Team RoboSimian: Semi-autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals
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- 作者：Sisir Karumanchi ; Kyle Edelberg ; Ian Baldwin ; Jeremy Nash ; Jason Reid ; Charles Bergh ; 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 ; Paul Backes ; Brett Kennedy ; Brian Satzinger ; Chelsea Lau ; Katie Byl ; Krishna Shankar and Joel Burdick
- 刊名：Journal of Field Robotics
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：34
- 期：2
- 页码：305-332
- 全文大小：3577K
- ISSN：1556-4967
文摘
This paper 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 an 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.