Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field

TL;DR

This paper introduces a novel adaptive artificial potential field method for UAVs that dynamically accounts for obstacle movement, effectively resolving local minima issues and improving collision avoidance in complex, dynamic environments.

Contribution

It proposes a Direction and Relative Velocity Weighted APF integrated with MPC, addressing local minima and obstacle kinematics for UAV collision avoidance.

Findings

Effectively resolves local minima in obstacle avoidance.

Enhances safety with smooth, predictive maneuvers.

Demonstrates superior path integrity in simulations.

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.