A Multi-UAV Collaborative Pollutant Traceability Method Based on Improved Simulated Annealing

Abstract

The invention discloses a multi-unmanned aerial vehicle collaborative pollutant tracing method based on an improved simulated annealing algorithm, which includes setting a suspected pollution source leakage area by using an artificial smell method, and dividing the suspected pollution source leakage area into multiple sub-areas according to the number of unmanned aerial vehicles. Each UAV transmits information to the PC-side ground center through the wireless transmission module for information exchange. The PC-side ground center continuously updates the position of each UAV based on the improved simulated annealing algorithm, and sends the new position information to each UAV. When each drone keeps wandering at a certain position, forming a circle with a radius of 1m, and the concentration of each drone's gas sensor is higher than a certain threshold, it is determined that the pollution source has been found. The algorithm of the present invention is further simulated under the artificially constructed Gaussian plume concentration field, and the simulation results verify that the algorithm of the present invention has high feasibility, accuracy and traceability efficiency in the field of pollutant traceability research.

Description

technical field [0001] The invention relates to a multi-unmanned aerial vehicle collaborative pollutant tracing method, which belongs to the field of multi-rotor unmanned aerial vehicles and atmospheric environment monitoring. Background technique [0002] Pollutants mainly refer to gaseous substances that are discharged into the atmosphere during human activities or natural processes and have harmful effects on people and the environment. In recent years, with the rapid development of industrialization, governments at all levels have put forward higher requirements for emergency response and emergency disposal capabilities in response to the severe situation of air pollution, accurately and quickly determine the location and impact of pollution sources, and achieve the goal of air pollution. Governance and development of effective emergency response plans are of great significance. [0003] Traditional pollution source location is generally based on fixed monitoring statio...

AI Technical Summary

Problems solved by technology

However, the positioning methods of fixed monitoring stations and vehicle-mounted monitoring stations are slow and costly; the grid automatic detection method covers a small area and the positioning accuracy is not high

In recent years, many scholars have tried to use single or multiple ground robots to search for odor pollution sources. However, due to the influence of the complex environment on the ground, the positioning speed of robots is slow and the coverage area is small, which is currently limited to the experimental stage.

There are also scholars who install gas sensors in each direction of a single UAV, and use the concentration gradient algorithm to locate the pollution source. However, due to the air being stirred by the propeller of the rotor UAV, there are large errors in the sensor detection data in all directions, and it is easy to fall into a local optimum. Excellent, and a single UAV takes a long time and has poor robustness

Images