Selection of Solar Powered Unmanned Aerial Vehicles for a Long Range Data Acquisition Chain

Area of Science:

• Robotics and Autonomous Systems
• Wireless Communication Networks
• Energy Harvesting Technologies

Background:

• Traditional data acquisition systems are limited in remote areas lacking power and communication infrastructure.
• Unmanned Aerial Vehicles (UAVs) offer a potential solution for establishing data chains in such environments.
• Existing UAV capabilities may not suffice for long-range, continuous data acquisition due to energy and flight limitations.

Purpose of the Study:

• To design and evaluate a long-range data acquisition chain utilizing UAVs for off-grid, infrastructure-less regions.
• To address key challenges including energy harvesting, optimal drone selection for speed, and payload-dependent flight range.
• To provide a framework for selecting suitable UAVs for extended data acquisition missions.

Main Methods:

• Development of an energy harvesting strategy for UAV battery charging.
• Simulation-based analysis to determine optimal UAV models for minimizing travel time over extended distances.
• Modeling the reduction in UAV flight range as a function of payload mass.
• Cost-benefit analysis involving 54 distinct drone models and 25 solar cell configurations.

Main Results:

• The study simulated and analyzed a UAV-based data acquisition chain capable of operating beyond single-drone flight ranges.
• Identified critical factors influencing system performance, including energy availability, drone efficiency, and payload capacity.
• Cost analysis provided insights into the economic feasibility of deploying such systems.

Conclusions:

• A viable long-range data acquisition system using UAVs can be implemented in remote, off-grid areas.
• Addressing energy harvesting, drone selection criteria, and flight range limitations are crucial for system success.
• A seven-step methodology is proposed to guide the selection of appropriate UAVs for specific data acquisition tasks.