of the material,for the rotation angle amplitude of the current node and the next node in the unmanned plane path, +.>The maximum turning angle of the unmanned aerial vehicle.
In one embodiment, in combination with the path length, mobility cost, threat level, and/or flight altitude of the drone, a multi-objective optimization function for drone path planning is established, comprising: establishing a threat degree cost function by combining the threat degree of the unmanned aerial vehicle:
；
in the method, in the process of the invention,for unmanned plane path->Threat level cost function of->For the number of waypoints that the drone path passes through,Qfor the total number of dynamic barriers +.>For threat level cost factor, < >>Threat level costs for forming a road segment for a single threat to two neighboring nodes, +.>For threatening the radius of the sphere model +.>For the width of the early warning area, & lt & gt>To unmanned planeqStraight line distance of the center of each dynamic barrier.
In one embodiment, in combination with the path length, mobility cost, threat level, and/or flight altitude of the drone, a multi-objective optimization function for drone path planning is established, comprising: establishing a flight altitude cost function by combining the flight altitude of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Is a flying height cost function of->For waypoints->Height costs arising beyond the desired flight height range, +.>For the number of waypoints the unmanned path passes, +.>Unit cost for deviation in flying height range, +.>For the flying height of the unmanned aerial vehicle relative to the ground, < ->For maximum of the flight height range, +.>Is the minimum of the range of flying heights.
Unmanned aerial vehicle path planning device based on PG algorithm includes:
the acquisition module is used for acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information and setting a starting point and a target point of path planning;
the first modeling module is used for calculating dynamic uncertainty according to the dynamic obstacle information and establishing a threat sphere model of each dynamic obstacle;
the second modeling module is used for calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, connecting the unmanned aerial vehicle with the threat ball model, and establishing an obstacle avoidance range cone model;
the function module is used for establishing a multi-objective optimization function of unmanned aerial vehicle path planning according to three-dimensional map information by combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle;
and the solving module is used for solving the multi-objective optimization function by taking the threat sphere model and the obstacle avoidance range cone model as the constraint of the PG algorithm to obtain an optimal path from the starting point to the target point.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information, and setting a starting point and a target point of path planning;
calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat sphere model of each dynamic obstacle;
according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model;
according to the three-dimensional map information, combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning;
and solving the multi-objective optimization function by taking the threat ball model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information, and setting a starting point and a target point of path planning;
calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat sphere model of each dynamic obstacle;
according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model;
according to the three-dimensional map information, combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning;
and solving the multi-objective optimization function by taking the threat ball model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point.
The unmanned plane path planning method, device, equipment and medium based on the PG algorithm improve the PG algorithm which is only applicable to global static planning, initialize three-dimensional map information and determine path planning environment information; constructing a threat ball model, and processing the obstacle into a static obstacle by calculating dynamic uncertainty of the obstacle by using the information such as speed vectors, sizes and the like of the obstacle and the unmanned aerial vehicle; introducing self-adaptive safety distance between the unmanned aerial vehicle and the threat ball, ensuring the safety distance of obstacle avoidance of the unmanned aerial vehicle, and constructing an obstacle avoidance range cone model; establishing a multi-objective optimization function according to conditions related to path length, flight height, threat level and mobility cost; and using a PG algorithm to find out the optimal path to reach the target point under the constraint of the multi-target optimization function. Aiming at the problem that the operation efficiency of the current PG algorithm is lower in a complex obstacle environment, the method and the device realize the processing function of the PG algorithm on dynamic obstacles by using a threat sphere model, and improve the actual value of the PG algorithm; the reference range of path finding is limited for the PG algorithm by utilizing the obstacle avoidance range cone model, blind path finding is avoided, so that the path points can be obtained more quickly by random sampling of the PG algorithm, and the algorithm convergence rate is improved; the path planned by the PG algorithm is optimized by utilizing the multi-objective optimization function, so that the blindness problem of the original PG algorithm on path planning is solved, the planned path cost is reduced, and the more ideal unmanned aerial vehicle flight path is obtained. The method converts the dynamic problem into the static problem, improves the processing capacity of the PG algorithm on dynamic obstacle, avoids the blindness of the PG algorithm in path finding under the constraint of the multi-objective optimization function, greatly reduces the path planning cost of the unmanned aerial vehicle, and greatly improves the path planning effect of the PG algorithm.
