CN114638155A - Unmanned aerial vehicle task allocation and path planning method based on intelligent airport - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle task allocation and path planning method based on an intelligent airport, which comprehensively considers factors such as flight distance constraint, signal strength, task fairness, demand coverage and the like, and performs joint optimization on unmanned aerial vehicle task allocation and path planning problems in an urban management scene by using a two-stage multi-target planning model. Meanwhile, the invention also discloses a task allocation and path planning algorithm which aims at the two-stage multi-target planning model and combines a genetic algorithm and a simulated annealing algorithm.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle task allocation and path planning method based on an intelligent airport.
Background
In urban management, different task requirements such as modification of a dangerous house, sewage treatment, forest fire, sanitation and epidemic prevention and the like often appear in different places. The traditional mode of using manual work to manage not only occupies a large amount of manpower resources, consumes time and labor, but also often has the phenomena of supervision dead angle and supervision delay. The unmanned aerial vehicle carrying various intelligent devices and the intelligent airport with the unattended operation function can realize the targets of flexible deployment, unattended operation, 360-degree dead-angle-free monitoring, remote control, timely response and the like of city management.
At present, some researches on unmanned aerial vehicle intelligent airport site selection exist, but the researches generally do not comprehensively consider the total flight time, the total signal intensity, the task amount fairness among intelligent airports, the task coverage and the like of the unmanned aerial vehicle, and do not uniformly optimize the city management task allocation and the path planning.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle city management task allocation and path planning method based on an intelligent airport, which realizes the optimal balance among targets with different priorities, such as the total flight time, the total signal intensity, the task quantity fairness among the intelligent airports, the task coverage rate and the like of the unmanned aerial vehicle by optimizing the flight path of the unmanned aerial vehicle and the allocation of task requirements among different intelligent airports.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in the method for determining the 'serviceable task set' of the intelligent airport, the priority of the highest demand coverage rate is higher than that of other targets in a plurality of targets with the minimum total flight distance, the maximum total average signal intensity, the highest demand coverage rate and the like, so that the 'serviceable task set' of each intelligent airport is determined by taking the highest demand coverage rate as a target. When determining the "serviceable set of tasks", the locations of all task requirements and the minimum required signal strengths are known and the address of the intelligent airport is already given. Unmanned aerial vehicle intelligence airport is for having unmanned aerial vehicle accomodate, intelligence to wait to watch, the automatic ground equipment that modules such as change unmanned aerial vehicle battery, radio communication, battery automatic maintenance, UPS power-off protection, trouble self-checking, take-off condition detection constitute. The method comprises the following steps:
(1.1) calculating the distance between the intelligent airport and the task site:
in the formula (1), the reaction mixture is,
representing the distance between the intelligent airport i and the task j, wherein i ∈ Ψ, j ∈ Φ, Ψ is the set of all intelligent airports, Φ is the set of all task requirements, (x)i,yi) Is the two-dimensional coordinate of the intelligent airport i, (x)j,yj) Is the two-dimensional coordinate of task location j;
(1.2) calculating the shortest flight time required by the unmanned aerial vehicle when the intelligent airport i serves the task j:
in the formula (2), Ti jRepresents the minimum flight time required for the drone, where v is the flight speed of the drone, TjIs the service time of task j;
(1.3), determining a 'serviceable task set' of the intelligent airport i:
Φi＝{j∈Φ|Ti jnot more than T-epsilon and i jRSRP≥RSRP}(3)，
in the formula (3), phiiRepresenting the "serviceable set" of intelligent airports i, where T is the maximum flight time of the drone, epsilon is the amount of redundancy,RSRPrepresenting the minimum signal strength required for the drone to perform the task, i jRSRPrepresenting the lowest signal strength on the flight path between the intelligent airport i and the task j;
(1.4), determining the total serviceable task set of the whole intelligent airports:
in the formula (4), the reaction mixture is,
intelligent machine capable of showing wholeThe "total serviceable set of tasks" for the farm.
