Hugues Thomas and his collaborators at the U of T Institute for Aerospace Studies created a new method for robot navigation based on self-supervised deep learning (photo by Safa Jinje)
Hugues Thomas and his collaborators at the U of T Institute for Aerospace Studies created a new method for robot navigation based on self-supervised deep learning (photo by Safa Jinje) - A team of researchers led by University of Toronto Professor Tim Barfoot is using a new strategy that allows robots to avoid colliding with people by predicting the future locations of dynamic obstacles in their path. The project, which is supported by Apple Machine Learning, will be presented at the International Conference on Robotics and Automation in Philadelphia at the end of May. The results from a simulation, which are not yet peer-reviewed, are available on the arXiv preprint service. "The principle of our work is to have a robot predict what people are going to do in the immediate future," says Hugues Thomas , a post-doctoral researcher in Barfoot's lab at the University of Toronto Institute for Aerospace Studies in Faculty of Applied Science & Engineering. "This allows the robot to anticipate the movement of people it encounters rather than react once confronted with those obstacles." To decide where to move, the robot makes use of Spatiotemporal Occupancy Grid Maps (SOGM). These are 3D grid maps maintained in the robot's processor, with each 2D grid cell containing predicted information about the activity in that space at a specific time. The robot choses its future actions by processing these maps through existing trajectory-planning algorithms. Another key tool used by the team is light detection and ranging (lidar), a remote sensing technology similar to radar except that it uses light instead of sound.
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