# FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments

arXiv: https://arxiv.org/abs/1903.03558 · HTML: https://mlsanity.com/paper/1903.03558/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [SABLAS: Learning Safe Control for Black-box Dynamical Systems](https://mlsanity.com/paper/2201.01918/) (2022)
- [A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs](https://mlsanity.com/paper/2311.12893/) (2023)
- [Task-driven Compression for Collision Encoding based on Depth Images](https://mlsanity.com/paper/2309.05289/) (2023)
- [SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots](https://mlsanity.com/paper/2108.01262/) (2021)
- [Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning](https://mlsanity.com/paper/2403.08152/) (2024)
- [What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing](https://mlsanity.com/paper/2109.08494/) (2021)
- [Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance](https://mlsanity.com/paper/2412.02249/) (2024)
- [Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs](https://mlsanity.com/paper/2111.00166/) (2021)
- [Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics](https://mlsanity.com/paper/2507.09340/) (2025)
- [Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires](https://mlsanity.com/paper/2602.08326/) (2026)

## Abstract (arXiv)

High-speed trajectory planning through unknown environments requires algorithmic techniques that enable fast reaction times while maintaining safety as new information about the operating environment is obtained. The requirement of computational tractability typically leads to optimization problems that do not include the obstacle constraints (collision checks are done on the solutions) or use a convex decomposition of the free space and then impose an ad-hoc time allocation scheme for each interval of the trajectory. Moreover, safety guarantees are usually obtained by having a local planner that plans a trajectory with a final "stop" condition in the free-known space. However, these two decisions typically lead to slow and conservative trajectories. We propose FASTER (Fast and Safe Trajectory Planner) to overcome these issues. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety guarantees are ensured by always having a feasible, safe back-up trajectory in the free-known space at the start of each replanning step. Furthermore, we present a Mixed Integer Quadratic Program formulation in which the solver can choose the trajectory interval allocation, and where a time allocation heuristic is computed efficiently using the result of the previous replanning iteration. This proposed algorithm is tested extensively both in simulation and in real hardware, showing agile flights in unknown cluttered environments with velocities up to 3.6 m/s.