Feasible Computationally Efficient Path Planning for UAV Collision Avoidance
This work addresses collision avoidance for UAVs in complex scenarios, offering a computationally efficient solution suitable for platforms with low computing capability, though it appears incremental as it combines existing methods.
The paper tackles the problem of real-time collision avoidance for UAVs by proposing a hybrid MWF-APF algorithm that switches between wall-following and artificial potential field methods, incorporating historical trajectory to improve decision-making, and demonstrates its effectiveness through simulations and physical tests on a quad-rotor with limited sensors.
This paper presents a robust computationally efficient real-time collision avoidance algorithm for Unmanned Aerial Vehicle (UAV), namely Memory-based Wall Following-Artificial Potential Field (MWF-APF) method. The new algorithm switches between Wall-Following Method (WFM) and Artificial Potential Field method (APF) with improved situation awareness capability. Historical trajectory is taken into account to avoid repetitive wrong decision. Furthermore, it can be effectively applied to platform with low computing capability. As an example, a quad-rotor equipped with limited number of Time-of-Flight (TOF) rangefinders is adopted to validate the effectiveness and efficiency of this algorithm. Both software simulation and physical flight test have been conducted to demonstrate the capability of the MWF-APF method in complex scenarios.