Abstract:The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, $\omega(\theta,v_{e})$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.
From: Nikita Pavle [view email]
[v1]
Mon, 8 Dec 2025 14:57:01 UTC (4,307 KB)
[v2]
Tue, 9 Dec 2025 17:04:29 UTC (4,307 KB)
[v3]
Mon, 24 Aug 2026 02:48:31 UTC (4,307 KB)
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