Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of
monitoring and research applications. To fully leverage their potential, a key
challenge is planning missions for efficient data acquisition in complex
environments. To address this issue, this article introduces a general
Informative Path Planning (IPP) framework for monitoring scenarios using an
aerial robot, focusing on problems in which the value of sensor information is
unevenly distributed in a target area and unknown a priori . The approach is
capable of learning and focusing on regions of interest via adaptation to map
either discrete or continuous variables on the terrain using
variable-resolution data received from probabilistic sensors. During a mission,
the terrain maps built online are used to plan information-rich trajectories in
continuous 3-D space by optimizing initial solutions obtained by a coarse grid
search. Extensive simulations show that our approach is more efficient than
existing methods. We also demonstrate its real-time application on a
photorealistic mapping scenario using a publicly available dataset and
demonstrate a proof of concept for an agricultural monitoring task.

该论文介绍了一种基于无人机的信息路径规划框架，在复杂环境下实现高效数据采集，通过对地形进行离散或连续变量的映射，学习和关注感兴趣区域，并通过优化初值解得到连续 3D 空间中信息丰富的轨迹，证明该方法比现有方法更高效。

基于无人机的地形监测信息化路径规划框架

An informative path planning framework for UAV-based terrain monitoring

Robot planning is the process of selecting a sequence of actions that
optimize for a task specific objective. The optimal solutions to such tasks are
heavily influenced by the implicit structure in the environment, i.e. the
configuration of objects in the world. State-of-the-art planning approaches,
however, do not exploit this structure, thereby expending valuable effort
searching the action space instead of focusing on potentially good actions. In
this paper, we address the problem of enabling planners to adapt their search
strategies by inferring such good actions in an efficient manner using only the
information uncovered by the search up until that time. We formulate this as a
problem of sequential decision making under uncertainty where at a given
iteration a planning policy must map the state of the search to a planning
action. Unfortunately, the training process for such partial information based
policies is slow to converge and susceptible to poor local minima. Our key
insight is that if we could fully observe the underlying world map, we would
easily be able to disambiguate between good and bad actions. We hence present a
novel data-driven imitation learning framework to efficiently train planning
policies by imitating a clairvoyant oracle - an oracle that at train time has
full knowledge about the world map and can compute optimal decisions. We
leverage the fact that for planning problems, such oracles can be efficiently
computed and derive performance guarantees for the learnt policy. We examine
two important domains that rely on partial information based policies -
informative path planning and search based motion planning. We validate the
approach on a spectrum of environments for both problem domains, including
experiments on a real UAV, and show that the learnt policy consistently
outperforms state-of-the-art algorithms.

利用数据驱动的模仿学习框架，通过模仿一个清晰的预测者，解决了机器人规划中的优化问题，从而为序列决策制定了更好的战略。在信息不完整的计划问题上进行了验证，包括真实的无人机实验，并且表现优于现有的算法。