(Georgia Institute of Technology, 2011-05)
Kunz, Tobias; Kingston, Peter; Stilman, Mike; Egerstedt, Magnus B.
We introduce and experimentally validate a novel
algorithmic model for physical human-robot interaction with
hybrid dynamics. Our computational solutions are complementary
to passive and compliant hardware. We focus on the case
where human motion can be predicted. In these cases, the
robot can select optimal motions in response to human actions
and maximize safety. By representing the domain as a Markov
Game, we enable the robot to not only react to the human but
also to construct an infinite horizon optimal policy of actions
and responses. Experimentally, we apply our model to simulated
robot sword defense. Our approach enables a simulated 7-DOF
robot arm to block known attacks in any sequence. We generate
optimized blocks and apply game theoretic tools to choose the
best action for the defender in the presence of an intelligent
adversary.
(Georgia Institute of Technology, 2011)
Kunz, Tobias; Stilman, Mike
This paper presents a method to generate the time-optimal
trajectroy that exactly follows a given differentiable
joint-space path within given bounds on joint accelerations and
velocities. We also present a path preprocessing method to make
nondifferentiable paths differentiable by adding circular blends.
(Georgia Institute of Technology, 2011)
Kunz, Tobias; Stilman, Mike
We present an approach for converting a path
of multiple continuous linear segments into a trajectory that
satisfies velocity and acceleration constraints and closely follows
the given path without coming to a complete stop at every
waypoint. Our method applies parabolic blends around waypoints
to improve speed. In contrast to established methods that
smooth trajectories with parabolic blends, our method does not
require the timing of waypoints or durations of blend phases.
This makes our approach particularly useful for robots that
must follow kinematic paths that are not explicitly parametrized
by time. Our method chooses timing automatically to achieve
high performance while satisfying the velocity and acceleration
constraints of a given robot.