Abstract
Path planning is one of the key technologies in unmanned aerial systems. The path-planning algorithm for UAVs in this study incorporates a three-dimensional obstacle model, addressing the limitations of existing research that primarily focuses on the two-dimensional plane. This approach elevates traditional two-dimensional obstacle constraints to a three-dimensional space, fulfilling the requirements for precise obstacle avoidance. Utilizing B-spline curves, an obstacle boundary model is proposed, which, in combination with an improved artificial potential field accounting for localized self-locking oscillations, achieves smooth path planning for obstacle avoidance from the starting point to the destination. The simulation results show the effectiveness of this method in collision-free path planning within three-dimensional environments containing static single or multiple obstacles. At the points of maximum curvature in the two-dimensional coordinate system, the path planned by the proposed algorithm exhibits a smoothness improvement of 68 % and 98 %, respectively, as compared to the traditional artificial potential field algorithm with the equivalent three-dimensional obstacle model and the tangent point method. The proposed algorithm enables drones to achieve precise obstacle avoidance along surfaces, generating smoother collision-free flight paths in a shorter period of time.
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