Drawings
Fig. 1 is an application scenario diagram of an unmanned aerial vehicle path planning method based on a PG algorithm in one embodiment;
fig. 2 is a flow chart of a method for planning a path of an unmanned aerial vehicle based on a PG algorithm in one embodiment;
fig. 3 is a schematic diagram of a threat ball model and a obstacle avoidance range cone model in an unmanned aerial vehicle path planning method based on a PG algorithm in one embodiment;
fig. 4 is a block diagram of a path planning apparatus for an unmanned aerial vehicle based on PG algorithm in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In addition, descriptions such as those related to "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated in this application. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality of sets" means at least two sets, e.g., two sets, three sets, etc., unless specifically defined otherwise.
In the present application, unless explicitly specified and limited otherwise, the terms "coupled," "secured," and the like are to be construed broadly, and for example, "secured" may be either permanently attached or removably attached, or integrally formed; the device can be mechanically connected, electrically connected, physically connected or wirelessly connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
In addition, the technical solutions of the embodiments of the present application may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, the combination of the technical solutions should be considered to be absent, and is not within the scope of protection claimed in the present application.
The method provided by the application can be applied to an application scene graph of the unmanned aerial vehicle path planning method based on the PG algorithm shown in fig. 1. The terminal 102 communicates with the server 104 through a network, where the terminal 102 may include, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be various portal sites, servers corresponding to a background of a working system, and the like.
The application provides a path planning method of an unmanned aerial vehicle based on a PG algorithm, as shown in fig. 2, in an embodiment, the method is applied to a terminal in fig. 1 for illustration, and comprises the following steps:
step 202, three-dimensional map information, unmanned plane parameter information and dynamic obstacle information are obtained, and a starting point and a target point of path planning are set.
In this step, initializing information and parameters, wherein the three-dimensional map information comprises map boundaries, the unmanned plane parameter information comprises unmanned plane speed and flying height, and the dynamic obstacle information comprises the number, position, size and speed of dynamic obstacles.
Specifically: importing three-dimensional coordinate information of mountain nodesMap boundary is imported->Setting map height +.>Setting obstacle information including speed +.>Radius->Center coordinates->Initializing the starting point +.>And target point->Initializing a path point array as a tuple, setting a starting point as a current point of the unmanned aerial vehicle, setting an algorithm growth step length, and determining the step length according to the required path precision.
And 204, calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat sphere model of each dynamic obstacle.
Specifically, the threat ball model includes:
；
in the method, in the process of the invention,for dynamic uncertainty, is the maximum dynamic uncertainty of a dynamic obstacle +.>Is a unit time->Dynamic obstacle speed detected for unmanned aerial vehicle, < >>For dynamic obstacle radius +.>Radius of the threat ball model;
unit time ofDistance of movement of inner dynamic obstacle and dynamic obstacle radius +.>There is a proportional relationship with dynamic obstacle radius +.>And speed->Calculating dynamic uncertainty +.>Then with dynamic barrier centre +>As a sphere center, with dynamic uncertainty +.>Expansion barrierRadius, a radius is determined as +.>Is used to construct a threat sphere model.
In the step, the dynamic uncertainty of the dynamic obstacle is determined according to the speed, the size (known quantity, namely dynamic obstacle information) and the like of the dynamic obstacle, a threat ball model is established in combination with the speed of the unmanned aerial vehicle, and the dynamic uncertainty processing is carried out on the obstacle in the map.
And 206, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model.
