# Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2406.19646 · HTML: https://mlsanity.com/paper/2406.19646/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [AlphaPilot: Autonomous Drone Racing](https://mlsanity.com/paper/2005.12813/) (2020)
- [Time-Optimal Planning for Quadrotor Waypoint Flight](https://mlsanity.com/paper/2108.04537/) (2021)
- [AirSim Drone Racing Lab](https://mlsanity.com/paper/2003.05654/) (2020)
- [Learning Minimum-Time Flight in Cluttered Environments](https://mlsanity.com/paper/2203.15052/) (2022)
- [Search-based Motion Planning for Aggressive Flight in SE(3)](https://mlsanity.com/paper/1710.02748/) (2017)
- [A New Approach to Time-Optimal Path Parameterization based on Reachability Analysis](https://mlsanity.com/paper/1707.07239/) (2017)
- [Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control](https://mlsanity.com/paper/1709.05401/) (2017)
- [Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing](https://mlsanity.com/paper/2309.06837/) (2023)
- [Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight](https://mlsanity.com/paper/2305.02772/) (2023)
- [Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments](https://mlsanity.com/paper/2401.09705/) (2024)

## Built on by (most cited first, 2 of 2)

- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)

## Abstract (arXiv)

This paper addresses the problem of guiding a quadrotor through a predefined sequence of waypoints in cluttered environments, aiming to minimize the flight time while avoiding collisions. Previous approaches either suffer from prolonged computational time caused by solving complex non-convex optimization problems or are limited by the inherent smoothness of polynomial trajectory representations, thereby restricting the flexibility of movement. In this work, we present a safe reinforcement learning approach for autonomous drone racing with time-optimal flight in cluttered environments. The reinforcement learning policy, trained using safety and terminal rewards specifically designed to enforce near time-optimal and collision-free flight, outperforms current state-of-the-art algorithms. Additionally, experimental results demonstrate the efficacy of the proposed approach in achieving both minimum flight time and obstacle avoidance objectives in complex environments, with a commendable 66.7% success rate in unseen, challenging settings.