PhD Defense by Michael X. Grey

Event Details
  • Date/Time:
    • Tuesday June 20, 2017
      12:00 pm - 2:00 pm
  • Location: TSRB 222
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Summary Sentence: High Level Decomposition for Bipedal Locomotion Planning

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Michael X. Grey

Robotics Ph.D. Candidate

School of Aerospace Engineering

Georgia Institute of Technology

 

Title: High Level Decomposition for Bipedal Locomotion Planning

Date: Tuesday, June 20th, 2017

Time: 12:00pm EDT

Location: TSRB 222

 

Committee Members:

Dr. C. Karen Liu (Advisor), School of Interactive Computing, Georgia Institute of Technology

Dr. Aaron D. Ames (Advisor), Mechanical and Civil Engineering & Control and Dynamical Systems, California Institute of Technology

Dr. Magnus Egerstedt, School of Electrical and Computer Engineering, Georgia Institute of Technology

Dr. Kris Hauser, Department of Electrical and Computer Engineering & Department of Mechanical Engineering and Computer Science & Department of Computer Science, Duke University

Dr. Matt Zucker, Engineering Department, Swarthmore University

 

Summary:

Legged robotic platforms offer an attractive potential for deployment in hazardous scenarios that would be too dangerous for human workers. Legs provide a robot with the ability to step over obstacles and traverse steep, uneven, or narrow terrain. Such conditions are common in dangerous environments, such as a collapsing building or a nuclear facility during a meltdown. However, identifying the physical motions that a legged robot needs to perform in order to move itself through such an environment is particularly challenging. A human operator may be able to manually design such a motion on a case-by-case basis, but it would be inordinately time-consuming and unsuitable for real-world deployment.

 

This thesis presents a method to decompose challenging large-scale motion planning problems into a high-level planning problem and a set of parallel low-level planning problems. We apply the method to quasi-static bipedal locomotion planning. The method is tested in a series of simulated environments that are designed to reflect some of the challenging geometric features that a robot may face in a disaster scenario. We analyze the improvement in performance that is provided by the high- and low-level decomposition, and we show that completeness is not lost by this decomposition.

 

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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jun 12, 2017 - 1:58pm
  • Last Updated: Jun 12, 2017 - 1:58pm