Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.
翻译:主动位置估计(APE)是利用一个或多个遥感平台将一个或多个目标本地化的任务。APE是搜索和救援任务、野生生物监测、源词估计和协作型移动机器人的一项关键任务。APE的成功取决于遥感平台的合作程度、数量、自由程度和所收集信息的质量。APE控制法通过满足纯剥削性或纯探索性标准,使主动遥感成为可能。前者最大限度地减少了定位估计的不确定性;后者使平台更接近任务完成。在本文件中,我们界定了APE的主要内容,以系统分类和批判性地讨论该领域的艺术状况。我们还提出了一个参考框架,作为分类与APE有关的解决办法的正式主义。总体而言,这项调查探讨了主要挑战,并设想了本地化任务自主认知系统领域的主要研究方向。还有利于促进开发强有力的积极检测方法,以搜索和跟踪应用。