The development of a humanoid robot that mimics human motion requires extensive programming as well as understanding the motion limitations of the robot. Programming the countless possibilities for a robot’s response to observed human motion can be time consuming. To simplify this process, this thesis presents a new approach for mimicking captured human motion data through the development of a composition routine. This routine is built upon a behavior-based framework and is coupled with optimization by calculus to determine the appropriate weightings of predetermined motion behaviors. The completion of this thesis helps to fill a void in human/robot interactions involving mimicry and behavior-based design. Technological advancements in the way computers and robots identify human motion and determine for themselves how to approximate that motion have helped make possible the mimicry of observed human subjects. In fact, many researchers have developed humanoid systems that are capable of mimicking human motion data; however, these systems do not use behavior-based design. This thesis will explain the framework and theory behind our optimal behavior composition algorithm and the selection of sinusoidal motion primitives that make up a behavior library. This algorithm breaks captured motion data into various time intervals, then optimally weights the defined behaviors to best approximate the captured data. Since this routine does not reference previous or following motion sequences, discontinuities may exist between time intervals. To address this issue, the addition of a PI controller to regulate and smooth out the transitions between time intervals will be shown. The effectiveness of using the optimal behavior composition algorithm to create an approximated motion that mimics capture motion data will be demonstrated through an example configuration of hardware and a humanoid robot platform. An example of arm motion mimicry will be presented and includes various image sequences from the mimicry as well as trajectories containing the joint positions for both the human and the robot.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/51872 |
| Date | 22 May 2014 |
| Creators | Bartholomew, Paul D. |
| Contributors | Egerstedt, Magnus |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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