スペクトルと共形予測による校正済み軌道予測と制御点区間のニューラルネットワーク手法
Spectral and Conformal: Neural Network Methods for Calibrated Trajectory Prediction and Control-Point Intervals
スペクトル基底を持つニューラルネットで軌道と分位帯を学習し、修正共形分位回帰で軌道レベルの被覆を校正、区間演算で制御点まで保証を拡張する枠組みを提案。Raspberry Pi Zero 2 W上で約100Hzのパイプラインを実現した。
著者: Jasmine Ratchford, Raffaele Romagnoli
分類: eess.SY, cs.AI, cs.LG, cs.RO
原文アブストラクト
Autonomous systems that navigate their environment need methods to quickly assess the feasibility and safety of desired trajectories. Traditional solutions that rely on numerical integration can be computationally heavy, while fast data-driven surrogates lack the formal guarantees that numerical approaches provide. This work demonstrates a framework that uses (1) a neural network with a spectral backbone to learn trajectories and quantile bands with built-in constraints (initial conditions and stability); (2) a modified conformal quantile regression to provide calibrated, joint trajectory-level coverage; and (3) interval arithmetic to extend noise-free coverage guarantees to control points. After refactoring the network for inference, the trajectory inference time for a quadrotor under a closed-loop linear quadratic regulator is 3.45~ms on a Raspberry Pi Zero 2 W. The total pipeline time, including trajectory inference time, is 9.81~ms, or approximately 100~Hz, consistent with typical control rates.