未知の障害物環境における分散型非線形モデル予測制御に基づく群れナビゲーションとリアルタイム障害物回避
Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance in Unknown Obstructed Environments
分散型非線形モデル予測制御を用いて、未知の障害物環境で群れ行動しながらナビゲーションする手法を提案し、点群処理による局所障害物回避を統合して計算負荷を低減、GazeboシミュレーションとHILシミュレーションで有効性を検証した。
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著者: Nuthasith Gerdpratoom, Kaoru Yamamoto
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This work extends our prior work on the distributed nonlinear model predictive control (NMPC) for navigating a robot fleet following a certain flocking behavior in unknown obstructed environments with a more realistic local obstacle avoidance strategy. More specifically, we integrate the local obstacle avoidance constraint using point clouds into the NMPC framework. Here, each agent relies on data from its local sensor to perceive and respond to nearby obstacles. A point cloud processing technique is presented for both two-dimensional and three-dimensional point clouds to minimize the computational burden during the optimization. The process consists of directional filtering and down-sampling that significantly reduce the number of data points. The algorithm's performance is validated through realistic 3D simulations in Gazebo, and its practical feasibility is further explored via hardware-in-the-loop (HIL) simulations on embedded platforms.
