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arXiv · 2403.12280

Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions

Jonathan Michaux·Qingyi Chen·Challen Enninful Adu·Jinsun Liu·Ram Vasudevan
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Abstract

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/.

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Jonathan Michaux, Qingyi Chen, Challen Enninful Adu, Jinsun Liu, Ram Vasudevan. 2024-03-18. Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions. https://arxiv.org/abs/2403.12280

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