(Georgia Institute of Technology, 2008-11)
Stilman, Mike; Kuffner, James J.
This paper presents artificial constraints as a method for guiding heuristic
search in the computationally challenging domain of motion planning among
movable obstacles. The robot is permitted to manipulate unspecified obstacles in
order to create space for a path. A plan is an ordered sequence of paths for robot
motion and object manipulation. We show that under monotone assumptions, anticipating
future manipulation paths results in constraints on both the choice of
objects and their placements at earlier stages in the plan. We present an algorithm
that uses this observation to incrementally reduce the search space and quickly find
solutions to previously unsolved classes of movable obstacle problems. Our planner
is developed for arbitrary robot geometry and kinematics. It is presented with an
implementation for the domain of navigation among movable obstacles.
(Georgia Institute of Technology, 2007)
Stilman, Mike; Nishiwaki, Koichi; Kagami, Satoshi; Kuffner, James J.
This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain
of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of
an environment filled with various fixed and movable obstacles, and automatically plans a navigation
strategy to reach a desired goal location. The planned strategy consists of a sequence of walking
and compliant manipulation operations. It is executed by the robot with online feedback. We give
an overview of our NAMO system, as well as provide details of the autonomous planning, online
grasping and compliant hand positioning during dynamically-stable walking. Finally, we present
results of a successful implementation running on the Humanoid Robot HRP-2.
(Georgia Institute of Technology, 2005-12)
Stilman, Mike; Kuffner, James J.
In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO):
a practical extension to navigation for humanoids and other dexterous mobile robots.
The robot is permitted to reconfigure the environment by moving obstacles and clearing
free space for a path. This paper presents a resolution complete planner for a subclass
of NAMO problems. Our planner takes advantage of the navigational structure through
state-space decomposition and heuristic search. The planning complexity is reduced to
the difficulty of the specific navigation task, rather than the dimensionality of the multi-object
domain. We demonstrate real-time results for spaces that contain large numbers
of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.