FlowBot: Flow-based Modeling for Robot Navigation
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Author / Producer
Date
2022
Publication Type
Conference Paper
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yes
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Abstract
Autonomous navigation among people is a complex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-fluid model of crowd flow for such problems. These have an intuitive physical interpretation and do not require much tuning. We further formalize an observation model to infer flow properties from discrete sensor observations, including support for partial observability, and pair it with a flow-aware planner. We demonstrate the potential of the approach in simulated navigation scenarios. We achieve state of the art results on the CrowdBot navigation benchmark, and also compare favorably against a standard ROS planner on a partially observable environment, demonstrating that the flow-aware planner successfully estimates and plans around counterflows in the crowd in real time. We conclude that flow-based planning shows great promise for crowded environments that may exhibit such flow-like behavior.
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Publication status
published
Editor
Book title
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
8799 - 8805
Publisher
IEEE
Event
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
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Methods
Software
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Subject
Navigation; Benchmark testing; Robot sensing systems; Real-time systems; Behavioral sciences; Planning; Observability