Trajectory Prediction for Robot Navigation using Flow-Guided Markov Neural Operator

Hrishikesh Viswanath, Rashmi Bhaskara, Aniket Bera

Abstract:

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 stateof-the-art deep learning methods like LSTM, GAN, and CNNbased 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.