(Georgia Institute of Technology, 2008)
van den Berg, Jur; Stilman, Mike; Kuffner, James; Lin, Ming; Manocha, Dinesh
In this paper we study the problem of path planning among movable
obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation that we can decouple
the computations of the robot motions and the obstacle movements, and present a
probabilistically complete algorithm, something which to date has not been achieved
for this problem. Our algorithm maintains an explicit representation of the robot's
configuration space. We present an efficient implementation for the case of planar,
axis-aligned environments and report experimental results on challenging scenarios.
(Georgia Institute of Technology, 2007-04)
Stilman, Mike; Schamburek, Jan-Ullrich; Kuffner, James; Asfour, Tamin
This paper presents the ResolveSpatialConstraints
(RSC) algorithm for manipulation planning in a domain with
movable obstacles. Empirically we show that our algorithm
quickly generates plans for simulated articulated robots in a
highly nonlinear search space of exponential dimension. RSC
is a reverse-time search that samples future robot actions and
constrains the space of prior object displacements. To optimize
the efficiency of RSC, we identify methods for sampling object
surfaces and generating connecting paths between grasps and
placements. In addition to experimental analysis of RSC, this
paper looks into object placements and task-space motion constraints
among other unique features of the three dimensional
manipulation planning domain.
(Georgia Institute of Technology, 2007)
Kagami, Satoshi; Nishiwaki, K.; Kuffner, James; Thompson, S.; Chestnutt, J.; Stilman, Mike; Michel, P.
Recently, research on humanoid-type robots has become increasingly active, and a broad array of fundamental issues are under investigation. However, in order to achieve a humanoid robot which can operate in human environments, not only the fundamental components themselves, but also the successful integration of these components will be required. At present, almost all humanoid robots that have been developed have been designed for bipedal locomotion experiments. In order to satisfy the functional demands of locomotion as well as high-level behaviors, humanoid robots require good mechanical design, hardware, and software which can support the integration of tactile sensing, visual perception, and motor control. Autonomous behaviors are currently still very primitive for humanoid-type robots. It is difficult to conduct research on high-level autonomy and intelligence in humanoids due to the development and maintenance costs of the hardware. We believe low-level autonomous functions will be required in order to conduct research on higher-level autonomous behaviors for humanoids.