Step (2), a method for planning the path of an unmanned aerial vehicle based on the method for determining a "serviceable task set" according to claim 1, wherein for certain tasks belonging to the "serviceable task set", the unmanned aerial vehicles at the intelligent airport i can be sequentially serviced in a single flight. And planning the path by taking the shortest total path length as a target, and solving the total average signal strength under the optimal path. The method comprises the following steps:
(2.1) calculating Euclidean distances among different tasks:
in the formula (5), the reaction mixture is,
representing task j1And task j2Is of the Euclidean distance between, wherein
Is task j1Is determined by the two-dimensional coordinates of (a),
is task j2Two-dimensional coordinates of (a);
(2.2) calculating the correction distance between different tasks:
in the formula (6), the reaction mixture is,
representing task j1And task j2A corrected distance therebetween, wherein
Representing task j1And task j2Flight route betweenThe lowest signal strength of (c);
(2.3), for any subset of its "serviceable set of tasks", defining a solution space of the flight path of the drone of the intelligent airport i:
in the formula (7), the reaction mixture is,
a solution space representing the flight path of the drone relative to the set of tasks A, the intelligent airport i, where
| A | represents the number of elements of the set A;
(2.4) setting an objective function with the shortest total flight path length:
in the formula (8), the reaction mixture is,
representing the total flight path length at flight path z;
(2.5), in combination with formula (7) and formula (8), written as follows:
(2.6) the path planning problem belongs to a traveler problem and is an NP difficult problem in combination optimization. The invention uses a simulated annealing algorithm to solve. The framework of the algorithm is as follows: 1) randomly generating a path as an initial solution z0(ii) a 2) Given a sufficiently large initial temperature Q0(ii) a 3) Giving an iteration number K; 4) n is given; 5) for K ═ 1, …, K does 6) to 9); 6) generating a new solution z' by using a two-transformation method; 7) computing pre-transformation solutions and transformationsDifference of transformed objective function:
8) if it is
Then accept z' as the new current solution, otherwise with probability
Accepting z' as a new current solution; 9) if the N continuous new solutions are not accepted, outputting the current solution as the optimal solution, and ending; 10) q decreases and goes to 5).
And
respectively representing the optimal path solved according to the algorithm and an objective function value under the optimal path;
(2.7) calculating the average signal strength under the optimal path:
in the formula (10), Wi A*Represents the total average signal strength under the optimal path, wherein
Representing task j1And task j2Average reference signal received power, RSRP, betweeni jRepresenting the average reference signal received power between the intelligent airport i and task j.
Step (3), a task allocation method based on the unmanned aerial vehicle path planning method of claim 2. The method comprises the following steps:
(3.1) calculating a power set of the total serviceable task set:
in the formula (11), the reaction mixture is,
representing a total set of serviceable tasks "
To a power of a set of
Representation collection
The number of elements (c);
(3.2) defining operations for 0-1 variable x and arbitrary set B
In the formula (12), the reaction mixture is,
representing an empty set;
(3.3) assigning decision variables to the tasks
The values of (a) are constrained:
in the formula (13), when
Time represents set BjIn a single flight from an intelligent airport iIn-line service when
Time represents set BjIs not served by the intelligent airport i in a single flight, wherein
(3.4) based on the "serviceable set of tasks" in claim 1, constraints are imposed on the task allocation:
(3.5) based on the "total serviceable task set" in claim 1, constraints are imposed on the task allocation:
(3.6) constraint on total time of single flight:
(3.7) calculating the total flight time, the total signal intensity and the task quantity difference among the intelligent airports of all the unmanned aerial vehicles, and setting an objective function according to the minimum weighting function:
in the formula (17), the compound represented by the formula (I),
SD (. beta.) represents the standard deviation, beta, of a set of data1、β2And beta3The relative importance of the difference of the total flight time, the total signal intensity and the task amount among the intelligent airports of all the unmanned aerial vehicles is representedA weight;
(3.8), summarizing formula (13) -formula (17), writing the following form:
and (3.9) combining the optimization problem with the path planning represented by the formula (9), belonging to a two-stage 0-1 planning problem. The method uses a genetic algorithm to solve, namely M feasible solutions of a random generation formula (18) are used as an initial population; selecting individuals in the current population by using an exponential sorting selection method for copying; randomly pairing individuals generated by the selection-duplication operation; performing mutation operation with a certain mutation probability for each individual generated by the crossover operation; and iterating until a termination condition is met.
The invention aims to provide an unmanned aerial vehicle city management task allocation and path planning method based on an intelligent airport.
The invention provides a path planning and task allocation algorithm which aims at the two-stage random multi-target planning model and combines a genetic algorithm and a simulated annealing algorithm.
