PhD Defense by Michael X. Grey

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  • 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



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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Graduate Studies

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