Autonomous Mission Planning for Fixed-Wing Unmanned Aerial Vehicles in Multiscenario Reconnaissance.

Area of Science:

• Aerospace Engineering
• Robotics
• Artificial Intelligence

Background:

• Fixed-wing UAVs require effective target identification during reconnaissance before tracking missions.
• Current mission planning methods often overlook payload integration, focusing mainly on flight performance and navigation.
• Autonomous reconnaissance is crucial for efficient target identification in complex mission areas.

Purpose of the Study:

• To develop an autonomous mission planning method for fixed-wing UAVs that integrates flight path and payload planning.
• To enhance target identification capabilities for UAVs through optimized reconnaissance strategies.
• To address limitations in existing planning methods by incorporating payload characteristics.

Main Methods:

• A Hierarchical Traveling Salesman Problem (HTSP) based approach for optimal flight path planning, including nearest neighbor algorithm for target sequencing.
• Integration of Generalized Traveling Salesman Problem (GTSP) for coverage path planning (CPP) in areas with no-fly zones.
• Payload mission planning considering parameters like scan resolution, imaging width, and operating modes to generate instruction sets.

Main Results:

• The proposed method effectively combines flight path and payload mission planning for autonomous reconnaissance.
• HTSP and GTSP-based path planning ensured efficient coverage and waypoint generation, even with no-fly zones.
• Payload constraints were analyzed to optimize path planning, ensuring mission instructions met performance requirements.

Conclusions:

• The developed autonomous mission planning method significantly improves reconnaissance effectiveness for fixed-wing UAVs.
• The integration of flight path and payload planning offers a superior approach compared to existing methods.
• Simulations confirmed the method's effectiveness and superiority in enabling precise target identification prior to tracking.