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.

翻译：本文件为无人驾驶航空飞行器(无人驾驶飞行器)提供了一种强有力的计算高效实时避免碰撞算法,即以内存为基础的长城后人造潜在场(MWF-APF)方法。长效跟踪法和人造潜在场法(APF)之间新的算法开关,提高了对情况的认识能力;考虑到历史轨迹,以避免重复作出错误的决定;此外,还可以有效地应用于计算能力低的平台;例如,采用了一种四轮式,配备了有限的光时空测距仪,以验证这种算法的效能和效率;进行了软件模拟和物理飞行测试,以证明多功能测距法在复杂情况下的能力。