Drawings
Fig. 1 is a flow chart of the task allocation and path planning of unmanned aerial vehicles based on intelligent airports.
FIG. 2 is a schematic diagram of a "serviceable set of tasks" for a smart airport.
Fig. 3 is a schematic diagram of unmanned aerial vehicle path planning for a smart airport.
Fig. 4 is a schematic view of flight path and mission demand allocation for a drone.
Claims (3)
1. The method for determining the 'serviceable task set' of the intelligent airport comprises the following steps that in a plurality of targets such as minimum total flight distance, maximum total average signal intensity, maximum required coverage rate and the like, the highest priority of the required coverage rate is higher than that of other targets, so that the maximum required coverage rate is firstly used as the target, the 'serviceable task set' of each intelligent airport is determined, when the 'serviceable task set' is determined, the places and the required minimum signal intensities of all task demands are known, the address of the intelligent airport is given, and the ground equipment formed by modules such as unmanned aerial vehicle storage, intelligent standby, automatic replacement of an unmanned aerial vehicle battery, wireless communication, automatic battery maintenance, UPS power-off protection, fault self-checking, takeoff condition detection and the like comprises the following steps:
(1.1) calculating the distance between the intelligent airport and the task site:
in the formula (1), the reaction mixture is,
representing the distance between the intelligent airport i and the task j, wherein i ∈ Ψ, j ∈ Φ, Ψ is the set of all intelligent airports, Φ is the set of all task requirements, (x)i,yi) Is the two-dimensional coordinate of the intelligent airport i, (x)j,yj) Is the two-dimensional coordinate of task location j;
(1.2) calculating the shortest flight time required by the unmanned aerial vehicle when the intelligent airport i serves the task j:
in the formula (2), Ti jRepresents the minimum flight time required for the drone, where v is the flight speed of the drone, TjIs the service time of task j;
(1.3), determining a 'serviceable task set' of the intelligent airport i:
in the formula (3), phiiRepresenting the "serviceable set" of intelligent airports i, where T is the maximum flight time of the drone, epsilon is the amount of redundancy,RSRPrepresenting the minimum signal strength required by the drone to perform the task, i jRSRPrepresenting the lowest signal strength on the flight path between the intelligent airport i and the task j;
(1.4), determining the total serviceable task set of the whole intelligent airports:
in the formula (4), the reaction mixture is,
representing the "total serviceable set of tasks" for the totality of intelligent airports.
2. An unmanned aerial vehicle path planning method based on the 'serviceable task set' determination method in claim 1, wherein for some tasks belonging to the 'serviceable task set', unmanned aerial vehicles at an intelligent airport i can be sequentially served in a single flight, path planning is performed with the shortest total path length as a target, and the total average signal strength under the optimal path is obtained, and the method comprises the following processes:
(2.1) calculating Euclidean distances among different tasks:
in the formula (5), the reaction mixture is,
representing task j1And task j2Of a Euclidean distance therebetween, wherein
Is task j1Is determined by the two-dimensional coordinates of (a),
is task j2Two-dimensional coordinates of (a);
(2.2) calculating the correction distance between different tasks:
in the formula (6), the reaction mixture is,
representing task j1And task j2A corrected distance therebetween, wherein
Representing task j1And task j2The lowest signal strength on the flight path in between;
(2.3) for any subset of its "serviceable set of tasks", defining a solution space for the flight path of the drone of the intelligent airport i:
in the formula (7), the reaction mixture is,
a solution space representing the flight path of the drone relative to the set of tasks A, where
| A | represents the number of elements of the set A;
(2.4) setting an objective function with the shortest total flight path length:
in the formula (8), the reaction mixture is,
representing the total flight path length at flight path z;
(2.5), in combination with formula (7) and formula (8), written as follows:
(2.6) the path planning problem belongs to a traveler problem and is an NP (network performance) difficult problem in combination optimization, the method is used for solving by using a simulated annealing algorithm, and the framework of the algorithm is as follows: 1) randomly generating a path as an initial solution z0(ii) a 2) Given a sufficiently large initial temperature Q0(ii) a 3) Giving an iteration number K; 4) n is given; 5) for K ═ 1, …, K does 6) to 9); 6) generating a new solution z' by using a two-transformation method; 7) calculating the difference between the solution before transformation and the objective function after transformation:
8) if it is
Then accept z' as the new current solution, otherwise with probability
Accepting z' as a new current solution; 9) if the N continuous new solutions are not accepted, outputting the current solution as the optimal solution, and ending; 10) q decreases and goes to 5);
and
respectively representing the optimal path solved according to the algorithm and an objective function value under the optimal path;
(2.7) calculating the average signal strength under the optimal path:
in the formula (10), Wi A*Represents the total average signal strength under the optimal path, wherein
Representing task j1And task j2Average reference signal received power, RSRP, betweeni jRepresenting the average reference signal received power between the intelligent airport i and task j.
