RTD-RAX: Fast, Safe Trajectory Planning for Systems under Unknown Disturbances
This work addresses safe trajectory planning for autonomous systems under real-time disturbances, representing an incremental improvement over existing RTD methods.
The paper tackles the limitations of Reachability-based Trajectory Design (RTD) by introducing RTD-RAX, which uses a non-conservative formulation and mixed monotone reachability for fast, disturbance-aware safety certification, enabling safe trajectory planning under unknown disturbances with a repair procedure for unsafe cases.
Reachability-based Trajectory Design (RTD) is a provably safe, real-time trajectory planning framework that combines offline reachable-set computation with online trajectory optimization. However, standard RTD implementations suffer from two key limitations: conservatism induced by worst-case reachable-set overapproximations, and an inability to account for real-time disturbances during execution. This paper presents RTD-RAX, a runtime-assurance extension of RTD that utilizes a non-conservative RTD formulation to rapidly generate goal-directed candidate trajectories, and utilizes mixed monotone reachability for fast, disturbance-aware online safety certification. When proposed trajectories fail safety certification under real-time uncertainty, a repair procedure finds nearby safe trajectories that preserve progress toward the goal while guaranteeing safety under real-time disturbances.