A Novel Approach to Planning for Physical Systems

Dr. Ladd is from Rice University
Over the last decade, motion planning algorithms have been
used to solve complex geometric problems and have
contributed to advances in industrial automation, service
robots and computer-assisted design of mechanisms. However,
some of the most exciting applications for motion planning,
such as surgical robots, humanoid robots and autonomous exploration
vehicles are beyond the limits of current planning techniques. The
fundamental reason for this gap is that motion planning
algorithms typically do not explicitly consider the physics
of robot motion. In contrast, predictive models for
mechanical systems have become quite good.
There are now many accurate and efficient software simulation software
packages available and a corresponding need for planning techniques
capable of leveraging them.

This talk will discuss recent work that has led to a novel
algorithm that seamlessly combines geometry and physics.
The geometry aspect of the problem is addressed with
a combination of sampling and subdivision methods.
The implementation uses a general purpose physical simulator for
Stewart-Trinkle rigid body dynamics to model
contact, friction and arbitrary kinematic constraints.
The effectiveness of the planner has been demonstrated in
a variety of studies including lifting a heavy weight
with an articulated limb and driving a realistic car.
Some theoretical aspects of the planner will be discussed as
well as its implications to robotics, graphics, artificial
intelligence and, more generally, our capability to
compute in the physical world.
Andrew Ladd is finishing his Ph.D. at Rice University. He is
interested in algorithmic robotics which broadly studies how
computers reason about physical systems. His work combines
theoretical foundations with engineering practice.