74 research outputs found
Ach: IPC for Real-Time Robot Control
We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license
The Motion Grammar: Linguistic Perception, Planning, and Control
We present the Motion Grammar: a novel unified representation
for task decomposition, perception, planning, and hybrid control
that provides a computationally tractable way to control robots in
uncertain environments with guarantees on completeness and correctness.
The grammar represents a policy for the task which is
parsed in real-time based on perceptual input. Branches of the syntax
tree form the levels of a hierarchical decomposition, and the
individual robot sensor readings are given by tokens. We implement
this approach in the interactive game of Yamakuzushi on a
physical robot resulting in a system that repeatably competes with
a human opponent in sustained game-play for matches up to six
minutes
Navigation Among Movable Obstacles: Real-Time Reasoning in Complex Environments
Electronic version of an article published as International Journal of Humanoid Robotics, Vol. 2, No. 4, December, 2005 pp. 479-504; DOI: 10.1142/S0219843605000545 ; © World Scientific Publishing Company ; http://www.worldscinet.com/ijhr/ijhr.shtmlIn 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
Optimized Control Strategies for Wheeled Humanoids and Mobile
Abstract-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
Correct Software Synthesis for Stable Speed-Controlled Robotic Walking
Presented at the 2013 Robotics: Science and Systems Conference VII (RSS), 24-28 June 2013, Berlin, Germany.We present a software synthesis method for speed-
controlled robot walking based on supervisory control of a
context-free Motion Grammar. First, we use Human-Inspired
control to identify parameters for fixed speed walking and for
transitions between fixed speeds, guaranteeing dynamic stability.
Next, we build a Motion Grammar representing the discrete-
time control for this set of speeds. Then, we synthesize C
code from this grammar and generate supervisors¹ online to
achieve desired walking speeds, guaranteeing correctness of
discrete computation. Finally, we demonstrate this approach on
the Aldebaran NAO, showing stable walking transitions with
dynamically selected speeds
Manipulation Planning Among Movable Obstacles.
© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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
Robot Jenga: Autonomous and Strategic Block Extraction
© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper describes our successful implementation
of a robot that autonomously and strategically removes
multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that
achieves repeatable performance in this challenging physical
domain. In contrast to previous implementations, we rely only
on low-cost, readily available system components and use
strategic algorithms to resolve system uncertainty. We present a
three-stage planner for block extraction which considers block
selection, extraction order, and physics-based simulation that
evaluates removability. Existing vision techniques are combined
in a novel sequence for the identification and tracking of blocks
within the tower. Discussion of our approach is presented
following experimental results on a 5-DOF robot manipulator
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