(Georgia Institute of Technology, 2010-10)
Kunz, Tobias; Reiser, Ulrich; Stilman, Mike; Verl, Alexander
We present a practical strategy for real-time path
planning for articulated robot arms in changing environments
by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies
bottlenecks in the algorithm and introduces new methods that
solve the overall task of detecting obstacles and planning a path
around them in under 100 ms.
A fast planner is necessary to enable the robot to react
to quickly changing human environments. We have tested
our implementation in real-world experiments where a human
subject enters the manipulation area, is detected and safely
avoided by the robot. This capability is critical for future
applications in automation and service robotics where humans
will work closely with robots to jointly perform tasks.
(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.