Multi-UAV Routing for Area Coverage and Remote Sensing with Minimum Time.

Area of Science:

• Robotics
• Operations Research
• Computer Science

Background:

• Efficient coverage of ground areas with unmanned air vehicles (UAVs) is crucial for various applications.
• Traditional vehicle routing problem (VRP) solutions often do not account for practical constraints in real-world UAV operations.
• Minimizing time for area coverage with sensor-equipped UAVs presents unique challenges.

Purpose of the Study:

• To develop a novel methodology for minimum time area coverage using a team of UAVs.
• To address practical challenges in UAV task execution not typically found in standard VRP.
• To automatically determine the optimal number of UAVs required for efficient area coverage.

Main Methods:

• Task modeling as a graph with vertices representing geographic coordinates for minimum single UAV coverage time.
• Formulation and solution of a mixed integer linear programming problem to optimize the routing of multiple UAVs.
• Integration of practical constraints such as UAV maximum flight time and setup time into the optimization model.

Main Results:

• The proposed methodology effectively routes a team of UAVs for minimum time area coverage.
• The optimization process automatically selects the number of UAVs, considering operational constraints.
• Experimental results with two fixed-wing UAVs demonstrate the practical applicability of the approach.

Conclusions:

• The developed method offers an efficient solution for time-critical area coverage missions using UAVs.
• The automatic selection of UAV fleet size enhances operational efficiency and reduces mission planning complexity.
• This approach provides a significant advancement over traditional VRP solutions for real-world UAV deployment.