Fast, lightweight autonomy through an unknown cluttered environment: Distribution statement: A — Approved for public release; distribution unlimited
Fast, lightweight autonomy through an unknown cluttered environment: Distribution statement: A — Approved for public release; distribution unlimited
复制标题
在未知的混乱环境中实现快速、轻量级的自治：分发声明：A — 批准公开发行，无限制；
DOI：
10.1109/aero.2017.7943617
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Rose
中科院分区：
文献类型：
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作者：
S. Paschall;J. Rose
清理建筑物的使命仍然是军事团队和急救人员执行的最危险的行动之一。寻找隐藏战斗人员或被困受害者的人员必须在危险和陌生的空间中迅速移动，面对每个角落的生命或死亡情况。智能机器人侦察兵可以帮助进行快速侦察，而搜索团队可以安全地监视外部的进度，但是墙壁，门和混乱的空间使操作员很难远程驾驶车辆。为了实现这一愿景，国防高级研究项目局（DARPA）快速轻量级自治（FLA）计划旨在创建新的导航，感知，计划和控制算法，从而通过未知的杂物环境实现自主，高速飞行。该计划将这项新技术与小型四轮摩托车无人机（UAV）相结合，该计划将展示激进，敏捷的飞行，在该飞行中，车辆将通过未知的室内/室外环境自动感知并操纵，而没有外部通信或GPS，以高达20米/20米/速度第二。 Draper/MIT团队正在努力创建高级导航，感知，计划和控制算法，运行在板载机上的定制，紧密集成的嵌入式计算和传感航空电子平台，以实现FLA程序目标。我们的算法必须非常有效，有效和低延迟，才能通过混乱，未知的环境来实现这种实时敏捷感和计划能力。为了支持此功能，我们广泛依靠高质量的IMU和单眼摄像机数据来实现准确的GPS有限的导航以及对直接环境的感知，以进行路径计划和避免碰撞的操作。计划在此程序下开发的软件，以最终作为DARPA作为开源的发布，以进一步促进自主权的最先进。这项技术不仅适用于无人机，还适用于任何自主机器人平台，探索未知的复杂环境，在这些环境中迅速移动而避免危险障碍至关重要。此外，使用室内功能的（即小型）四轮摩托车作为开发平台，可确保所得的航空电子系统将是低尺寸，重量和功率（交换），因此非常适合各种自主机器人（空气，陆地，陆地，陆地，陆地，陆地，陆地，陆地，陆地，土地）海洋和空间）应用。本文介绍了Draper/MIT团队正在使用的高级系统体系结构和算法方法，以及有关最新现场测试结果的报告。
The mission of clearing buildings remains among the most dangerous operations performed by military teams and first responders. Personnel searching for hidden combatants or trapped victims must move quickly through hazardous and unfamiliar spaces, facing life-or-death situations around each corner. A smart robotic scout could help perform rapid reconnaissance while search teams safely monitor progress from the outside, but walls, doors, and cluttered spaces make it difficult for operators to pilot the vehicles remotely. Towards this vision, the Defense Advanced Research Projects Agency (DARPA) Fast Lightweight Autonomy (FLA) program aims to create a new class of navigation, perception, planning, and control algorithms which enables autonomous, high-speed flight through unknown, cluttered environments. Integrating this new technology with small quad-rotor unmanned aerial vehicles (UAVs), the program will demonstrate aggressive, agile flight where the vehicle will autonomously sense and maneuver through unknown indoor/outdoor environments without external communications or GPS at speeds up to 20 meters/second. The Draper/MIT team is working to create advanced navigation, perception, planning, and control algorithms running on-board a custom, tightly integrated embedded computing and sensing avionics platform to accomplish the FLA program goals. Our algorithms must be exceedingly efficient, effective, and low-latency to enable this real-time agile perception and planning capability through cluttered, unknown environments. To support this capability we rely extensively on high quality IMU and monocular camera data to enable accurate GPS-denied navigation and perception of the immediate environment for path planning and collision avoidance maneuvering. The software developed under this program is planned for eventual release as open source by DARPA, to further advance the state-of-the-art in autonomy. This technology has applicability to not only UAVs, but to any autonomous robotic platform exploring unknown complex environments where moving quickly yet avoiding hazardous obstacles is critical. Additionally, using indoor capable (i.e. small) quad-rotor UAVs as the development platform guarantees that the resulting avionics system will be low Size, Weight, and Power (SWaP), and therefore ideal for a variety of autonomous robotic (air, land, sea, and space) applications. This paper describes the high-level system architecture and algorithmic approaches being utilized by the Draper/MIT team, as well as reports on recent field testing results.