深度画像を活用した深層ニューラルネットワークによる衝突回避のためのN-MPC
N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images
深度画像を処理する深層ニューラルネットワークを非線形モデル予測制御に組み込み、UAVの軌道追従タスクにおける衝突回避を実現した研究。
著者: Martin Jacquet, Kostas Alexis
分類: cs.RO
原文アブストラクト
This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance in trajectory-tracking tasks with UAVs. The network is trained on simulated depth images to output a collision score for queried 3D points within the sensor field of view. Then, this network is translated into an algebraic symbolic equation and included in the N-MPC, explicitly constraining predicted positions to be collision-free throughout the receding horizon. The N-MPC achieves real time control of a UAV with a control frequency of 100Hz. The proposed framework is validated through statistical analysis of the collision classifier network, as well as Gazebo simulations and real experiments to assess the resulting capabilities of the N-MPC to effectively avoid collisions in cluttered environments. The associated code is released open-source along with the training images.
