陰的ニューラル符号付距離関数の厳密定式化による到達可能性ベース軌道設計
Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
オフラインの到達可能性解析とゾノトープ演算を組み合わせ、ニューラル陰的表現で厳密な符号付距離を計算する衝突回避制約を構築し、自動運転車の実時間再cedingホライズン軌道計画を安全に実現する手法を提案。
著者: Jonathan Michaux, Qingyi Chen, Challen Enninful Adu, Jinsun Liu, Ram Vasudevan
分類: cs.RO
原文アブストラクト
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/.
