CN111751502B - Multi-unmanned-aerial-vehicle cooperative pollutant tracing method based on improved simulated annealing - Google Patents
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Abstract
The invention discloses a multi-unmanned-aerial-vehicle cooperative pollutant tracing method based on an improved simulated annealing algorithm. Each unmanned aerial vehicle transmits information to the PC terminal ground center through the wireless transmission module, so that information interaction is carried out. The PC terminal ground center continuously updates the positions of all unmanned aerial vehicles based on an improved simulated annealing algorithm, and sends new position information to all unmanned aerial vehicles. When each unmanned aerial vehicle wanders ceaselessly at a certain position to form a circle with the radius of 1m, and the concentration of each unmanned aerial vehicle gas sensor is higher than a certain threshold value, the unmanned aerial vehicle gas sensor judges that a pollution source is found. Further, the algorithm is subjected to a simulation experiment under a manually-built Gaussian plume concentration field, and the simulation result verifies that the algorithm has higher feasibility, accuracy and traceability efficiency in the field of pollutant traceability research.
Description
Technical Field
The invention relates to a multi-unmanned-aerial-vehicle cooperative pollutant tracing method, and belongs to the field of multi-rotor unmanned aerial vehicles and atmospheric environment monitoring.
Background
Pollutants are primarily gaseous substances that are emitted into the atmosphere during human activities or natural processes and have a harmful effect on humans and the environment. Along with the rapid development of industrialization in recent years, governments at all levels put forward the requirements of higher emergency response and emergency disposal capability aiming at the severe situation of atmospheric pollution, accurately and rapidly determine the position and the influence of a pollution source, and have important significance for realizing the targeted treatment of the atmospheric pollution and establishing an effective emergency disposal scheme.
Traditional pollution source location is generally based on fixed monitoring stations, vehicle-mounted monitoring stations and wireless sensor networks, and the location of the pollution source is estimated by combining the location and pollutant concentration information. However, the positioning method of the fixed monitoring station and the vehicle-mounted monitoring station is slow and high in cost; the grid automatic detection method has small coverage area and low positioning accuracy. In recent years, many scholars try to search odor pollution sources by using single or multiple ground robots, however, the robots are influenced by the complex ground environment, the positioning speed is slow, the coverage area is small, and the robots are limited to the experimental stage at present. Also the scholars install gas sensor respectively in single unmanned aerial vehicle all directions, adopt concentration gradient algorithm to carry out the pollution source location, nevertheless there is very big error in the sensor detection data that leads to all directions by the reason that rotor unmanned aerial vehicle screw mixxed air, get into local optimum easily, and single unmanned aerial vehicle spends long time, and the robustness is relatively poor.
Disclosure of Invention
The invention aims to provide a multi-unmanned aerial vehicle cooperative pollutant tracing method based on improved simulated annealing. The method has the advantages of high positioning precision, high speed and strong robustness, and can avoid falling into local optimum.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a multi-unmanned aerial vehicle cooperative pollutant tracing method based on an improved simulated annealing algorithm comprises the following steps: the system comprises a plurality of rotor unmanned aerial vehicles, a gas sensor module, a wireless transmission module, an obstacle avoidance module and a PC end ground center; the plurality of rotor unmanned aerial vehicles are used for searching the specific position information of the leakage pollution source; the gas sensor module detects a pollutant gas type and concentration; the wireless transmission module is used for realizing information interaction between a plurality of unmanned aerial vehicles and a ground center of a PC (personal computer) terminal; the obstacle avoidance module is used for avoiding obstacles in the flight process of the unmanned aerial vehicle; and the PC terminal ground center is used for receiving information such as concentration and position of each unmanned aerial vehicle and updating the position of the unmanned aerial vehicle. The method comprises the following steps:
step 1: the suspected pollution source leakage area is set by adopting an artificial olfaction method, and the suspected pollution source leakage area is generally set mainly in densely distributed areas such as petrochemical plants, garbage treatment plants, leather plants, sewage treatment plants and the like.
Step 2: and dividing the suspected pollution source into a plurality of sub-areas according to the number N of the unmanned aerial vehicles. The fan-shaped sub-region angles are:
an unmanned aerial vehicle is placed in each sub-area to form a particle for searching, and all unmanned aerial vehicles in the whole area form a particle group to carry out pollution source positioning through information interaction.