Specifically, the obstacle avoidance range cone model includes:
；
in the method, in the process of the invention,for the adaptive safety distance between the unmanned aerial vehicle and the dynamic obstacle, < > a->Is the speed of the unmanned aerial vehicle;
determining unmanned aerial vehicle speedObstacle speed->Calculating self-adaptive safe distance between unmanned aerial vehicle and obstacle，/>The numerical settings of (2) are related to the speed of the unmanned aerial vehicle, the obstacle and the threat ball radius, +.>The reaction space is reserved for the unmanned aerial vehicle to avoid the obstacle. Then, let unmanned plane position be +.>I.e. regarding the drone as particle->In->Unmanned plane connected on axis>And threat ball model, and->Point tangential line->And->Tangent to the threat sphere model, and constructing an obstacle avoidance range cone model.
In the step, an obstacle avoidance range cone model is established on the basis of an initial three-dimensional space obstacle avoidance range.
Step 208, according to the three-dimensional map information, combining the path length, mobility cost, threat level and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning.
Specifically:
according to the three-dimensional map information, a path length cost function is built by combining the path length of the unmanned aerial vehicle, a mobility cost function is built by combining the mobility cost of the unmanned aerial vehicle, a threat degree cost function is built by combining the threat degree of the unmanned aerial vehicle, and a flying height cost function is built by combining the flying height of the unmanned aerial vehicle;
and establishing a multi-objective optimization function of unmanned plane path planning according to the path length cost function, the maneuverability cost function, the threat degree cost function and/or the flying height cost function.
More specifically:
the method comprises the steps of establishing a path length cost function by combining the path length of the unmanned aerial vehicle, enabling the flight path of the unmanned aerial vehicle to be in a map boundary, and connecting a starting point and a target point, and specifically: when avoiding the obstacle, the path length cost should be prioritized, and the unmanned plane flight path is consideredExpressed as a need for flight through +.>List of individual waypoints, each corresponding to a coordinate in the environment +.>By representing the Euclidean distance between two nodes as +.>Cost associated with path length +.>The method can be calculated to obtain:
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in the method, in the process of the invention,for unmanned plane path->Path length cost function of>For the number of waypoints the unmanned path passes, +.>For each waypoint pairShould be the coordinates of the nodes in the environment.
The mobility cost function is established in combination with the mobility cost of the unmanned aerial vehicle, in particular:
；
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in the method, in the process of the invention,for unmanned plane path->Mobility cost function of>For the corner cost between every two adjacent nodes in the unmanned plane path, +.>For the cost factor->For the rotation angle amplitude of the current node and the next node in the unmanned plane path, +.>The maximum turning angle of the unmanned aerial vehicle.
The threat level cost function is established in combination with the threat level of the unmanned aerial vehicle, specifically: is provided withFor the set of all obstacles, the threat sphere model is taken as a reference obstacle, in +.>For the width of the early warning area, the early warning area represents the area where the navigation point is close to the threat ball and is about to collide, and the pre-warning area is a part of the area where the threat ball is about to collide>The threat degree cost and the linear distance from the unmanned aerial vehicle to the center of the dynamic obstacle are determined by the size of the unmanned aerial vehicle and the speed of the unmanned aerial vehicle for acquiring the obstacle information>Proportional, introduce->As penalty factor, the safety distance cost function +.>I.e. threat level cost functions can be obtained:
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in the method, in the process of the invention,for unmanned plane path->Is a threat level cost function of (a),Qfor the total number of dynamic barriers +.>For threat level cost factor, < >>Threat level costs for forming a road segment for a single threat to two neighboring nodes, +.>For threatening the radius of the sphere model +.>For the width of the early warning area, & lt & gt>To unmanned planeqStraight line distance of the center of each dynamic barrier.
