Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet

Area of Science:

• Robotics and Automation
• Remote Sensing Technologies
• Computer Science

Background:

• Unmanned Aerial Vehicles (UAVs) offer cost-effective and flexible platforms for data collection.
• Drone-based remote sensing is valuable for environmental monitoring, disaster response, and exploration.
• Efficient deployment of drone fleets is crucial due to limited battery life and operational constraints.

Purpose of the Study:

• To develop a 3D placement algorithm for multi-drone sensing platforms.
• To ensure complete area coverage without dead zones while minimizing the number of deployed drones.
• To optimize drone energy consumption and maximize fleet lifetime in remote areas.

Main Methods:

• A 3D landscaping-aware drone placement algorithm was proposed.
• The algorithm incorporates line-of-sight considerations and dynamic adjustments based on an energy model.
• The energy model accounts for mechanical motion, data transmission, calculation, and inter-drone communication.

Main Results:

• The proposed scheme effectively covers target regions with minimal drone deployment.
• Simulations demonstrated a significant extension of the drone fleet's operational lifetime.
• The algorithm successfully minimized overall energy consumption across complex terrains.

Conclusions:

• The developed 3D placement algorithm enhances the efficiency and endurance of drone fleets for remote sensing missions.
• This approach is particularly beneficial for operations in remote or rural areas lacking recharging infrastructure.
• Optimized drone placement leads to prolonged mission duration and reduced operational costs.