And 3, step 3: particle group X ═ X (X) composed of N unmanned aerial vehicles1，X2，X3...XN) Starting to take off from an initial position in a three-dimensional search space, and recording the initial position of each unmanned aerial vehicle as Xi＝(Xix，Xiy，Xiz) (i ═ 1, 2.., N) and read the current location concentration data f (X)i)。
And 4, step 4: executing an improved simulated annealing global search strategy, searching smoke plume by adopting the thought of a hill climbing algorithm by each unmanned aerial vehicle, taking the current position as the circle center, taking the detection range of the gas sensor as the radius, and reading concentration data f (X ') of any point in the detection range'i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X'i)＞f(Xi) Then Xi＝X′i，f(Xi)＝f(X′i) Otherwise Xi＝Xi，f(Xi)＝f(Xi) (ii) a The global search capability of the unmanned aerial vehicle is enhanced, and the global search efficiency of the unmanned aerial vehicle is improved.
And 5: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing global search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiAnd quickly searching for smoke plume.
Step 6: and the ground center of the PC terminal judges whether the unmanned aerial vehicle finds the smoke plume by calculating whether the concentration data value of the population unmanned aerial vehicle exceeds a threshold value, if so, the local search strategy of executing improved simulated annealing is entered, and otherwise, the global search strategy of executing improved simulated annealing is continuously executed.
And 7: after the latest position information of each unmanned aerial vehicle is updated through the step 4, the concentration data f (X) of each unmanned aerial vehicle at the current position is comparedi) And the position of the unmanned aerial vehicle storing the current global optimal solution concentration data is taken as the center, the detection range of the gas sensor is taken as the radius, any N point concentration data in the detection range are read and randomly and evenly distributed to N unmanned aerial vehicles, and the group of concentration data is recorded as f (X ″)i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X ″)i)＞f(Xi) Or f (X ″)i)＜f(Xi) And meets Metropolis criterion, then Xi＝X″i，f(Xi)＝f(X″i) Otherwise Xi＝Xi，f(Xi)＝f(Xi)。
And 8: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing local search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiGradually approaching the pollution source.
And step 9: judging whether the updated position is a malodor pollution source, if each unmanned aerial vehicle ceaselessly wanders at a certain position and is enclosed into a circular shape with the radius of about 1m, and the concentration of each unmanned aerial vehicle gas sensor is higher than a certain threshold value, judging that the pollution source is found, transmitting the position of the pollution source back to the PC-end ground center, and transmitting a return flight instruction to the unmanned aerial vehicle by the PC-end ground center; otherwise, continuing to execute the local search strategy of the improved simulated annealing in the step 7.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a schematic diagram of the division of the suspected contamination source leakage area and sub-area
FIG. 3 is a simulation route diagram of the algorithm of the present invention under the artificially constructed Gaussian plume concentration field
FIG. 4 is a polygonal line graph of experiment times and efficiency of the algorithm in the simulation process
FIG. 5 is a bar graph of the number of hits versus error rate for the inventive algorithm during simulation
FIG. 6 is a bar graph of the effective times and iteration times of the algorithm of the present invention during simulation
Detailed Description
As shown in fig. 1, a multi-drone collaborative pollutant tracing method based on improved simulated annealing specifically includes the following steps:
step 1: the suspected pollution source leakage area is set by adopting an artificial olfaction method, and the suspected pollution source leakage area is generally set mainly in densely distributed areas such as petrochemical plants, garbage treatment plants, leather plants, sewage treatment plants and the like.
Step 2: and dividing the suspected pollution source into a plurality of sub-areas according to the number N of the unmanned aerial vehicles. An unmanned aerial vehicle is placed in each sub-area to form a particle for searching, and all unmanned aerial vehicles in the whole area form a particle group to carry out pollution source positioning through information interaction.
The step 2: according to the number N of the unmanned aerial vehicles, the suspected pollution source is divided into a plurality of fan-shaped sub-areas, and the angles of the fan-shaped sub-areas are as follows:
and step 3: particle group X ═ X (X) composed of N unmanned aerial vehicles1,X2,X3…XN) Starting to take off from an initial position in a three-dimensional search space, and recording the initial position of each unmanned aerial vehicle as Xi＝(Xix,Xiy,Xiz) (i ═ 1,2, …, N) and read the current position density data f (X)i)。
And 4, step 4: executing an improved simulated annealing global search strategy, and reading concentration data f (X ') of any point in a detection range by taking the current position as the circle center and the detection range of the gas sensor as the radius of each unmanned aerial vehicle'i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X'i)＞f(Xi) Then Xi＝X′i，f(Xi)＝f(X′i) Otherwise Xi＝Xi，f(Xi)＝f(Xi)。
The step 4: and an improved simulated annealing global search strategy is executed, and each unmanned aerial vehicle adopts the idea of a hill-climbing algorithm to search smoke plumes, so that the global search capability of the unmanned aerial vehicles is enhanced, and the global search efficiency of the unmanned aerial vehicles is improved.
