An Anti-Disturbance Resilience Enhanced Algorithm for UAV 3D Route Planning

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Artificial Intelligence

Background:

• Unmanned Aerial Vehicle (UAV) operations in complex urban environments face significant challenges due to spatial disturbances.
• Existing route planning algorithms often lack robustness against real-time, time-varying environmental uncertainties.
• Ensuring UAV safety and mission success requires advanced navigation strategies that enhance resilience.

Purpose of the Study:

• To develop a novel 3D route planning algorithm for UAVs that improves resilience and safety in complex environments.
• To address the limitations of traditional algorithms in handling dynamic disturbances and ensuring real-time operational safety.
• To enhance the UAV's ability to tolerate general uncertain disturbances and improve overall system resilience.

Main Methods:

• Development of a risk identification, assessment, and modeling method for UAV safety.
• Implementation of a 3D route planning algorithm combining A* and artificial potential fields (REPARE).
• Real-time adjustment of potential risk discriminant coefficients to manage time-varying disturbances.

Main Results:

• The REPARE algorithm demonstrates superior anti-disturbance capabilities compared to traditional methods.
• REPARE offers enhanced safety over standard A* algorithms and reduced running time compared to artificial potential field algorithms.
• The algorithm effectively overcomes local optimization issues and improves tolerance to uncertain disturbances.

Conclusions:

• The REPARE algorithm significantly enhances the resilience of UAV 3D route planning in complex and disturbed environments.
• REPARE provides a safer and more efficient navigation solution for UAVs operating in urban settings.
• The developed method improves the UAV system's ability to adapt to and overcome unpredictable environmental challenges.