Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, S. Scherer
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引用次数: 9

Abstract

—We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultane- ously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard a team of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a new frontier-based exploration algorithm. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to 3 robots that fly up to 3 m/s and distances exceeding one kilometer. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. We provide an extended discussion of lessons learned, release software as open source, and present a video in the supplementary material.
一组空中机器人对多种环境的弹性多传感器探索
我们提出了一个协调的自主管道,用于多传感器在受限环境中的探索。我们同时解决了在之前的工作中通常被忽视的四个广泛的挑战:(a)有效利用距离和视觉传感模式,(b)在广泛的环境中进行这种探索,(c)对不良事件具有弹性,以及(d)在物理机器人团队上执行此任务。我们的解决方案以行为树架构为中心,它可以自适应地在各种行为之间切换,包括协调探索和响应不良事件。我们的勘探策略利用了视觉和距离传感器的优势,采用了一种新的基于边界的勘探算法。自动化管道通过一系列广泛的现场实验进行评估,团队中有多达3个机器人,飞行速度可达3米/秒,飞行距离超过1公里。我们提供了各种现场实验的总结,并详细介绍了产生的弹性行为:操纵狭窄的门道,适应意外的环境变化,以及紧急着陆。我们提供了对经验教训的扩展讨论,将软件作为开放源代码发布,并在补充材料中提供了一个视频。
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