SHINE: Social Homology Identification for Navigation in Crowded Environments
arxiv(2024)
摘要
Navigating mobile robots in social environments remains a challenging task
due to the intricacies of human-robot interactions. Most of the motion planners
designed for crowded and dynamic environments focus on choosing the best
velocity to reach the goal while avoiding collisions, but do not explicitly
consider the high-level navigation behavior (avoiding through the left or right
side, letting others pass or passing before others, etc.). In this work, we
present a novel motion planner that incorporates topology distinct paths
representing diverse navigation strategies around humans. The planner selects
the topology class that imitates human behavior the best using a deep neural
network model trained on real-world human motion data, ensuring socially
intelligent and contextually aware navigation. Our system refines the chosen
path through an optimization-based local planner in real time, ensuring
seamless adherence to desired social behaviors. In this way, we decouple
perception and local planning from the decision-making process. We evaluate the
prediction accuracy of the network with real-world data. In addition, we assess
the navigation capabilities in both simulation and a real-world platform,
comparing it with other state-of-the-art planners. We demonstrate that our
planner exhibits socially desirable behaviors and shows a smooth and remarkable
performance.
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