Bonding nothingThe flying height of the man-machine establishes a flying height cost function, in particular: the unmanned aerial vehicle flight altitude is typically limited between two given extremes, for ranges outside of the flight altitudeThe cost is infinite, and the navigation point is +.>Associated high cost->And summing all waypoints gives a high cost function +.>The method can obtain the following steps:
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in the method, in the process of the invention,for unmanned plane path->Is a flying height cost function of->For waypoints->Height costs arising beyond the desired flight height range, +.>The unit cost for the deviation in the flying height range is a constant,/for>Is relative to unmanned aerial vehicleFlying height on the ground, +.>For maximum of the flight height range, +.>Is the minimum of the range of flying heights.
In this step, by taking into account the and pathThe associated path length, mobility, threat level and flying height related constraints are summed up to obtain a multi-objective optimization function:
。
and 210, solving the multi-objective optimization function by taking the threat sphere model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point.
In the step, taking obstacle avoidance range cones of a threat ball model and an obstacle avoidance range cone model as reference ranges of path finding of a PG algorithm, performing iterative calculation in the ranges, generating paths by utilizing the PG algorithm, and obtaining global paths reaching target points; and calculating the path cost of the global path generated by each iteration by utilizing a multi-objective optimization function, updating the path with the optimal reserved cost, obtaining the global optimal path after the iteration calculation of the upper limit times, and ending the algorithm.
In the embodiment, a three-dimensional map environment is established, and information and parameters of path planning are initialized; a threat ball model and an obstacle avoidance range cone model are constructed (as shown in fig. 3, wherein B is a threat ball,to threaten the centre of the ball, also the centre of the dynamic barrier,>to threaten the radius of the ball +.>Is Unmanned Aerial Vehicle (UAV)>For the adaptive safety distance between the unmanned aerial vehicle and the obstacle +.>And->For->A tangent to the point tangent to the threat sphere model); establishing a multi-objective optimization function by combining the path length, mobility cost, threat level and flying height of the unmanned aerial vehicle; and (3) improving the path searching rule of the original PG algorithm by adopting a multi-objective optimization function, and searching paths by adopting the improved PG algorithm under the guidance of the multi-objective optimization function, wherein the obtained solution is the optimal path of the planned unmanned aerial vehicle to the target point.
The traditional PG algorithm is a random way-finding, so that the path nodes generated by the algorithm cover the whole map area along with the rising of the iteration times: on one hand, the algorithm can find a path after more iterations, so that the convergence rate is too low; on the other hand, due to random sampling, the obtained path ideality is lower.
According to the unmanned aerial vehicle path planning method based on the PG algorithm, the PG algorithm which is only applicable to global static planning is improved, three-dimensional map information is initialized, and path planning environment information is determined; constructing a threat ball model, and processing the obstacle into a static obstacle by calculating dynamic uncertainty of the obstacle by using the information such as speed vectors, sizes and the like of the obstacle and the unmanned aerial vehicle; introducing self-adaptive safety distance between the unmanned aerial vehicle and the threat ball, ensuring the safety distance of obstacle avoidance of the unmanned aerial vehicle, and constructing an obstacle avoidance range cone model; establishing a multi-objective optimization function according to conditions related to path length, flight height, threat level and mobility cost; and using a PG algorithm to find out the optimal path to reach the target point under the constraint of the multi-target optimization function. Aiming at the problem that the operation efficiency of the current PG algorithm is lower in a complex obstacle environment, the method and the device realize the processing function of the PG algorithm on dynamic obstacles by using a threat sphere model, and improve the actual value of the PG algorithm; the reference range of path finding is limited for the PG algorithm by utilizing the obstacle avoidance range cone model, blind path finding is avoided, so that the path points can be obtained more quickly by random sampling of the PG algorithm, and the algorithm convergence rate is improved; the path planned by the PG algorithm is optimized by utilizing the multi-objective optimization function, so that the blindness problem of the original PG algorithm on path planning is solved, the planned path cost is reduced, and the more ideal unmanned aerial vehicle flight path is obtained. The method converts the dynamic problem into the static problem, improves the processing capacity of the PG algorithm on dynamic obstacle, avoids the blindness of the PG algorithm in path finding under the constraint of the multi-objective optimization function, greatly reduces the path planning cost of the unmanned aerial vehicle, and greatly improves the path planning effect of the PG algorithm.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
