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Erschienen in:
The DARPA Robotics Challenge Finals: Results and Perspectives Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Team RoboSimian: Semi-autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

verfasst von : Sisir Karumanchi, Kyle Edelberg, Ian Baldwin, Jeremy Nash, Brian Satzinger, Jason Reid, Charles Bergh, Chelsea Lau, John Leichty, Kalind Carpenter, Matthew Shekels, Matthew Gildner, David Newill-Smith, Jason Carlton, John Koehler, Tatyana Dobreva, Matthew Frost, Paul Hebert, James Borders, Jeremy Ma, Bertrand Douillard, Krishna Shankar, Katie Byl, Joel Burdick, Paul Backes, Brett Kennedy

Erschienen in: The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue

Verlag: Springer International Publishing

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Abstract

This article discusses hardware and software improvements to the RoboSimian system leading up to and during the 2015 DARPA Robotics Challenge (DRC) Finals. Team RoboSimian achieved a 5th place finish by achieving 7 points in 47:59 min. We present an architecture that was structured to be adaptable at the lowest level and repeatable at the highest level. The low-level adaptability was achieved by leveraging tactile measurements from force torque sensors in the wrist coupled with whole body motion primitives. We use the term “behaviors” to conceptualize this low-level adaptability. Each behavior is a contact-triggered state machine that enables execution of short order manipulation and mobility tasks autonomously. At a high level, we focused on a teach-and-repeat style of development by storing executed behaviors and navigation poses in object/task frame for recall later. This enabled us to perform tasks with high repeatability on competition day while being robust to task differences from practice to execution.

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Vorheriges Kapitel DRC Team NimbRo Rescue: Perception and Control for Centaur-Like Mobile Manipulation Robot Momaro
Nächstes Kapitel Director: A User Interface Designed for Robot Operation with Shared Autonomy
Fußnoten
1
Behaviors are executed in body frame but are stored in task frame for recall. When a task/object is fit in world frame, we can get the task-to-body transform based on current robot state in the world frame. The stored behaviors generate body frame constraints for motion planning.
 
2
At DRC finals, repeatability was at the task level which was considered high level (e.g. turn the valve with consistent trial-by-trial execution) and adaptability was at the body contact level (e.g. adapt to changing forces as the robot interacts physically).
 
3
We made a special effort to design the mobility and manipulation planners so that the processes on the robot and OCU side would never be unsynchronized. For example, the processes transmit plan requests and action requests over TCP to guarantee delivery over the wireless links and plan with the compressed robot state in order to be numerically consistent with the worst case state estimate on the remote side.
 
4
Does not include the body pose.
 
5
Body pose is specified in 6 dimensions; a 3D position vector and a 3D rotation vector in angle-scaled axis form.
 
6
By bounding the robot start and end states, one is able to avoid kinematic drift which can occur to due minor variations in the input parameters across multiple executions of a planner.
 
7
This enforces that the manipulation hand is in a conic field of view.
 
8
\(c_{pos}()\) is represented by the euclidean distance metric.
 
9
\(c_{orient}()\) is represented by \(cos({tol})-1 \le 0.5*Trace((R^{W}_1)^T R^{W}_2) -1 \le 0\) where \(R^{W}_1\) and \(R^{W}_2\) are two rotation matrices.
 
10
\(c_{gaze}()\) is represented by \(cos({tol})-1 \le (\frac{p_{tar}-p_{cam}}{\Vert p_{tar}-p_{cam}\Vert })^T(R^{W}_{cam}u^{cam}_{axis}) -1 \le 0\) where \(p_{tar}\) is position of the gaze target, \(p_{cam}\) is position of the camera, \(R^{W}_{cam}\) is the orientation of the camera in world frame and \(u^{cam}_{axis}\) is a desired gaze axis in camera frame.
 
11
\(c_{margin}()\) is represented by \(-\inf \le \{ -sign(n^T(p_{ee_{3}} - p_{ee_1}))*(n^T(p_{com} - p_{ee_2}))+margin \} \le 0\), where \(n = \frac{(p_{ee_1} - p_{ee_2}) \times n_{support\_plane}}{\Vert (p_{ee_1} - p_{ee_2}) \times n_{support\_plane}\Vert }\); \(p_{ee_1}\), \(p_{ee_2}\) and \(p_{ee_3}\) are positions of the end effectors on the boundary of the support polygon, \(p_{ee_3}\) is the furthest end effector from the limb that is moving, \(p_{com}\) is the position of the center of mass (COM), and finally margin specifies a fixed tolerance in the directional distance of the COM from the active boundary of the support polygon.
 
12
\(c_{res}()\) is represented by \((\theta -\theta _{prev})^T(\theta -\theta _{prev})\).
 
13
The timing data was derived from DARPA video casts on you tube (DARPA 2015b, a). Day 1 run of RoboSimian on the red course starts at https://​youtu.​be/​vgt6FPWU2Lc?​t=​17932, and we received our 7th point at https://​youtu.​be/​vgt6FPWU2Lc?​t=​20810 (DARPA 2015b). Day 2 run of RoboSimian on the blue course starts at https://​youtu.​be/​s6ZdC_​ZJXK8?​t=​35864, and we received our 7th point at https://​youtu.​be/​s6ZdC_​ZJXK8?​t=​39148 (DARPA 2015a).
 
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Metadaten
Titel
Team RoboSimian: Semi-autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals
verfasst von
Sisir Karumanchi
Kyle Edelberg
Ian Baldwin
Jeremy Nash
Brian Satzinger
Jason Reid
Charles Bergh
Chelsea Lau
John Leichty
Kalind Carpenter
Matthew Shekels
Matthew Gildner
David Newill-Smith
Jason Carlton
John Koehler
Tatyana Dobreva
Matthew Frost
Paul Hebert
James Borders
Jeremy Ma
Bertrand Douillard
Krishna Shankar
Katie Byl
Joel Burdick
Paul Backes
Brett Kennedy
Copyright-Jahr
2018
Buch
The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue

Print ISBN: 978-3-319-74665-4

Electronic ISBN: 978-3-319-74666-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-74666-1_6

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