フロー誘導マルコフニューラルオペレータによるロボットナビゲーションのための軌道予測
Trajectory Prediction for Robot Navigation using Flow-Guided Markov Neural Operator
歩行者の動きをマルコフ過程としてモデル化し、オプティカルフローを統合したニューラルオペレータFlowMNOで軌道予測を行う手法を提案。既存の深層学習手法を大きく上回る精度を示し、ロボットナビゲーションへの応用可能性を示した。
著者: Rashmi Bhaskara, Hrishikesh Viswanath, Aniket Bera
分類: cs.RO
原文アブストラクト
Predicting pedestrian movements remains a complex and persistent challenge in robot navigation research. We must evaluate several factors to achieve accurate predictions, such as pedestrian interactions, the environment, crowd density, and social and cultural norms. Accurate prediction of pedestrian paths is vital for ensuring safe human-robot interaction, especially in robot navigation. Furthermore, this research has potential applications in autonomous vehicles, pedestrian tracking, and human-robot collaboration. Therefore, in this paper, we introduce FlowMNO, an Optical Flow-Integrated Markov Neural Operator designed to capture pedestrian behavior across diverse scenarios. Our paper models trajectory prediction as a Markovian process, where future pedestrian coordinates depend solely on the current state. This problem formulation eliminates the need to store previous states. We conducted experiments using standard benchmark datasets like ETH, HOTEL, ZARA1, ZARA2, UCY, and RGB-D pedestrian datasets. Our study demonstrates that FlowMNO outperforms some of the state-of-the-art deep learning methods like LSTM, GAN, and CNN-based approaches, by approximately 86.46% when predicting pedestrian trajectories. Thus, we show that FlowMNO can seamlessly integrate into robot navigation systems, enhancing their ability to navigate crowded areas smoothly.
