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Institution

Willow Garage

About: Willow Garage is a based out in . It is known for research contribution in the topics: Robot & Mobile robot. The organization has 76 authors who have published 191 publications receiving 28617 citations.

Papers published on a yearly basis

Papers
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Book ChapterDOI
01 Jan 2014
TL;DR: An extendable framework that combines measurements from the robot's various sensors (proprioceptive and external) to calibrate the robot’s joint offsets and external sensor locations is proposed, allowing sensors with very different error characteristics to be used side by side in the calibration.
Abstract: Complex robots with multiple arms and sensors need good calibration to perform precise tasks in unstructured environments. The sensors must be calibrated both to the manipulators and to each other, since fused sensor data is often needed. We propose an extendable framework that combines measurements from the robot’s various sensors (proprioceptive and external) to calibrate the robot’s joint offsets and external sensor locations. Our approach is unique in that it accounts for sensor measurement uncertainties, thereby allowing sensors with very different error characteristics to be used side by side in the calibration. The framework is general enough to handle complex robots with kinematic components, including external sensors on kinematic chains. We validate the framework by implementing it on the Willow Garage PR2 robot, providing a significant improvement in the robot’s calibration.

106 citations

Journal IssueDOI
TL;DR: The main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control are described.
Abstract: The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. © 2008 Wiley Periodicals, Inc.

103 citations

Proceedings ArticleDOI
04 Jun 2012
TL;DR: A multi-robot collision avoidance system based on the velocity obstacle paradigm that alleviates the strong requirement for perfect sensing using Adaptive Monte-Carlo Localization on a per-agent level and combines the computation for collision-free motion with localization uncertainty.
Abstract: This paper describes a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level. While such methods as Optimal Reciprocal Collision Avoidance guarantee local collision-free motion for a large number of robots, given perfect knowledge of positions and speeds, a realistic implementation requires further extensions to deal with inaccurate localization and message passing delays. The presented algorithm bounds the error introduced by localization and combines the computation for collision-free motion with localization uncertainty. We provide an open source implementation using the Robot Operating System (ROS). The system is tested and evaluated with up to eight robots in simulation and on four differential drive robots in a real-world situation.

103 citations

Proceedings ArticleDOI
03 May 2010
TL;DR: This paper shows how to overcome the high-dimensionality of the planning problem by identifying a graph-based representation that is small enough for efficient planning yet rich enough to contain feasible motions that open doors.
Abstract: Computing a motion that enables a mobile manipulator to open a door is challenging because it requires tight coordination between the motions of the arm and the base. Hard-coding the motion, on the other hand, is infeasible since doors vary widely in their sizes and types, some doors are opened by pulling and others by pushing, and indoor spaces often contain obstacles that limit the freedom of the mobile manipulator and the degree to which the doors open up. In this paper, we show how to overcome the high-dimensionality of the planning problem by identifying a graph-based representation that is small enough for efficient planning yet rich enough to contain feasible motions that open doors. The use of graph search-based motion planning enables us to handle consistently the wide variance of conditions under which doors need to be open. We demonstrate our approach on the PR2 robot - a mobile manipulator with an omnidirectional base and a 7 degree of freedom arm. The robot was successful in opening a variety of doors both by pulling and pushing.

102 citations

Journal ArticleDOI
TL;DR: This work presents two methods for generating time-extended coordination solutions—solutions where more than one task is assigned to each agent—for domains with intra-path constraints and compares these approaches with a range of single task allocation approaches in a simulated disaster response domain.
Abstract: Many applications require teams of robots to cooperatively execute tasks. Among these domains are those in which successful coordination must respect intra-path constraints, which are constraints that occur on the paths of agents and affect route planning. This work focuses on multi-agent coordination for disaster response with intra-path precedence constraints, a compelling application that is not well addressed by current coordination methods. In this domain a group of fire truck agents attempt to address fires spread throughout a city in the wake of a large-scale disaster. The disaster has also caused many city roads to be blocked by impassable debris, which can be cleared by bulldozer robots. A high-quality coordination solution must determine not only a task allocation but also what routes the fire trucks should take given the intra-path precedence constraints and which bulldozers should be assigned to clear debris along those routes. This work presents two methods for generating time-extended coordination solutions--solutions where more than one task is assigned to each agent--for domains with intra-path constraints. Our first approach uses tiered auctions and two heuristic techniques, clustering and opportunistic path planning, to perform a bounded search of possible time-extended schedules and allocations. Our second method uses a centralized, non-heuristic, genetic algorithm-based approach that provides higher quality solutions but at substantially greater computational cost. We compare our time-extended approaches with a range of single task allocation approaches in a simulated disaster response domain.

97 citations


Authors

Showing all 76 results

NameH-indexPapersCitations
Ian Goodfellow85137135390
Kurt Konolige6417124749
Andreas Paepcke501409405
Gunter Niemeyer4715317135
Radu Bogdan Rusu439715008
Mike J. Dixon421828272
Gary Bradski418223763
Leila Takayama34904549
Sachin Chitta34564589
Wendy Ju341843861
Maya Cakmak341114452
Brian P. Gerkey32517923
Caroline Pantofaru26654116
Matei Ciocarlie25913176
Kaijen Hsiao24292366
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20172
20164
20152
201414
201336
201239