基于改进人工势场的未知障碍物无人机编队避障

计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 686-693.doi: 10.11896/jsjkx.210500194

基于改进人工势场的未知障碍物无人机编队避障

陈博琛¹, 唐文兵², 黄鸿云³, 丁佐华¹

1 浙江理工大学信息学院杭州 310018
2 华东师范大学软件工程学院上海 200062
3 浙江理工大学图书馆多媒体大数据中心杭州 310018

出版日期:2022-06-10 发布日期:2022-06-08
通讯作者: 丁佐华(zouhuading@hotmail.com)
作者简介:(chbc@foxmail.com)
基金资助:
国家自然科学基金(61751210)

Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field

CHEN Bo-chen¹, TANG Wen-bing², HUANG Hong-yun³, DING Zuo-hua¹

1 School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China
2 Software Engineering Institute,East China Normal University,Shanghai 200062,China
3 Center of Multimedia Big Data of Library,Zhejiang Sci-Tech University,Hangzhou 310018,China

Online:2022-06-10 Published:2022-06-08
About author:CHEN Bo -chen,born in 1995,postgra-duate.His main research interests include autonomous vehicles,artificial intelligence and multi-robot collaboration.
DING Zuo -hua,born in 1964,professor,Ph.D supervisor.His main research interests include system modeling,software reliability prediction,intelligent software systems and service robots.
Supported by:
National National Natural Science Foundation of China(61751210).

摘要/Abstract

摘要： 随着无人机相关技术的成熟,无人机的发展前景和潜在的应用场景也被越来越多的人所认识,其中无人机编队能够打破单个无人机在载荷和任务种类等方面受到的限制,因此无人机编队飞行是未来重要的发展方向。在飞行的过程中,无人机编队可能会受到新建高楼、临时禁飞区等未知障碍物的限制。目前避障方法的主要关注点是在出发前且障碍物信息已知的条件下,为二维场景下的无人机生成不与障碍物相交的参考路径。但是这种方法不够灵活,无法满足在实际三维环境前进的过程中避开这些未知障碍物的要求。文中提出一种碰撞风险感知的编队防碰撞系统(Formation Collision Avoidance System,FCAS),通过对无人机的运动趋势进行分析,筛选出编队中最有可能发生碰撞的无人机;通过改进人工势场(Improved Artificial Potential Field,iAPF)对未知障碍物进行躲避,能够有效避免避障过程中编队中无人机间的碰撞,有效减少编队内部通信链路的数量,将障碍物对无人机编队的影响降到最低。在完成避障后,所有无人机将重新保持原来的队形并返回参考路径。模拟实验显示,该系统使无人机编队在参考路径飞行过程中能够处理静态未知障碍物,并最终无碰撞地抵达终点,验证了策略的可行性。

关键词: 避障, 编队防碰撞系统, 多旋翼无人机, 人工势场

Abstract: With the maturity of UAV-related technologies,the development prospects and potential application scenarios of UAVs are also recognized by more and more people.Among them,UAV formation can overcome the load,endurance and mission of a single UAV.Due to restrictions on types and other aspects,UAV formation flying is an important development direction in the future.During the flight,the UAV formation may be restricted by unknown obstacles such as new high-rise buildings and temporary no-fly zones.The main focus of current obstacle avoidance methods is to generate a reference flight path that does not intersect the obstacle for the UAV in the two-dimensional scene before the departure,with the obstacle information is known.However,this method is not flexible enough to meet the requirements of avoiding these unknown obstacles in the process of advancing in the actual three-dimensional environment.A formation collision avoidance system(FCAS) for collision risk perception is proposed.By analyzing the movement trend of UAVs,those UAVs within the formation that are most likely to collide are screened out,and the improved artificial potential field is used to unknown obstacles.Avoidance of such obstacles can effectively avoid collisions between UAVs within the formation during the obstacle avoidance process,effectively reduce the number of communication links within the formation,and minimize the impact of obstacles on the formation of UAVs.After the obstacle avoi-dance is completed,all UAVs will resume their original formation and return to the reference paths.Simulation results show that the system enables the UAV formation to deal with static unknown obstacles during the flight of the reference path,and finally reaches the destination without collision,thus verifying the feasibility of the strategy.

Key words: Artificial potential field, Formation collision avoidance system, Multi-rotor UAV, Obstacle avoidance

中图分类号:

TP301

陈博琛, 唐文兵, 黄鸿云, 丁佐华. 基于改进人工势场的未知障碍物无人机编队避障[J]. 计算机科学, 2022, 49(6A): 686-693. https://doi.org/10.11896/jsjkx.210500194

CHEN Bo-chen, TANG Wen-bing, HUANG Hong-yun, DING Zuo-hua. Pop-up Obstacles Avoidance for UAV Formation Based on Improved Artificial Potential Field[J]. Computer Science, 2022, 49(6A): 686-693. https://doi.org/10.11896/jsjkx.210500194

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