FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
This addresses the problem of real-time safe navigation in unknown environments for autonomous systems, offering a modular solution that integrates with existing planners, though it is incremental in combining fast planning with safety guarantees.
The paper tackles the trade-off between computational speed and safety in motion planning for autonomous systems by introducing FaSTrack, a modular framework that provides a safety controller and guaranteed tracking error bounds for high-dimensional dynamics, demonstrated with a 10D quadrotor model tracking a 3D path from an RRT planner.
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.