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

2026-01-12

AI Summary

This work addresses the challenge of achieving both high-speed and safe flight for micro aerial vehicles (MAVs) in unknown, cluttered environments, where existing approaches tend to be overly conservative. The authors propose an integrated planning and control framework that generates time-optimal polynomial trajectories at a 100 Hz replanning rate and, for the first time, leverages imitation learning to accelerate online time allocation. By incorporating variable-horizon safety corridors and a time-optimal model predictive contouring control (MPCC) strategy, the system enables aggressive yet safe maneuvering under high dynamic conditions. Tightly coupling LiDAR-based perception with real-time replanning, the approach significantly enhances flight aggressiveness in simulation and demonstrates robust performance in real-world experiments, achieving a peak speed of 18 m/s and completing ten consecutive missions successfully in complex environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• autonomous flight
• time-optimal
• micro air vehicles
• cluttered environments
• aggressive maneuvering

Innovation

• time-optimal trajectory
• imitation learning
• model predictive contouring control
• safe flight corridor
• autonomous MAV