# Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs

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## Abstract

**:**

## 1. Introduction

- Definition of the target area. Since the weather conditions in maritime environments are usually inaccurate or unknown and the air and ocean currents can drift the debris away from their last known position, it is necessary to establish a method to define the search target area.
- Design of a search path. Even if the target area is correctly defined, there is no optimal solution for the design of the search trajectory problem. In this work, the goal is to compute paths along the whole searching area that cover the points with the higher probability of containment as fast as possible.

## 2. Materials and Methods

#### 2.1. Target Position Estimation

#### 2.2. Coverage Path Planning Algorithm

## 3. Results

#### Comparison with Other Optimization Algorithms

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**Data fitted to Gaussian mixture model [20].

**Figure 5.**Probability of containment map obtained by fitting the drifter particle positions to a Gaussian distribution.

**Figure 14.**Evolution of the mean POC as the number of members of the population and number of iterations increase.

**Figure 17.**Evolution of the POC/time ratio as the number of members of the population and number of iterations increase.

Number of Iterations | ||||||
---|---|---|---|---|---|---|

5 | 10 | 15 | 25 | 50 | ||

Time | Mean | 0.2301 | 0.4221 | 0.6157 | 0.9981 | 1.8952 |

Std | 0.013 | 0.0157 | 0.0197 | 0.0400 | 0.0295 | |

POC $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{5}$ | Mean | 5.0197 | 5.1536 | 5.2459 | 5.3407 | 5.4704 |

Std | 0.2767 | 0.2267 | 0.2647 | 0.2456 | 0.3204 | |

Angle | Mean | 10.33 | 12.83 | 11.16 | 14.22 | 16.97 |

Std | 46.75 | 46.35 | 48.55 | 48.28 | 47.72 |

**Table 2.**Simulation results for an incrementing number of members of the population of the DE algorithm.

Number of Population Members | ||||||
---|---|---|---|---|---|---|

5 | 10 | 15 | 25 | 50 | ||

Time | Mean | 0.2301 | 0.446 | 0.6705 | 1.1309 | 2.2447 |

Std | 0.013 | 0.0065 | 0.0103 | 0.0504 | 0.0145 | |

POC $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{5}$ | Mean | 5.0197 | 5.0171 | 5.0209 | 5.0343 | 5.036 |

Std | 0.2767 | 0.2919 | 0.2878 | 0.3 | 0.2725 | |

Angle | Mean | 10.33 | 12.62 | 11.25 | 11.39 | 11.53 |

Std | 46.75 | 46.97 | 47.47 | 46.07 | 48.23 |

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**MDPI and ACS Style**

Muñoz, J.; López, B.; Quevedo, F.; Monje, C.A.; Garrido, S.; Moreno, L.E. Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs. *Drones* **2021**, *5*, 120.
https://doi.org/10.3390/drones5040120

**AMA Style**

Muñoz J, López B, Quevedo F, Monje CA, Garrido S, Moreno LE. Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs. *Drones*. 2021; 5(4):120.
https://doi.org/10.3390/drones5040120

**Chicago/Turabian Style**

Muñoz, Javier, Blanca López, Fernando Quevedo, Concepción A. Monje, Santiago Garrido, and Luis E. Moreno. 2021. "Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs" *Drones* 5, no. 4: 120.
https://doi.org/10.3390/drones5040120