Planning for Large-Scale Multi-Robot Systems 🗓

— Robots navigate autonomously in Amazon fulfillment centers, Multi-robot path-planning.

Knobbe Martens Irvine offices Map

IEEE OC Computer Society & OCACM
Meeting Date: January 16, 2018
Time: 6:30 PM Networking & Food; 7:00 PM Presentation
Speaker: Sven Koenig
Location: Knobbe Martens Irvine offices
Cost: none
RSVP: requested, through website
Event Details: IEEE vTools

Summary: Multi-robot systems are now being used in industry. For example, hundreds of robots navigate autonomously in Amazon fulfillment centers to move inventory pods all the way from their storage locations to the inventory stations that need the products they store (and vice versa). Autonomous aircraft towing vehicles will soon tow aircraft all the way from the runways to their gates (and vice versa), thereby reducing pollution, energy consumption, congestion, and human workload. Path planning for these robots is difficult, yet one must find high-quality collision-free paths for them in real-time. Shorter paths result in higher throughput or lower operating costs (since fewer robots are required). Prof. Koenig will discuss different versions of such multi-robot path-planning problems, algorithms for solving them, and their applications.

Prof. Koenig will also discuss a planning architecture that combines ideas from artificial intelligence and robotics. It makes use of a simple temporal network to post-process the output of a multi-robot path-planning algorithm in polynomial time to create a plan-execution schedule for robots that provides a guaranteed safety distance between them and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. This talk is suitable for audiences with some computer science background. A background in artificial intelligence or robotics is not necessary.

Bio: Sven Koenig is a professor in computer science at the University of Southern California.
Most of his research centers around techniques for decision making (planning and
learning) that enable single situated agents (such as robots or decision-support systems)
and teams of agents to act intelligently in their environments and exhibit goal-directed
behavior in real-time, even if they have only incomplete knowledge of their environment,
imperfect abilities to manipulate it, limited or noisy perception or insufficient
reasoning speed.