  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.
