# FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning

arXiv: https://arxiv.org/abs/1703.07373 · HTML: https://mlsanity.com/paper/1703.07373/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2017.

## Built on by (most cited first, 23 of 23)

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- [NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning](https://mlsanity.com/paper/2210.00120/) (2022)
- [DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization](https://mlsanity.com/paper/2403.05571/) (2024)
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- [Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning](https://mlsanity.com/paper/2505.10547/) (2025)
- [Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU](https://mlsanity.com/paper/2604.07644/) (2026)
- [Correct-by-construction reach-avoid control of partially observable linear stochastic systems](https://mlsanity.com/paper/2103.02398/) (2021)
- [Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models](https://mlsanity.com/paper/2205.05787/) (2022)
- [Learning Contraction Policies from Offline Data](https://mlsanity.com/paper/2112.05911/) (2021)
- [Can LLMs plan paths with extra hints from solvers?](https://mlsanity.com/paper/2410.05045/) (2024)
- [Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers](https://mlsanity.com/paper/2605.25346/) (2026)
- [Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landing](https://mlsanity.com/paper/2311.08652/) (2023)
- [Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems](https://mlsanity.com/paper/2401.04960/) (2024)
- [Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy](https://mlsanity.com/paper/2512.17899/) (2025)
- [Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees](https://mlsanity.com/paper/2601.01076/) (2026)
- [Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems](https://mlsanity.com/paper/2304.06876/) (2023)
- [Robots that Suggest Safe Alternatives](https://mlsanity.com/paper/2409.09883/) (2024)
- [Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approach](https://mlsanity.com/paper/2512.22793/) (2025)
- [RENEW: Risk- and Energy-Aware Navigation in Dynamic Waterways](https://mlsanity.com/paper/2601.16424/) (2026)
- [LeHami: Symplectic Latent Dynamics for World Models](https://mlsanity.com/paper/2610.15267/) (2026)

## Abstract (arXiv)

Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for autonomous systems is computationally intensive, often requiring simplified dynamics that sacrifice safety and dynamic feasibility in order to plan efficiently. Conversely, safe trajectories can be computed using more sophisticated dynamic models, but this is typically too slow to be used for real-time planning. We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems. A path or trajectory planner using simplified dynamics to plan quickly can be incorporated into the FaSTrack framework, which provides a safety controller for the vehicle along with a guaranteed tracking error bound. This bound captures all possible deviations due to high dimensional dynamics and external disturbances. Note that FaSTrack is modular and can be used with most current path or trajectory planners. We demonstrate this framework using a 10D nonlinear quadrotor model tracking a 3D path obtained from an RRT planner.