クアッドロータの軌道最適化のための深層学習
Deep Learning for Optimization of Trajectories for Quadrotors
深層学習とモデルベース最適化を組み合わせ、安全飛行回廊内の各区間の時間配分をニューラルネットで学習することで、クアッドロータの滑らかで高速な軌道をリアルタイム生成する手法を提案。
著者: Yuwei Wu, Xiatao Sun, Igor Spasojevic, Vijay Kumar
分類: cs.RO
原文アブストラクト
This paper presents a novel learning-based trajectory planning framework for quadrotors that combines model-based optimization techniques with deep learning. Specifically, we formulate the trajectory optimization problem as a quadratic programming (QP) problem with dynamic and collision-free constraints using piecewise trajectory segments through safe flight corridors [1]. We train neural networks to directly learn the time allocation for each segment to generate optimal smooth and fast trajectories. Furthermore, the constrained optimization problem is applied as a separate implicit layer for backpropagation in the network, for which the differential loss function can be obtained. We introduce an additional penalty function to penalize time allocations which result in solutions that violate the constraints to accelerate the training process and increase the success rate of the original optimization problem. To this end, we enable a flexible number of sequences of piece-wise trajectories by adding an extra end-of-sentence token during training. We illustrate the performance of the proposed method via extensive simulation and experimentation and show that it works in real time in diverse, cluttered environments.
