(Georgia Institute of Technology, 2010-05)
Teeyapan, Kasemsit; Wang, Jiuguang; Kunz, Tobias; Stilman, Mike
We present successful control strategies for dynamically
stable robots that avoid low ceilings and other vertical
obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle
dimensions, our method uses a sequential composition of IO-linearized
controllers and applies stochastic optimization to automatically
compute the best controller gains and references, as
well as the times for switching between the different controllers.
We demonstrate this system through numerical simulations,
validation in a physics-based simulation environment, as well
as on a novel two-wheeled platform. The results show that the
generated control strategies are successful in mission planning
for this challenging problem domain and offer significant
advantages over hand-tuned alternatives.
(Georgia Institute of Technology, 2009-12)
Stilman, Mike; Wang, Jiuguang; Teeyapan, Kasemsit; Marceau, Ray
Optimizing the control of articulated mobile
robots leads to emergent behaviors that improve the effectiveness,
efficiency and stability of wheeled humanoids and
dynamically stable mobile manipulators. Our simulated results
show that optimization over the target pose, height and
control parameters results in effective strategies for standing,
acceleration and deceleration. These strategies improve system
performance by orders of magnitude over existing controllers.
This paper presents a simple controller for robot motion and
an optimization method for choosing its parameters. By using
whole-body articulation, we achieve new skills such as standing
and unprecedented levels of performance for acceleration and
deceleration of the robot base. We describe a new control
architecture, present a method for optimization, and illustrate
its functionality through two distinct methods of simulation.