3. A task allocation method based on the unmanned aerial vehicle path planning method of claim 2, comprising the following processes:
(3.1) calculating a power set of the total serviceable task set:
in the formula (11), the reaction mixture is,
representing a total set of serviceable tasks "
Set of powers of (1), wherein
Representation collection
The number of elements (c);
(3.2) defining operations for 0-1 variable x and arbitrary set B
In the formula (12), the reaction mixture is,
representing an empty set;
(3.3) assigning decision variables to the tasks
The values of (a) are constrained:
in the formula (13), when
Time represents set BjIs served by the intelligent airport i in a single flight when
Time represents set BjIs not serviced by intelligent airport i in a single flight, wherein
(3.4) based on the "serviceable set of tasks" in claim 1, constraints are imposed on the task allocation:
(3.5) based on the "total serviceable set of tasks" in claim 1, constraints are placed on task allocation:
(3.6) constraint on total time of single flight:
(3.7) calculating the total flight time, the total signal intensity and the task quantity difference among the intelligent airports of all the unmanned aerial vehicles, and setting an objective function according to the minimum weighting function:
in the formula (17), the compound represented by the formula (I),
SD (. beta.) represents the standard deviation, beta, of a set of data1、β2And beta3The weight represents the relative importance of the total flight time, the total signal intensity and the task quantity difference between the intelligent airports of all the unmanned aerial vehicles;
(3.8), summarizing formula (13) -formula (17), writing the following form:
(3.9) combining the optimization problem with the path planning represented by the formula (9), belonging to a two-stage 0-1 planning problem, solving by using a genetic algorithm, namely randomly generating M feasible solutions of the formula (18) as an initial population; selecting individuals in the current population by using an exponential sorting selection method for copying; randomly pairing individuals generated by the selection-duplication operation; performing mutation operation with a certain mutation probability for each individual generated by the crossover operation; and iterating until a termination condition is met.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115373424A (en) * | 2022-09-20 | 2022-11-22 | 广东翼景信息科技有限公司 | Time window-oriented multi-unmanned aerial vehicle airport site selection and random task scheduling method |
| CN115860292A (en) * | 2022-11-21 | 2023-03-28 | 武汉坤达安信息安全技术有限公司 | Fishing administration monitoring-based optimal path planning method and device for unmanned aerial vehicle |
| CN116859990A (en) * | 2023-06-26 | 2023-10-10 | 北京锐士装备科技有限公司 | Unmanned aerial vehicle flight management method and system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115373424A (en) * | 2022-09-20 | 2022-11-22 | 广东翼景信息科技有限公司 | Time window-oriented multi-unmanned aerial vehicle airport site selection and random task scheduling method |
| CN115373424B (en) * | 2022-09-20 | 2024-02-09 | 广东翼景信息科技有限公司 | Time window-oriented multi-unmanned aerial vehicle airport site selection and random task scheduling method |
| CN115860292A (en) * | 2022-11-21 | 2023-03-28 | 武汉坤达安信息安全技术有限公司 | Fishing administration monitoring-based optimal path planning method and device for unmanned aerial vehicle |
| CN115860292B (en) * | 2022-11-21 | 2023-08-04 | 武汉坤达安信息安全技术有限公司 | Unmanned aerial vehicle optimal planning path method and device based on fishery monitoring |
| CN116859990A (en) * | 2023-06-26 | 2023-10-10 | 北京锐士装备科技有限公司 | Unmanned aerial vehicle flight management method and system |
| CN116859990B (en) * | 2023-06-26 | 2024-04-19 | 北京锐士装备科技有限公司 | Unmanned aerial vehicle flight management method and system |
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