Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
<|reference_start|>Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions: Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory needs to be planned before the previously computed trajectory is completely executed. This becomes even more difficult if the trajectory is required to satisfy continuous-time collision-avoidance constraints while accounting for a large number of obstacles. To address these challenges, this paper proposes a novel real-time, receding-horizon motion planning algorithm named REachability-based trajectory Design via Exact Formulation of Implicit NEural signed Distance functions (REDEFINED). REDEFINED first applies offline reachability analysis to compute zonotope-based reachable sets that overapproximate the motion of the ego vehicle. During online planning, REDEFINED leverages zonotope arithmetic to construct a neural implicit representation that computes the exact signed distance between a parameterized swept volume of the ego vehicle and obstacle vehicles. REDEFINED then implements a novel, real-time optimization framework that utilizes the neural network to construct a collision avoidance constraint. REDEFINED is compared to a variety of state-of-the-art techniques and is demonstrated to successfully enable the vehicle to safely navigate through complex environments. Code, data, and video demonstrations can be found at https://roahmlab.github.io/redefined/.<|reference_end|>
@article{michaux2024reachability-based,
    title={Reachability-based Trajectory Design via Exact Formulation of Implicit
  Neural Signed Distance Functions},
    author={Jonathan Michaux, Qingyi Chen, Challen Enninful Adu, Jinsun Liu, and
  Ram Vasudevan},
    journal={arXiv preprint arXiv:2403.12280},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12280},
    primaryClass={cs.RO}
}