LOONG: Online Time-Optimal Autonomous Flight for MAVs in Cluttered Environments

2026-01-12 · Xin Guan, Fangguo Zhao, Qianyi Wang, Chengcheng Zhao, Jiming Chen, Shuo Li arxiv

Autonomous flight of micro air vehicles (MAVs) in unknown, cluttered environments remains challenging for time-critical missions due to conservative maneuvering strategies. This article presents an integrated planning and control framework for high-speed, time-optimal autonomous flight of MAVs in cluttered environments. In each replanning cycle (100 Hz), a time-optimal trajectory under polynomial presentation is generated as a reference, with the time-allocation process accelerated by imitation learning. Subsequently, a time-optimal model predictive contouring control (MPCC) incorporates safe flight corridor (SFC) constraints at variable horizon steps to enable aggressive yet safe maneuvering, while fully exploiting the MAV's dynamics. We validate the proposed framework extensively on a custom-built LiDAR-based MAV platform. Simulation results demonstrate superior aggressiveness compared to the state of the art, while real-world experiments achieve a peak speed of 18 m/s in a cluttered environment and succeed in 10 consecutive trials from diverse start points. The video is available at the following link: https://youtu.be/vexXXhv99oQ.

📄 PDF Abstract BibTeX arXiv:2601.07434

Code (0)

등록된 구현이 없습니다.

Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection

2026-05-28 · Yutong Wang, Xuebo Liu, Derek F. Wong, Zhilin Li 외 arxiv

Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while simultaneously suffering from redundant …

Reinforcement Learning

LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm

2025-12-30 · Chunhui Wan, Xunan Dai, Zhuo Wang, Minglei Li 외 arxiv

The transition from static Large Language Models (LLMs) to self-improving agents is hindered by the lack of structured reasoning in traditional evolutionary approaches. Existing methods often struggle with premature conv…

Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

2026-02-27 · Václav Riss, Vít Krátký, Robert Pěnička, Martin Saska arxiv

This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of the tower. Our algorithm relies on camer…

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

2020-04-23 · Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan Mcallister 외

Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These changes can lead to suboptimal flight perfo…

Meta-Learning Meta Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Simultaneous learning of state-to-state minimum-time planning and control

2025-10-22 · Swati Dantu, Robert Pěnička, Martin Saska arxiv

This paper tackles the challenge of learning a generalizable minimum-time flight policy for UAVs, capable of navigating between arbitrary start and goal states while balancing agile flight and stable hovering. Traditiona…