FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking
This addresses the problem of safe, real-time navigation for autonomous systems, offering a modular solution that balances speed and robustness, though it appears incremental as it builds on existing reachability methods.
The authors tackled the challenge of achieving both real-time replanning and guaranteed safety in motion planning for navigation in unknown environments, proposing FaSTrack, a modular framework that precomputes tracking error bounds and controllers, and demonstrated it with three different planners and model pairs.
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, 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.