SURF 2020: Social-Aware Robot Navigation

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2020 SURF: project description

  • Mentor: Richard M. Murray
  • Co-mentor: Francesca Baldini

Real-time navigation in dense human environments is a challenging problem in robotics.

In order to navigate through a dense crowd in a socially compliant manner, robots need to understand human behavior and comply with their cooperative rules.

We are interested in an autonomous robot design that can detect human intentions and interact safely with them to navigate itself in the crowd.

In this study, we may first use data-driven methods to detect the intention of the human agent on-board and also the intention of other agents nearby. Then a higher-level controller can be designed to execute, modify, or override the human intention. The autonomous robot design will be implemented and tested on a robot platform available in the lab. The testing scenarios may include crowd locations, e.g. campus cafeteria.

Skills Required:

  • For the controller design, general knowledge about planar rigid-body dynamics and PID control are desired.
  • For the experimental implementation, programming experience with Python, Pytorch and ROS is needed.

References:

[1] https://www.semanticscholar.org/paper/Robot-navigation-in-dense-human-crowds%3A-Statistical-Trautman-Ma/f5783d21492ceeec6cfcf42595d270dedbb91d5e

[2] https://arxiv.org/pdf/1809.08835.pdf

[3] https://arxiv.org/pdf/1903.00143.pdf