论文标题

目标追逐,墙壁建造和消防:Nimbro团队的自治无人机,在MBZIRC 2020

Target Chase, Wall Building, and Fire Fighting: Autonomous UAVs of Team NimbRo at MBZIRC 2020

论文作者

Beul, Marius, Schwarz, Max, Quenzel, Jan, Splietker, Malte, Bultmann, Simon, Schleich, Daniel, Rochow, Andre, Pavlichenko, Dmytro, Rosu, Radu Alexandru, Lowin, Patrick, Scheider, Bruno, Schreiber, Michael, Süberkrüb, Finn, Behnke, Sven

论文摘要

穆罕默德·本·扎伊德国际机器人挑战赛(MBZIRC)2020年对无人机(无人机)提出了各种各样的挑战。我们介绍了四个量身定制的无人机,这些无人机是专门针对MBZIRC的单个空中机器人任务开发的,包括自定义硬件和软件组件。 在挑战1中,使用高效率的板载对象检测管道来追求目标无人机,以从目标无人机捕获球。第二个无人机使用类似的检测方法来查找和散布在整个竞技场的流行气球。 对于挑战2,我们演示了一个能够自动空中操纵的较大的无人机:从相机图像中找到并跟踪砖块。随后,它们接近,采摘,运输并放在墙上。 最后,在挑战3中,我们的无人机自动地使用LiDAR和热摄像头发现火灾。它用载火灭火器灭火。 尽管每个机器人都具有特定于任务的子系统,但所有无人机都依赖于针对此特定和未来竞争开发的标准软件堆栈。我们介绍了主要是开源软件解决方案,包括用于系统配置,监视,强大的无线通信,高级控制和敏捷轨迹生成的工具。为了解决MBZIRC 2020年的任务,我们在机器视觉和轨迹生成等多个研究领域中提高了最新技术。 我们提出了我们的科学贡献,构成了我们的算法和系统的基础,并分析了2020年MBZIRC竞赛的结果,在阿布扎比,我们的系统在大挑战中排名第二。此外,我们讨论了从参与这一复杂的机器人挑战中学到的经验教训。

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 posed diverse challenges for unmanned aerial vehicles (UAVs). We present our four tailored UAVs, specifically developed for individual aerial-robot tasks of MBZIRC, including custom hardware- and software components. In Challenge 1, a target UAV is pursued using a high-efficiency, onboard object detection pipeline to capture a ball from the target UAV. A second UAV uses a similar detection method to find and pop balloons scattered throughout the arena. For Challenge 2, we demonstrate a larger UAV capable of autonomous aerial manipulation: Bricks are found and tracked from camera images. Subsequently, they are approached, picked, transported, and placed on a wall. Finally, in Challenge 3, our UAV autonomously finds fires using LiDAR and thermal cameras. It extinguishes the fires with an onboard fire extinguisher. While every robot features task-specific subsystems, all UAVs rely on a standard software stack developed for this particular and future competitions. We present our mostly open-source software solutions, including tools for system configuration, monitoring, robust wireless communication, high-level control, and agile trajectory generation. For solving the MBZIRC 2020 tasks, we advanced the state of the art in multiple research areas like machine vision and trajectory generation. We present our scientific contributions that constitute the foundation for our algorithms and systems and analyze the results from the MBZIRC competition 2020 in Abu Dhabi, where our systems reached second place in the Grand Challenge. Furthermore, we discuss lessons learned from our participation in this complex robotic challenge.

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