災害現場での視覚に基づく自律着陸
Vision-based Autonomous Landing in Catastrophe-Struck Environments
倒壊建物の瓦礫上に安全に着陸するため、地形の平坦さや傾斜などを考慮したコストマップとクラスタリングで着陸地点を選び、最小ジャーク軌道で自律着陸するシステムを提案した。
著者: Mayank Mittal, Abhinav Valada, Wolfram Burgard
分類: cs.RO
原文アブストラクト
Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as the structure of these debris piles is often unknown and no prior knowledge can be leveraged. In this work, we propose a computationally efficient system that is able to reliably identify safe landing sites and autonomously perform the landing maneuver. Specifically, our algorithm computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy and energy consumption information. We first estimate dense candidate landing sites from the resulting costmap and then employ clustering to group neighboring sites into a safe landing region. Finally, a minimum-jerk trajectory is computed for landing considering the surrounding obstacles and the UAV dynamics. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.