And 5: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing global search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiAnd quickly searching for smoke plume.
Step 6: the PC terminal ground center calculates whether the concentration data value of the unmanned aerial vehicles in the population exceeds a threshold epsilon, and if the current concentration data of each unmanned aerial vehicle is smaller than a certain threshold, the value is max (f (x)1)，f(x2)，...f(xN) If epsilon is less than epsilon, the PC end ground center sends continuous execution improvement to each unmanned aerial vehicleAnd (3) simulating an annealing global search strategy instruction, if the current concentration data of a certain unmanned aerial vehicle is greater than a set threshold epsilon, judging that smoke plume is found, and sending an instruction for executing an improved simulated annealing local search strategy to each unmanned aerial vehicle by the ground center of the PC terminal.
And 7: after the latest position information of each unmanned aerial vehicle is updated through the step 4, the concentration data f (X) of each unmanned aerial vehicle at the current position is comparedi) And the position of the unmanned aerial vehicle storing the current global optimal solution concentration data is taken as the center, the detection range of the gas sensor is taken as the radius, any N point concentration data in the detection range are read and randomly and evenly distributed to N unmanned aerial vehicles, and the group of concentration data is recorded as f (X ″)i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X ″)i)＞f(Xi) Or f (X ″)i)＜f(Xi) And meets Metropolis criterion, then Xi＝X″i，f(Xi)＝f(X″i) Otherwise Xi＝Xi，f(Xi)＝f(Xi)。
The step 7: the Metropolis criteria decision rule is as follows:
let Δ f (x)i)＝f(X″i)-f(Xi)，
If Δ f (x)i) If > 0, then Xi＝X″i，f(Xi)＝f(X″i)；
If Δ f (x)i) < 0 and c ═ random (0, 1) > P, then Xi＝Xi，f(Xi)＝f(Xi)；
If Δ f (x)i) < 0 and c ═ random (0, 1) < P, then Xi＝X″i，f(Xi)＝f(X″i)；
Where k is the number of iterations and T (k) is the temperature after k iterations.
And 8: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing local search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiGradually approaching the pollution source.
And step 9: judging whether the pollution source is successfully positioned or not, if so, transmitting the position of the pollution source back to the PC terminal ground center, and transmitting a return flight instruction to the unmanned aerial vehicle by the PC terminal ground center; otherwise, continuing to execute the improved simulated annealing local search strategy in the step 7.
The step 9: judging whether the pollution source is successfully positioned is represented by the fact that if the unmanned aerial vehicles wander at a certain position continuously, a circle with the radius of about 1m is formed, and the concentration of the gas sensor of each unmanned aerial vehicle is higher than a certain threshold value, the pollution source is judged to be found.
As shown in fig. 4, the fact that whether the simulation experiment is effective is judged to be that if the monitoring concentration of each unmanned aerial vehicle is higher than a certain threshold, the simulation is effective.
As shown in fig. 4, on the basis of counting 100 simulation experiments, the number of times that a pollution source can be successfully searched is 93, the simulation effective rate reaches 93%, and data shows that the algorithm of the present invention has high feasibility in the field of pollutant tracing research.
As shown in FIG. 5, the error rate is calculated as
Wherein
Error rate for ith drone, FiConcentration monitoring value F of the ith unmanned aerial vehicle in Gaussian plume concentration field built for people0And (4) setting up an actual concentration value of the pollution source in the Gaussian plume concentration field.
As shown in fig. 5, the probability of the traceability accuracy error range of each unmanned aerial vehicle is 0.871, 0.849, 0.892, 0.828 and 0.914 respectively in the range of 0-5%, and the probability of the traceability accuracy error range of each unmanned aerial vehicle is 0.968, 0.946, 0.957 and 0.968 respectively in the range of 0-10%, and data shows that the algorithm of the present invention has high accuracy in the field of researching the pollutant traceability.
As shown in fig. 6, the probability of the unmanned aerial vehicle population iteration frequency in the range of 0-20 is 0.763, and the probability of the unmanned aerial vehicle population iteration frequency in the range of 0-40 is 0.946, and data shows that the algorithm has high traceability efficiency in the field of pollutant traceability research.
The invention provides a multi-unmanned-aerial-vehicle cooperative pollutant tracing method based on improved simulated annealing, a simulation experiment is carried out under a manually-built Gaussian plume concentration field, and a simulation result verifies that the algorithm has higher feasibility, accuracy and tracing efficiency in the field of pollutant tracing research.
