Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

Area of Science:

• Aerospace Engineering
• Artificial Intelligence
• Operations Research

Background:

• Unmanned aerial vehicles (UAVs) are crucial for surveillance missions, where autonomy and endurance are key performance indicators.
• Reducing fuel consumption in UAVs directly translates to increased operational range and mission duration.
• Existing methods for optimizing UAV power settings often lack efficiency or adaptability to dynamic mission parameters.

Purpose of the Study:

• To develop and validate a particle swarm optimization (PSO) method for optimizing UAV power settings along a predefined trajectory.
• To minimize fuel consumption and maximize the autonomy of fixed-wing UAVs during surveillance operations.
• To enhance the computational efficiency of the optimization process through parallelization.

Main Methods:

• Implemented 3D path smoothing for fixed-wing UAVs using circular arcs over waypoints.
• Utilized equations of motion and Newton's equation decomposition to calculate fuel consumption based on power settings.
• Applied particle swarm optimization (PSO) to determine optimal power settings, adhering to physical constraints (load factor, lift coefficient, speed, fuel capacity).
• Parallelized the PSO algorithm on a multicore processor for accelerated computation.

Main Results:

• The proposed PSO method achieved up to a 25% reduction in fuel consumption across tested trajectories.
• The parallel implementation demonstrated a significant speedup of 21.67× compared to a sequential CPU implementation.
• The method successfully optimized power settings while respecting all specified physical constraints.

Conclusions:

• Particle swarm optimization (PSO) provides an effective strategy for minimizing fuel consumption and enhancing UAV autonomy in surveillance missions.
• The parallelized approach offers rapid, real-time power setting optimization, crucial for adapting to in-flight trajectory changes.
• This research offers a valuable tool for improving the efficiency and effectiveness of UAV operations.