Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a co-designed hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open-source and open-hardware and supports both model-based and neural-network--based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, GPU-accelerated compute hardware for real-time perception and neural-network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural-network--based controllers. Our demonstrators include trajectory tracking at up to 5g and 70 km/h in a motion-capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Finally, we demonstrate its use for hardware-in-the-loop simulation in virtual-reality environments. Thanks to its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research.

翻译：自主敏捷四旋翼飞行在感知、规划、学习与控制方面对机器人研究提出了根本性挑战。为加速研究进程并让研究者聚焦核心问题，亟需一个标准化通用平台。为此，我们提出Agilicious——一套专为自主敏捷四旋翼飞行设计的软硬件协同框架。该框架完全开源且开放硬件，同时支持基于模型与基于神经网络的控制器。其具备高推力重量比与力矩惯量比以实现敏捷性，搭载机载视觉传感器、用于实时感知与神经网络推理的GPU加速计算硬件、实时飞行控制器，以及多功能软件栈。与现有框架相比，Agilicious实现了灵活软件栈与高性能硬件的独特组合。我们将其与既往工作进行对比，并在不同敏捷任务中分别验证基于模型与基于神经网络的控制器。实验展示包括：在运动捕捉系统中以高达5g加速度与70km/h速度完成轨迹追踪；在结构化与非结构化环境中仅凭机载感知实现视觉特技飞行与避障。此外，我们还展示了其在虚拟现实环境中进行硬件在环仿真的应用。凭借其多用途性，我们相信Agilicious将支持下一代科学与工业四旋翼研究。