In this paper, we address the challenge of exploring unknown indoor aerial environments using autonomous aerial robots with Size Weight and Power (SWaP) constraints. The SWaP constraints induce limits on mission time requiring efficiency in exploration. We present a novel exploration framework that uses Deep Learning (DL) to predict the most likely indoor map given the previous observations, and Deep Reinforcement Learning (DRL) for exploration, designed to run on modern SWaP constraints neural processors. The DL-based map predictor provides a prediction of the occupancy of the unseen environment while the DRL-based planner determines the best navigation goals that can be safely reached to provide the most information. The two modules are tightly coupled and run onboard allowing the vehicle to safely map an unknown environment. Extensive experimental and simulation results show that our approach surpasses state-of-the-art methods by 50-60% in efficiency, which we measure by the fraction of the explored space as a function of the length of the trajectory traveled.

翻译：本文针对尺寸、重量和功率受限的自主飞行机器人在未知室内环境中进行探索的挑战。此类约束限制了任务时间，要求探索过程具备高效性。我们提出了一种新颖的探索框架，该框架利用深度学习根据先前观测预测最可能的室内地图，并采用深度强化学习进行探索，专为满足现代尺寸、重量和功率约束的神经处理器设计。基于深度学习的映射预测器可对未知环境的占据情况进行预测，而基于深度强化学习的规划器则能确定可安全抵达的最优导航目标，以获取最大信息量。这两个模块紧密耦合且运行于机载设备上，使飞行器能够安全地绘制未知环境地图。广泛的实验与仿真结果表明，我们的方法在效率上超越当前最先进方法50-60%，该效率通过以轨迹行进长度为函数的探索空间比例进行衡量。