Event Location: NSH 1305
Bio: Ashish Kapoor is a senior researcher at Microsoft Research, Redmond. Currently, his research focuses on Aerial Informatics and Robotics with an emphasis on building intelligent and autonomous flying agents that are safe and enable applications that can positively influence our society. The research builds upon cutting edge research in machine intelligence, robotics and human-centered computation in order to enable an entire fleet of flying robots that range from micro-UAVs to commercial jetliners. Various applications scenarios include Weather Sensing, Monitoring for Precision Agriculture, Safe Cyber-Physical Systems etc. Ashish received his PhD from MIT Media Laboratory in 2006. He also holds FAA Commercial Pilot certificate (SEL), FAA Flight Instructor certificate (Airplane Single Engine and Instrument Airplane) and is an avid amateur aircraft builder (see build blog: http://rv8qb.blogspot.com/).

Abstract: Achieving optimality while staying safe is one of the key problems that arise when planning under uncertainty. We specifically focus on path planning for aerial vehicles, where the uncertainties arise due to unobserved winds and other air traffic. A flight plan or a policy that doesn’t take into account such uncertainties can not only result in highly inefficient flight paths but can also jeopardize safety. In this talk, we will first focus on how to reduce uncertainty in wind predictions by using airplanes in flight as a large-scale sensor network. In particular, we explore how information from existing commercial aircraft on their normal business can be harnessed to observe and predict weather phenomena at a continental scale in greater detail that currently available. In the second part of the talk, we consider the problem of path planning under uncertain winds and traffic conditions. Specifically we propose planning algorithms that trade off exploration and exploitation in near-optimal manner and have appealing no-regret properties. Further, we will also discuss how Probabilistic Signal Temporal Logic (PrSTL) can be adapted to the robotic path planning problems in order to guarantee safety. We will present results from longitudinal real-world studies that demonstrate effectiveness of the framework.