无人机污染源自主搜寻算法综述

Review of UAV-based autonomous search algorithms for hazardous sources

自主搜寻在环境保护、灾后搜救和工业监控等方面有重要作用.无人机传感平台凭借其灵活的机动性成为实现自主搜寻的有效途径之一.针对自主搜寻中两个关键任务,即污染源定位和污染边界跟踪,本文分别梳理了相关算法体系,并揭示了其设计思想的本质.对于污染物溯源,控制驱动和信息驱动两类算法是目前的研究主流,但两者的结合却鲜有报道.在边界跟踪方面,本文从单平台和多平台协作两方面总结了现有文献的控制和估计方法.根据双重控制思想,本文分析了相关算法的设计思路,并展望了下一步的研究重点.

Autonomous search plays an important role in a wide range of applications,such as environmental protection,industrial monitoring,and search and rescue.Sensor platforms based on unmanned aerial vehicles(UAVs)have been regarded as one of the most efficient methods for achieving fully autonomous searching because of their superior flexibility and motility.This paper reviews existing algorithms for two important subjects,i.e.,source seeking and boundary tracking,based on which their design principles are revealed.Although control-oriented and information-driven approaches are the two mainstream state-of-the-art methods for source seeking,their hybrids have been poorly studied.Regarding boundary tracking,existing solutions can be allocated into two classes:single-agent tracking and multiagent cooperative tracking.From the perspective of dual control for exploration and exploitation,this review outlines the fundamental insights into algorithm design and presents several visions for future research.