61 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
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 supervisorsÂą 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
Kinematics and Inverse Kinematics for the Humanoid Robot HUBO2+
This paper derives the forward and inverse kinematics
of a humanoid robot. The specific humanoid that the
derivation is for is a robot with 27 degrees of freedom but
the procedure can be easily applied to other similar humanoid
platforms. First, the forward and inverse kinematics are derived
for the arms and legs. Then, the kinematics for the torso and
the head are solved. Finally, the forward and inverse kinematic
solutions for the whole body are derived using the kinematics of
arms, legs, torso, and head
Global Manipulation Planning in Robot Joint Space With Task Constraints
© 2010 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.Stilman, M.; "Global Manipulation Planning in Robot Joint Space With Task Constraints," Robotics, IEEE Transactions on , vol.26, no.3, pp.576-584, June 2010
doi: 10.1109/TRO.2010.2044949
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5467152&isnumber=5477213We explore global randomized joint space path planning
for articulated robots that are subject to task space constraints. This
paper describes a representation of constrained motion for joint space
planners and develops two simple and efficient methods for constrained
sampling of joint configurations: Tangent Space Sampling (TS) and
First-Order Retraction (FR). FR is formally proven to provide global
sampling for linear task space transformations. Constrained joint space
planning is important for many real world problems involving redundant
manipulators. On the one hand, tasks are designated in work space
coordinates: rotating doors about fixed axes, sliding drawers along fixed
trajectories or holding objects level during transport. On the other, joint
space planning gives alternative paths that use redundant degrees of
freedom to avoid obstacles or satisfy additional goals while performing
a task. We demonstrate that our methods are faster and more invariant
to parameter choices than existing techniques
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