FlowBot: Flow-based Modeling for Robot Navigation

Autonomous navigation among people is a com-plex 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 counter-flows 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.

Authors

• Daniel Dugas
• Kuanqi Cai
• Olov Andersson
• Nicholas R. J. Lawrance
• Roland Siegwart
• Jen Jen Chung

Keywords

• Navigation
• Benchmark testing
• Robot sensing systems
• Real-time systems
• Behavioral sciences
• Planning
• Observability
• Robot Navigation
• Simulation Scenarios
• Flow Model
• Train Station
• Partial Observation
• Crowded Environment
• Autonomous Navigation
• Flow Velocity
• Pedestrian
• Social Forces
• Over Space
• Slow Movement
• Velocity Estimation
• Planning Methods
• Planning Algorithm
• Fluid State
• Average Success Rate
• Static Obstacles
• Pedestrian Flow