In this talk, I will discuss approaches that enable a quadrotor to
autonomously navigate and explore complex indoor and outdoor
environments. Micro-aerial vehicles (MAVs), and in our case quadrotors,
offer exceptional 3D mobility over ground platforms, making them
particularly suitable for search-and-rescue missions in which the
vehicle must be able to navigate through complex 3D environments. In
such missions, especially in response to emergency or disaster
situations, it may not be safe for a human to enter the environment and
therefore the MAV must be able to operate fully autonomously without
requiring any human operator commands or external infrastructure. This
talk summarizes a sequence of projects that move towards the goal of
fully autonomous MAVs and will consist of three parts: (1) algorithms
and systems design that enable autonomous exploration of complex indoor
3D environments with a quadrotor equipped with a laser scanner, an IMU,
and limited computation; (2) a state estimation approach that permits
autonomous navigation in mixed indoor and outdoor environments using
laser, GPS and IMU; and (3) a vision-based state estimator that greatly
expand the capable operational environments of our quadrotor platform.
Extensive experimental evaluations are presented in each part of the
talk.
Bio:
Shaojie Shen is a Ph.D candidate in the Department of Electrical and
Systems Engineering at the University of Pennsylvania. His research
interests include autonomous navigation of ground and aerial robots in
complex environments with focus on state estimation and mapping. His
work on autonomous micro-aerial vehicle has been covered by major media
outlets such as ABC, The New Yorker, and Discovery Channel.