小型自律飛行ロボットによる屋内環境の効率的探索学習
Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles
深層学習で未観測領域の地図を予測し、深層強化学習で探索経路を計画する枠組みを提案し、SWaP制約下の小型ドローンで効率を50〜60%向上させた。
著者: Yuezhan Tao, Eran Iceland, Beiming Li, Elchanan Zwecher, Uri Heinemann, Avraham Cohen, Amir Avni, Oren Gal, Ariel Barel, Vijay Kumar
分類: cs.RO
原文アブストラクト
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.
