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People Prediction, Tracking, and Avoidance

Robots must behave predictably in order to be accepted in human environments. Part of predictability is that robots in turn must anticipate the motion of humans so that they can avoid causing unnecessary disturbance. For example, when approaching a person in a hallway, the robot should move to one side to allow the person to pass. In more open environments, such as the lobby of a hotel, people's paths can be harder to predict. By combining machine learning techniques with knowledge of the geometry of a given environment, the robot can form a statistical model of likely destinations for a person given their current trajectory. After constructing a time/space map of likely future person locations, the robot must then plan a path through the room that minimizes the chance interaction with the person while still reaching the robot's destination.

Link

Carnegie Mellon Robotics Institute Project

Publications

  • Ross A. Knepper and Daniela Rus, “Pedestrian-Inspired Sampling-Based Multi-Robot Collision Avoidance”, in Proceedings of the International Symposium on Robot and Human Interactive Communication (RO-MAN), Paris, France, September 2012. [PDF]