(Georgia Institute of Technology, 2013-06)
Dantam, Neil; Stilman, Mike
We present the Motion Grammar: an approach to
represent and verify robot control policies based on Context-Free
Grammars. The production rules of the grammar represent a
top-down task decomposition of robot behavior. The terminal
symbols of this language represent sensor readings that are
parsed in real-time. Efficient algorithms for context-free parsing
guarantee that online parsing is computationally tractable. We
analyze verification properties and language constraints of this
linguistic modeling approach, show a linguistic basis that unifies
several existing methods, and demonstrate effectiveness through
experiments on a 14-DOF manipulator interacting with 32 objects
(chess pieces) and an unpredictable human adversary. We provide
many of the algorithms discussed as Open Source, permissively
licensed software. ¹
(Georgia Institute of Technology, 2010-06)
Stilman, Mike
We explore global randomized joint space path planning
for articulated robots that are subject to task space constraints. This
paper describes a representation of constrained motion for joint space
planners and develops two simple and efficient methods for constrained
sampling of joint configurations: Tangent Space Sampling (TS) and
First-Order Retraction (FR). FR is formally proven to provide global
sampling for linear task space transformations. Constrained joint space
planning is important for many real world problems involving redundant
manipulators. On the one hand, tasks are designated in work space
coordinates: rotating doors about fixed axes, sliding drawers along fixed
trajectories or holding objects level during transport. On the other, joint
space planning gives alternative paths that use redundant degrees of
freedom to avoid obstacles or satisfy additional goals while performing
a task. We demonstrate that our methods are faster and more invariant
to parameter choices than existing techniques.