Autonomous Tail-Sitter Flights in Unknown Environments

TL;DR

This article introduces the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in unknown, cluttered environments, and develops an efficient feasibility-assured solver, Efficient Feasibility-assured OPTimization solver (EFOPT), tailored for the online planning of tail-sitter UAVs.

Abstract

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.

Figures (14)

• Figure 1: Autonomous tail-sitter UAV equipped with a LiDAR, driven by real-time onboard perception, planning, and control.
• Figure 2: Trajectory paramterization. The trajectory (blue curve) consecutively connects the initial position $\mathbf p_0$, internal waypoint sequence $\mathbf q_1, \cdots, \mathbf q_{M-1}$, and terminal position $\mathbf p_f$. Each trajectory segment has flight duration $t_i$, and the entire flight duration is $T_f$.
• Figure 3: System overview
• Figure 4: Three benchmark simulations to evaluate the performance of different solvers in trajectory optimization.
• Figure 5: Convergence progress in the two-variable optimization for the backward transition trajectory in Fig. \ref{['fig_sim']} (a).

Theorems & Definitions (1)

• Remark 1