Urban low-altitude UAV path planning by fusing an enhanced A* algorithm with an adaptive artificial potential field...

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Aerospace Engineering

Background:

• Trajectory planning for multi-rotor UAVs in urban low-altitude environments faces challenges like dense obstacles and dynamic avoidance.
• Existing methods struggle with path continuity and complex environmental interactions.

Purpose of the Study:

• To develop an advanced trajectory planning method for multi-rotor UAVs in intricate urban settings.
• To improve path feasibility, safety, and dynamic controllability during take-off, cruising, and landing.

Main Methods:

• Integration of an enhanced A* algorithm with an adaptive artificial potential field (APF) for trajectory planning.
• Utilized a 3D voxelized urban airspace model, phased advancement mechanism, dual-radius safety model, and 3D kinematic constraints.
• Employed improved APF combined forces for heading guidance and local obstacle avoidance, with B-spline algorithms for path smoothing.

Main Results:

• The A*-APF method reduced average computation time by over 36% and path length by over 18% compared to state-of-the-art algorithms.
• Achieved a 100% obstacle avoidance success rate in simulations.
• Demonstrated effective response to emergent threats and dynamic environments.

Conclusions:

• The proposed A*-APF method offers a robust solution for multi-rotor UAV trajectory planning in complex urban environments.
• The synergistic integration of multi-phase mechanisms and adaptive APF is crucial for high success rates and threat response.
• The method enhances mission profile support, ensuring safe and efficient UAV operations.