Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions
<|reference_start|>Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions: Real-time navigation in a priori unknown environment remains a challenging task, especially when an unexpected (unmodeled) disturbance occurs. In this paper, we propose the framework Safe Returning Fast and Safe Tracking (SR-F) that merges concepts from 1) Robust Control Lyapunov-Value Functions (R-CLVF), and 2) the Fast and Safe Tracking (FaSTrack) framework. The SR-F computes an R-CLVF offline between a model of the true system and a simplified planning model. Online, a planning algorithm is used to generate a trajectory in the simplified planning space, and the R-CLVF is used to provide a tracking controller that exponentially stabilizes to the planning model. When an unexpected disturbance occurs, the proposed SR-F algorithm provides a means for the true system to recover to the planning model. We take advantage of this mechanism to induce an artificial disturbance by ``jumping'' the planning model in open environments, forcing faster navigation. Therefore, this algorithm can both reject unexpected true disturbances and accelerate navigation speed. We validate our framework using a 10D quadrotor system and show that SR-F is empirically 20\% faster than the original FaSTrack while maintaining safety.<|reference_end|>
@article{gong2024safe,
    title={Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions},
    author={Zheng Gong, Boyang Li, Sylvia Herbert},
    journal={arXiv preprint arXiv:2404.02472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02472},
    primaryClass={cs.RO cs.SY eess.SY}
}