The application further provides an unmanned aerial vehicle path planning device based on a PG algorithm, as shown in fig. 4, in one embodiment, the unmanned aerial vehicle path planning device includes: an acquisition module 402, a first modeling module 404, a second modeling module 406, a function module 408, and a solution module 410, wherein:
the acquisition module 402 is configured to acquire three-dimensional map information, unmanned aerial vehicle parameter information, and dynamic obstacle information, and set a starting point and a target point of path planning;
a first modeling module 404, configured to calculate a dynamic uncertainty according to the dynamic obstacle information, and build a threat sphere model of each dynamic obstacle;
the second modeling module 406 is configured to calculate an adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, connect the unmanned aerial vehicle and the threat ball model, and establish an obstacle avoidance range cone model;
the function module 408 is configured to establish a multi-objective optimization function of unmanned aerial vehicle path planning according to the three-dimensional map information in combination with the path length, mobility cost, threat level and/or flight height of the unmanned aerial vehicle;
the solving module 410 is configured to solve the multi-objective optimization function with the threat sphere model and the obstacle avoidance range cone model as constraints of the PG algorithm, so as to obtain an optimal path from the starting point to the target point.
For specific limitations of the unmanned aerial vehicle path planning device based on the PG algorithm, reference may be made to the above limitations of the unmanned aerial vehicle path planning method based on the PG algorithm, which are not described herein. Each of the modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for unmanned aerial vehicle path planning based on the PG algorithm. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method of the above embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method of the above embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims (10)

1. The unmanned aerial vehicle path planning method based on the PG algorithm is characterized by comprising the following steps of:
acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information, and setting a starting point and a target point of path planning;
calculating dynamic uncertainty according to the dynamic obstacle information, and establishing a threat sphere model of each dynamic obstacle;
according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle, connecting the unmanned aerial vehicle and the threat ball model, and establishing an obstacle avoidance range cone model;
according to the three-dimensional map information, combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle, and establishing a multi-objective optimization function of unmanned aerial vehicle path planning;
and solving the multi-objective optimization function by taking the threat ball model and the obstacle avoidance range cone model as constraints of the PG algorithm to obtain an optimal path from the starting point to the target point.
2. The unmanned aerial vehicle path planning method based on the PG algorithm according to claim 1, wherein the multi-objective optimization function of unmanned aerial vehicle path planning is established according to three-dimensional map information by combining the path length, mobility cost, threat level and/or flight height of the unmanned aerial vehicle, and the method comprises the following steps:
according to the three-dimensional map information, a path length cost function is built by combining the path length of the unmanned aerial vehicle, a mobility cost function is built by combining the mobility cost of the unmanned aerial vehicle, a threat degree cost function is built by combining the threat degree of the unmanned aerial vehicle, and a flying height cost function is built by combining the flying height of the unmanned aerial vehicle;
and establishing a multi-objective optimization function of unmanned plane path planning according to the path length cost function, the maneuverability cost function, the threat degree cost function and/or the flying height cost function.
3. The unmanned aerial vehicle path planning method based on the PG algorithm according to claim 1 or 2, wherein the threat sphere model comprises:
；
in the method, in the process of the invention,for dynamic uncertainty, ++>Is a unit time->For dynamic obstacle speed, +.>In order to be a dynamic obstacle radius,radius of the threat ball model;
the obstacle avoidance range cone model comprises:
；
in the method, in the process of the invention,for the adaptive safety distance between the unmanned aerial vehicle and the dynamic obstacle, < > a->Is the speed of the unmanned aerial vehicle;
with unmanned aerial vehicle position asConnecting the unmanned plane with the threat ball model, and crossing +.>Point tangential line->And->Tangent to the threat sphere model, and constructing an obstacle avoidance range cone model.