Claims (1)
1. A multi-unmanned aerial vehicle cooperative pollutant tracing method based on an improved simulated annealing algorithm is characterized by comprising the following steps:
step 1: setting a suspected pollution source leakage area by using an artificial olfaction method, wherein the suspected pollution source leakage area is mainly set in an area with dense distribution of a petrochemical plant, a garbage treatment plant, a leather plant and a sewage treatment plant;
step 2: according to the number N of the unmanned aerial vehicles, the suspected pollution source leakage area is divided into a plurality of sub-areas, one unmanned aerial vehicle is placed in each sub-area to form particle search, all unmanned aerial vehicles in the whole area form particle groups, pollution source positioning is carried out through information interaction, the suspected pollution source leakage area is divided into a plurality of fan-shaped sub-areas, and the fan-shaped sub-area angles are as follows: phi is 180/N;
and step 3: particle group X ═ X (X) composed of N unmanned aerial vehicles1，X2，X3...XN) Starting to take off from an initial position in a three-dimensional search space, and recording the initial position of each unmanned aerial vehicle as Xi＝(Xix，Xiy，Xiz) (i ═ 1, 2.., N) and read the current location concentration data f (X)i)；
And 4, step 4: executing an improved simulated annealing global search strategy, reading concentration data f (X ') of any point in a detection range by adopting the idea of a hill climbing algorithm and taking the current position as the circle center and the detection range of the gas sensor as the radius for each unmanned aerial vehicle'i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X'i)＞f(Xi) Then Xi＝X′i，f(Xi)＝f(X′i) Otherwise Xi＝Xi，f(Xi)＝f(Xi)；
And 5: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing global search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiRapidly searching for smoke plumes;
step 6: the PC terminal ground center calculates whether the concentration data value of the unmanned aerial vehicles in the population exceeds a threshold epsilon, and if the current concentration data of each unmanned aerial vehicle is smaller than a certain threshold, the value is max (f (x)1)，f(x2)，...f(xN) If the current concentration data of a certain unmanned plane is greater than the set threshold value epsilon, determining that smoke plume is found, and sending an instruction for executing an improved simulated annealing local search strategy to each unmanned plane by the PC-end ground center;
and 7: after the latest position information of each unmanned aerial vehicle is updated through the step 4, the concentration data f (X) of each unmanned aerial vehicle at the current position is comparedi) And the position of the unmanned aerial vehicle storing the current global optimal solution concentration data is taken as the center, the detection range of the gas sensor is taken as the radius, any N point concentration data in the detection range are read and randomly and evenly distributed to N unmanned aerial vehicles, and the group of concentration data is recorded as f (X ″)i) And the read data is transmitted back to the ground center of the PC terminal through the transmission module and is judged: if f (X ″)i)＞f(Xi) And meets Metropolis criterion, then Xi＝X″i，f(Xi)＝f(X″i) Otherwise Xi＝Xi，f(Xi)＝f(Xi)；
The MetroDolis criterion judgment rule is as follows:
let Δ f (x)i)＝f(X″i)-f(Xi)，
If Δ f (x)i) < 0, then Xi＝Xi，f(Xi)＝f(Xi)；
If Δ f (x)i) > 0 and c ═ random (0, 1) > P, then Xi＝Xi，f(Xi)＝f(Xi)；
If Δ f (x)i) > 0 and c ═ random (0, 1) < P, then Xi＝X″i，f(Xi)＝f(X″i)；
Wherein k is the number of iterations, and T (k) is the temperature after k iterations;
and 8: continuously updating position X of each unmanned aerial vehicle based on improved simulated annealing local search strategyiThe unmanned aerial vehicle receives the instruction and flies to the updated position XiGradually approaching the pollution source;
and step 9: judging whether the pollution source is successfully positioned or not, if so, transmitting the position of the pollution source back to the PC terminal ground center, and transmitting a return flight instruction to the unmanned aerial vehicle by the PC terminal ground center; otherwise, continuing to execute the improved simulated annealing local search strategy in the step 7;
wherein, the judgment criterion for successfully positioning the pollution source is as follows: if each unmanned aerial vehicle wanders continuously at a certain position to form a circle with the radius of about 1m, and the concentration of each unmanned aerial vehicle gas sensor is higher than a certain threshold value, the unmanned aerial vehicle gas sensor judges that a pollution source is found.
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| 机器人主动嗅觉烟羽分布辨识方法研究;黄建新 等;《计算机仿真》;20190930;第36卷(第9期);第346-351页 * |
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