UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments
UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments
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DOI：
10.3390/rs12203386
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发表时间：
2020-10-01
期刊：
影响因子：
5
通讯作者：
Gonzalez, Felipe
中科院分区：
文献类型：
--
作者：
Sandino, Juan;Vanegas, Fernando;Gonzalez, Felipe
在边境保护、人道主义救济和灾害监测等紧急应用中，无人机的使用改善了应急工作，无人机提供了灵活部署的空中眼睛。随着自主行为的进步，如避障、起飞、着陆、悬停和航路点飞行模式，这些努力得到了进一步改善。然而，大多数无人机缺乏在复杂环境中导航的自主决策。这种限制导致无人机依赖于地面控制站，因此也依赖于它们的通信系统。在室内飞行作业中，挑战甚至更为复杂，因为在室内飞行作业中，全球导航卫星系统（GNSS）信号的强度不存在或很弱，影响了飞机的行为。本文提出了一种无人机自主导航框架，以解决不完整的传感器读数在杂乱的室内场景中的不确定性和部分可观测性。该框架设计将计算过程分配到无人机的飞行控制器和配套计算机上，使其能够在没有人类操作员监督和物理存在的情况下探索危险的室内区域。该系统是说明下的搜索和救援（SAR）的情况下，检测和定位受害者在一个模拟的办公楼。导航问题被建模为部分可观测马尔可夫决策过程（POMDP），并通过增广信度树（ABT）算法在真实的时间内求解。数据收集使用硬件在环（HIL）模拟和真实的飞行试验。实验结果表明，所提出的框架的鲁棒性，以检测受害者在各种级别的位置不确定性。所提出的系统通过让无人机在没有人类操作员干预的情况下探索危险环境来确保人身安全。
Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.