4. A method for unmanned aerial vehicle path planning based on PG algorithm according to claim 3, wherein the creating of the multi-objective optimization function for unmanned aerial vehicle path planning in combination with the path length, mobility cost, threat level and/or flight altitude of the unmanned aerial vehicle comprises: establishing a path length cost function by combining the path length of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Path length cost function of>For the number of waypoints the unmanned path passes, +.>Each waypoint corresponds to a node coordinate in the environment.
5. A method for unmanned aerial vehicle path planning based on PG algorithm according to claim 3, wherein the creating of the multi-objective optimization function for unmanned aerial vehicle path planning in combination with the path length, mobility cost, threat level and/or flight altitude of the unmanned aerial vehicle comprises: establishing a mobility cost function by combining the mobility cost of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Mobility cost function of>For the number of waypoints that the drone path passes through,for the corner cost between every two adjacent nodes in the unmanned plane path, +.>For the cost factor->For the rotation angle amplitude of the current node and the next node in the unmanned plane path, +.>The maximum turning angle of the unmanned aerial vehicle.
6. A method for unmanned aerial vehicle path planning based on PG algorithm according to claim 3, wherein the creating of the multi-objective optimization function for unmanned aerial vehicle path planning in combination with the path length, mobility cost, threat level and/or flight altitude of the unmanned aerial vehicle comprises: establishing a threat degree cost function by combining the threat degree of the unmanned aerial vehicle:
；
in the method, in the process of the invention,for unmanned plane path->Threat level cost function of->Number of waypoints for unmanned aerial vehicle pathThe amount of the product is calculated,Qfor the total number of dynamic barriers +.>For threat level cost factor, < >>Threat level costs for forming a road segment for a single threat to two neighboring nodes, +.>For threatening the radius of the sphere model +.>For the width of the early warning area, & lt & gt>To unmanned planeqStraight line distance of the center of each dynamic barrier.
7. A method for unmanned aerial vehicle path planning based on PG algorithm according to claim 3, wherein the creating of the multi-objective optimization function for unmanned aerial vehicle path planning in combination with the path length, mobility cost, threat level and/or flight altitude of the unmanned aerial vehicle comprises: establishing a flight altitude cost function by combining the flight altitude of the unmanned aerial vehicle:
；
；
in the method, in the process of the invention,for unmanned plane path->Cost function of flying height of (2)Count (n)/(l)>For waypoints->Height costs arising beyond the desired flight height range, +.>For the number of waypoints the unmanned path passes, +.>Unit cost for deviation in flying height range, +.>For the flying height of the unmanned aerial vehicle relative to the ground, < ->For maximum of the flight height range, +.>Is the minimum of the range of flying heights.
8. Unmanned aerial vehicle path planning device based on PG algorithm, its characterized in that includes:
the acquisition module is used for acquiring three-dimensional map information, unmanned aerial vehicle parameter information and dynamic obstacle information and setting a starting point and a target point of path planning;
the first modeling module is used for calculating dynamic uncertainty according to the dynamic obstacle information and establishing a threat sphere model of each dynamic obstacle;
the second modeling module is used for calculating the self-adaptive safe distance between the unmanned aerial vehicle and the dynamic obstacle according to the unmanned aerial vehicle parameter information and the dynamic obstacle information, connecting the unmanned aerial vehicle with the threat ball model, and establishing an obstacle avoidance range cone model;
the function module is used for establishing a multi-objective optimization function of unmanned aerial vehicle path planning according to three-dimensional map information by combining the path length, mobility cost, threat degree and/or flight height of the unmanned aerial vehicle;
and the solving module is used for solving the multi-objective optimization function by taking the threat sphere model and the obstacle avoidance range cone model as the constraint of the PG algorithm to obtain an optimal path from the starting point to the target point.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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