Abstract
This paper studies the cooperation method of multi-cooperative Unmanned Surface Vehicles (USVs) for chemical pollution source monitoring in a dynamic water environment. Multiple USVs formed a mobile sensor network in a symmetrical or asymmetrical formation. Based on ‘Infotaxis’ algorithms for multi-USV, an improved shared probability is proposed for solving the problems of low success rate and low efficiency resulting from the cognitive differences of multi-USV in cooperative exploration. By introducing the confidence factor, the cognitive differences between USVs are coordinated. The success rate and the efficiency of exploration are improved. To further optimize the exploration strategy, the particle swarm optimization (PSO) algorithm is introduced into the ‘Infotaxis’ algorithm to plan the USVs’ exploration path. This method is called the ‘PSO-Infotaxis’ algorithm. The effectiveness of the proposed method is verified by simulation and laboratory experiments. A comparison of the test results shows that the ‘PSO-Infotaxis’ algorithm is superior with respect to exploring efficiency. It can reduce the uncertainty of the estimation for source location faster and has lower exploration time, which is most important for the exploration of a large range of water areas.
Highlights
In recent years, frequent sudden pollution accidents have seriously threatened the ecological environment of water
The matter released by the chemical source is diffused with the flow or wind field, forming the distribution of Suppose the chemical pollution source is located in an unknown position in the space
An improved shared probability updating method based on information confidence judgment is proposed to solve the cognitive difference problem of multiple Unmanned Surface Vehicles (USVs)
Summary
Frequent sudden pollution accidents have seriously threatened the ecological environment of water. When pollutants are discharged into water, a dynamic spatial and temporal pollution field is formed. When monitoring the water quality, identifying the source of the pollution in a timely and effective fashion is a key problem. Traditional monitoring methods have difficulty tracking and monitoring such dynamic pollution fields. USVs in autonomous detection are providing new solutions for water quality monitoring. There are still many issues to be studied, especially in water environments such as lakes. Because lake currents are not as directional as rivers, and do not have clear tidal characteristics like oceans, it is difficult to estimate the location of pollution sources quickly and accurately in cases of emergency monitoring with limited individual knowledge. The slow flow velocity and large turbulence, wind field and environmental noise cause the pollution fields to present discrete local extrema within a local range, meaning that the USV can produce incorrect assessments, affecting the detection efficiency
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