A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs

Area of Science:

• Robotics
• Artificial Intelligence
• Logistics Management

Background:

• Unmanned aerial vehicles (UAVs) face challenges in real-time path planning for urban logistics due to complex environments.
• Existing algorithms often struggle with performance and safety in low-altitude urban settings.

Purpose of the Study:

• To develop an enhanced path planning method for UAVs in low-altitude urban logistics.
• To improve the real-time performance, accuracy, and safety of UAV path planning.

Main Methods:

• A hybrid approach combining a modified Beetle Antennae Search (BAS) algorithm with the Simulated Annealing (SA) algorithm.
• Designing a fitness function for waypoint search considering UAV flight constraints and obstacle avoidance.
• Improving BAS by dynamically adjusting search parameters and integrating SA for waypoint optimization.

Main Results:

• The proposed method demonstrated superior environmental adaptability across various obstacle densities.
• Significant improvements were observed in path length, waypoint efficiency, safety, and path smoothness compared to the original BAS.
• The algorithm effectively avoids local optima through the Metropolis criterion-based waypoint evaluation.

Conclusions:

• The enhanced path planning method effectively addresses the real-time performance limitations for UAVs in urban logistics.
• The hybrid BAS-SA approach offers a robust solution for safe and efficient UAV navigation in complex urban environments.
• This method satisfies the critical requirements for real-time path planning in low-altitude urban logistics applications.