Dynamic smooth and stable obstacle avoidance for unmanned aerial vehicle based on collision prediction

Haixu Ma, Zixuan Cai, Guoan Xu, Guang Yang, Guanyu Chen
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Abstract

Abstract Autonomous dynamic obstacle avoidance of unmanned aerial vehicles (UAVs) based on collision prediction is critical for stable flight missions. UAVs must deal with high‐speed and nonlinearly moving obstacles that obey nonlinear dynamics, which are one of the numerous objects affecting stable flight. To satisfy the requirements of smooth flight and attitude stability, a novel dynamic smooth obstacle avoidance (QVA) method based on obstacle trajectory prediction is proposed. To predict the dynamic obstacle trajectories, a trajectory prediction (QLTP) method using a quasi‐linear parameter varying representations is proposed. The proposed QVA integrates the QLTP approach, velocity obstacle (VO) approach, and artificial potential field (APF) methods. The QVA detects an imminent collision based on the QLTP method, and then replans the UAV path based on the VO method at the time of the predicted collision. The UAV tracks planned waypoints for collision avoidance. To ensure the flight safety of the UAV, a virtual APF is constructed with waypoints as local targets and obstacles. The simulation results show that the proposed method performs better than the improved APF and VO methods in terms of the smoothness of the obstacle avoidance path and the stability of the UAV attitude.

Abstract Image

基于碰撞预测的无人机动态平稳避障
摘要基于碰撞预测的无人机自主动态避障是实现无人机稳定飞行任务的关键。无人机必须处理服从非线性动力学的高速非线性移动障碍物,这是影响无人机稳定飞行的众多目标之一。为满足飞行器平稳飞行和姿态稳定的要求,提出了一种基于障碍物轨迹预测的动态平滑避障方法。为了预测动态障碍物的运动轨迹,提出了一种准线性参数变化表示的运动轨迹预测方法。该方法集成了QLTP方法、速度障碍方法和人工势场方法。QVA基于QLTP方法检测即将发生的碰撞,然后在预测碰撞发生时基于VO方法重新规划无人机路径。无人机跟踪规划的航路点以避免碰撞。为了保证无人机的飞行安全,以航路点作为局部目标和障碍物,构建了虚拟APF。仿真结果表明,该方法在避障路径的平滑性和无人机姿态的稳定性方面都优于改进的APF和VO方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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