FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

Mo Chen, Sylvia Herbert, Haimin Hu, Ye Pu, Jaime Fernandez Fisac, Somil Bansal, Soojean Han, Claire J. Tomlin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. On the other hand, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified \textit{planning model} of the system. The plan is tracked by the system, represented by a more realistic, higher-dimensional \textit{tracking model}. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs.

Original languageEnglish (US)
JournalIEEE Transactions on Automatic Control
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Computational modeling
  • Heuristic algorithms
  • Planning
  • Real-time systems
  • Safety
  • Trajectory
  • Trajectory planning


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