# Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Overall Structure of UAV Water Sampling and Detection System

#### 2.2. Control Scheme for Water Sampling and Multi-Parameter Monitoring System

## 3. Double-Layer Composite Path Planning Algorithm Based on PSO and RRT

#### 3.1. Hierarchical Hybrid Improved PSO Algorithm

#### 3.1.1. Dynamic Adjustment of Model Parameters

#### 3.1.2. Introduction of Crossover and Mutation Operators

#### 3.2. Obstacle Avoidance Path Planning Algorithm Based on Hierarchical Mixed Improved PSO and RRT

#### 3.3. Path Processing

#### 3.4. Implementation Steps of the Improved Algorithm

Algorithm 1: LHIPSO algorithm |

Input: ω_{max}; ω_{min}; c_{1}; c_{2}; low; up; Pc; Pm; iteration number; sampling point; M; N; UAV point;Output: Sampling point execution sequence |

1:Random initialization of M particles 2: for i = 1 to N do//Calculate the distance between the sampling points3: for j =1 to N do//n is the number of sampling points4: calculate D(i,j)//D(i,j) is the sampling point distance 5: end for6: end for7: for i = 1 to M do//Calculate the fitness value8: Calculate fitness(i) 9: end for10:Calculate pbest, gbest//Calculate the local and global optimality value 11: while iter < itermax 12: Array = sort(fitness)//Sort fitness(i) in ascending order according to fitness value 13: The velocity and position of pop(1:low,:) are updated by Equation (4). 14: The velocity and position of pop(low + 1:up,:) are updated by Equation (5). 15: The velocity and position of pop(up + 1:M,:) are updated by the crossover mutation operator. 16: Calculate fitness 17: [minvalue,min_index] = min(fitness)//Determine the minimum fitness value and index 18: if minvalue < fitness(pbest) then//Update the local optimality value19: pbest = pop(min_index) 20: end if21: if minvalue< fitness(gbest) then//Update the global optimality value22: gbest = pbest 23: end if26: end while25: Return gbest |

Algorithm 2: RRT algorithm |

Input: map; sampling point; N; UAV point; gbest; stepOutput: UAV flight path |

1:Position sequence = [UAV point; sampling point(gbest);UAV point]//The sequence of locations that need to be planned 2: for i = 1 to (N + 1) do//Cyclic planning of adjacent points3: Xstart = Position sequence[i] 4: T = Xstart 5: Xgoal = Position sequence[i + 1] 6: parent = 1 7: while goalFound = false//Whether to plan to the end8: Xrand = sample(Xstart, Xgoal) 9: Xnear = Nearest(T, Xrand) 10: Xnew = Steer(Xnear, Xrand, step) 11: if Obstaclecollision(Xnew, Xnear, map) == false//Obstacle judgment12: parent(Xnew) = Xnear 13: T = [T, Xnew] 14: if distance(Xnew, Xgoal) ≤ Thresholdvalue//Target point judgment15: goalFound = True 16: break; 17: end if18: else if19: continue 20: end if21: end while22: Path = [Path, T]//All adjacent point path combinations 23: end for24: return Path |

## 4. Experimental Analysis and Results

#### 4.1. Simulation Experiment

#### 4.1.1. Environmental Model Design

#### 4.1.2. Simulation Results and Analysis

#### 4.2. Field Experiment

#### Fishpond Experiment Data and Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Illustration of the autonomous sampling process of the UAV water quality sampling and monitoring system.

**Figure 3.**The flowchart of the sampling and detection process, where $\left({x}_{i},{y}_{i},{z}_{i}\right)$ denotes the UAV’s position; $\left({x}_{i},{y}_{i},{z}_{i}\right)\left(i=1,2,\dots ,n\right)$ is the waypoint sequence; $(\theta ,\beta ,\phi )$ is a set consisting of the pitch, yaw, and roll angles of the UAV; $D$ is the distance between the UAV’s position and the current sampling position; ${D}_{n}$ is the distance error threshold; $({\theta}_{d},{\beta}_{d},{\phi}_{d})$ is the difference between the current and target attitude angles; ${h}_{c}$ is the current flying altitude; ${h}_{n}$ is the UAV’s altitude at the sampling point.

**Figure 7.**Simulation results of UAV path planning: (

**a**) path planning result of the LHIPSO algorithm; (

**b**) the path obtained after using the RRT in path planning; (

**c**) the path after removing redundant points for the previously planned path; (

**d**) the second-order B-spline smoothing path.

**Figure 8.**Simulation experimental data. (

**a**) Curve of the relationship between iteration count and fitness value. (

**b**) Comparison of algorithm parameters.

Sensor Name | Component Image | Test Range | Operating Voltage (V)/Current (mA) | Accuracy (at 25 °C) | Output Voltage (V) | Operating Temperature (°C) |
---|---|---|---|---|---|---|

pH | 0–14 | 5 V/(5–10) mA | ±1% | 0–5 | 0–60 | |

Turbidity | 0–1000 NTU | 5 V/40 mA | ±5% | 0–4.5 | −20–90 | |

TDS | 0–1000 ppm | (3.3–5.0) V/(3–6) mA | ±5% | 0–2.3 | 0–55 |

Water Quality Parameters | Depth: 1 m | Depth: 2 m | ||||
---|---|---|---|---|---|---|

Maximum Value | Minimum Value | Mean Value | Maximum Value | Minimum Value | Mean Value | |

TDS (ppm) | 92 | 82 | 84.35 | 88 | 83 | 84.35 |

Turbidity (NTU) | 84.4 | 16 | 34.16 | 103 | 21.3 | 58.42 |

pH | 8.52 | 6.95 | 7.72 | 8.44 | 6.9 | 7.485 |

Nitrite (mg/L) | 0.025 | 0.001 | 0.011 | 0.08 | 0.001 | 0.013 |

Ammonia Nitrogen (mg/L) | 1.37 | 0.02 | 0.37 | 0.64 | 0.01 | 0.31 |

DO (mg/L) | 14.42 | 3.5 | 9.47 | 12.65 | 3.87 | 7.25 |

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## Share and Cite

**MDPI and ACS Style**

Zhang, R.; Wang, Z.; Li, X.; She, Z.; Wang, B.
Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation. *Water* **2023**, *15*, 2129.
https://doi.org/10.3390/w15112129

**AMA Style**

Zhang R, Wang Z, Li X, She Z, Wang B.
Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation. *Water*. 2023; 15(11):2129.
https://doi.org/10.3390/w15112129

**Chicago/Turabian Style**

Zhang, Rihong, Zhenhao Wang, Xiaomin Li, Zipeng She, and Baoe Wang.
2023. "Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation" *Water* 15, no. 11: 2129.
https://doi.org/10.3390/w15112129