Trajectory generation for fully autonomous flights of tail-sitter unmanned aerial vehicles (UAVs) presents substantial challenges due to their highly nonlinear aerodynamics. In this paper, we introduce, to the best of our knowledge, the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in unknown, cluttered environments. The UAV autonomy is enabled by cutting-edge technologies including LiDAR-based sensing, differential-flatness-based trajectory planning and control with purely onboard computation. In particular, we propose an optimization-based tail-sitter trajectory planning framework that generates high-speed, collision-free, and dynamically-feasible trajectories. To efficiently and reliably solve this nonlinear, constrained \textcolor{black}{problem}, we develop an efficient feasibility-assured solver, EFOPT, tailored for the online planning of tail-sitter UAVs. We conduct extensive simulation studies to benchmark EFOPT's superiority in planning tasks against conventional NLP solvers. We also demonstrate exhaustive experiments of aggressive autonomous flights with speeds up to 15m/s in various real-world environments, including indoor laboratories, underground parking lots, and outdoor parks. A video demonstration is available at https://youtu.be/OvqhlB2h3k8, and the EFOPT solver is open-sourced at https://github.com/hku-mars/EFOPT.

Abstract--Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, search and rescue. Efficient, accurate self and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This paper proposes Swarm-LIO2: a fully decentralized, plug-andplay, computationally efficient, and bandwidth-efficient LiDARinertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized, plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego-state, mutual observation measurements, and global extrinsic transformations. To support the plug-and-play of new teammate participants, Swarm-LIO2 detects potential teammate UAVs and initializes the temporal offset and global extrinsic transformation all automatically. For state estimation, Swarm-details can be found in the attached video at https://youtu.be/Q7cJ9iRhlrY GPS-denied scenes, degenerated scenes for cameras or LiDARs. GPS and RTK-GPS are commonly used for self-localization in outdoor environments, as reported in previous studies [22, 23]. N recent years, multi-robot systems, especially aerial swarm systems, have exhibited great potential in many for state estimation in multi-robot systems. These methods fields, such as collaborative autonomous exploration[1, 2, 3], [24, 25, 26, 27] often rely on the stationary ground station, target tracking[4, 5, 6, 7], search and rescue[8, 9, 10], etc. resulting in a centralized system that is prone to single-pointof-failure. Although the complementary and observed teammate locations (i.e., mutual observation anchor-free UWB can provide distance measurements, it is measurements), which are enhanced by careful measurement susceptible to multi-path effects and obstacle occlusion in the modeling and temporal compensation.