UAV Network Path Planning and Optimization Using a Vehicle Routing Model
Abstract
:1. Introduction
2. Methods
2.1. Problem Definition
2.2. Model Establishment
3. Simulations
3.1. Observation Area
3.2. Simulation Results
- The VRP model-based UAV network path planning algorithm can perform task assignments after optimizing the coverage path of a single UAV, substantially reducing the time for UAV network observations.
- The proposed method effectively solves the UAV route crossing problem and is superior to the original method. The path planning results of the original method show a route crossing (Figure 4g,h and Figure 5c,d). By adding the constraint function, the optimized path planning results of the proposed method are shown in Figure 5a,b,e,f. The data in Table 1 indicate that the number of UAVs and the observation time are the same for the proposed and original methods, i.e., the proposed method solves the route-crossing problem without increasing the time.
- The preparation time of the UAV affects the result of the network task assignment. As shown in Table 1, the smaller the , the larger the number of UAVs. For instance, at a UAV flight speed of 7.5 km/h, the number of UAVs increased from 4 to 5 when the decreased from 0.2 h to 0.1 h. However, when the flight duration of the UAV was short, did not affect the number of required UAVs significantly. For example, at a flight speed of 6 km/h, the number of required UAVs remained at 4 whether the was 0.1 h or 0.2 h.
- The number of UAVs and the total observation time obtained from the solvers CPLEX and Gurobi are the same using the same method with the same parameters.
4. Discussion
4.1. Comparison of Path Planning Results for Two Task Allocation Goals
4.2. Modification of Objective Function
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Flight Speed/km/h | Preparation Time/h | Solver | Method | Number of UAVs | Observation Time of the k-th UAV/h | Network Observation Time/h | ||||
---|---|---|---|---|---|---|---|---|---|---|
7.5 | 0.1 | CPLEX | Optimization | 5 | 0.94 | 0.95 | 0.93 | 0.88 | 0.85 | 0.95 |
Original | 5 | 0.94 | 0.95 | 0.93 | 0.88 | 0.85 | 0.95 | |||
Gurobi | Optimization | 5 | 0.94 | 0.95 | 0.93 | 0.88 | 0.85 | 0.95 | ||
Original | 5 | 0.94 | 0.95 | 0.93 | 0.88 | 0.85 | 0.95 | |||
0.2 | CPLEX | Optimization | 4 | 0.94 | 1.05 | 1.15 | 1.05 | \ | 1.15 | |
Original | 4 | 0.97 | 1.11 | 115 | 1.0 | \ | 1.15 | |||
Gurobi | Optimization | 4 | 0.94 | 1.05 | 1.15 | 1.05 | \ | 1.15 | ||
Original | 4 | 0.97 | 1.11 | 115 | 1.0 | \ | 1.15 | |||
6 | 0.1 | CPLEX | Optimization | 4 | 1.17 | 1.16 | 1.12 | 1.07 | \ | 1.17 |
Original | 4 | 1.17 | 1.16 | 1.17 | 1.03 | \ | 1.17 | |||
Gurobi | Optimization | 4 | 1.17 | 1.16 | 1.12 | 1.07 | \ | 1.17 | ||
Original | 4 | 1.17 | 1.16 | 1.17 | 1.03 | \ | 1.17 | |||
0.2 | CPLEX | Optimization | 4 | 1.17 | 1.26 | 1.34 | 1.16 | \ | 1.34 | |
Original | 4 | 1.17 | 1.26 | 1.34 | 1.16 | \ | 1.34 | |||
Gurobi | Optimization | 4 | 1.17 | 1.26 | 1.34 | 1.16 | \ | 1.34 | ||
Original | 4 | 1.17 | 1.26 | 1.34 | 1.16 | \ | 1.34 |
Endurance Distance/km | Task Allocation Goal | Preparation Time/h | Number of UAVs | Network Observation Time/h |
---|---|---|---|---|
16.7 | Minimum observation time | 0.1 | 5 | 0.95 |
0.2 | 4 | 1.15 | ||
Minimum number of UAVs | 0.1 | 2 | 1.98 | |
0.2 | 2 | 1.98 | ||
13.3 | Minimum observation time | 0.1 | 4 | 1.17 |
0.2 | 4 | 1.34 | ||
Minimum number of UAVs | 0.1 | 2 | 1.89 | |
0.2 | 2 | 1.89 |
Flight Distance/km | Coefficient | Preparation Time/h | Number of UAVs | Network Observation Time/h | |
a | b | ||||
16.7 | 1 | 0 | 0.1 | 5 | 0.95 |
0.9 | 0.1 | 3 | 1.05 | ||
0.8 | 0.2 | 3 | 1.05 | ||
0.7 | 0.3 | 3 | 1.05 | ||
0.6 | 0.4 | 2 | 1.51 | ||
0.5 | 0.5 | 2 | 1.51 | ||
0 | 1 | 2 | 1.98 | ||
1 | 0 | 0.2 | 4 | 1.15 | |
0.9 | 0.1 | 3 | 1.25 | ||
0.8 | 0.2 | 3 | 1.25 | ||
0.7 | 0.3 | 2 | 1.51 | ||
0.6 | 0.4 | 2 | 1.51 | ||
0.5 | 0.5 | 2 | 1.51 | ||
0 | 1 | 2 | 1.98 | ||
13.3 | 1 | 0 | 0.1 | 4 | 1.17 |
0.9 | 0.1 | 3 | 1.27 | ||
0.8 | 0.2 | 3 | 1.27 | ||
0.7 | 0.3 | 3 | 1.27 | ||
0.6 | 0.4 | 2 | 1.89 | ||
0.5 | 0.5 | 2 | 1.89 | ||
0 | 1 | 2 | 1.89 | ||
1 | 0 | 0.2 | 4 | 1.34 | |
0.9 | 0.1 | 4 | 1.34 | ||
0.8 | 0.2 | 3 | 1.46 | ||
0.7 | 0.3 | 3 | 1.46 | ||
0.6 | 0.4 | 2 | 1.89 | ||
0.5 | 0.5 | 2 | 1.89 | ||
0 | 1 | 2 | 1.89 |
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Chen, X.; Li, Q.; Li, R.; Cai, X.; Wei, J.; Zhao, H. UAV Network Path Planning and Optimization Using a Vehicle Routing Model. Remote Sens. 2023, 15, 2227. https://doi.org/10.3390/rs15092227
Chen X, Li Q, Li R, Cai X, Wei J, Zhao H. UAV Network Path Planning and Optimization Using a Vehicle Routing Model. Remote Sensing. 2023; 15(9):2227. https://doi.org/10.3390/rs15092227
Chicago/Turabian StyleChen, Xiaotong, Qin Li, Ronghao Li, Xiangyuan Cai, Jiangnan Wei, and Hongying Zhao. 2023. "UAV Network Path Planning and Optimization Using a Vehicle Routing Model" Remote Sensing 15, no. 9: 2227. https://doi.org/10.3390/rs15092227
APA StyleChen, X., Li, Q., Li, R., Cai, X., Wei, J., & Zhao, H. (2023). UAV Network Path Planning and Optimization Using a Vehicle Routing Model. Remote Sensing, 15(9), 2227. https://doi.org/10.3390/rs15092227