Creating systems that can operate autonomously in complex environments is a challenge for contemporary engineering techniques. Automatic design methods offer a promising alternative, but so far they have not been able to produce agents that outperform manual designs. One such method is evolutionary robotics. It has been shown to be a robust and versatile tool for designing robots to perform simple tasks, but more challenging tasks at present remain out of reach of the method.
In this thesis I discuss and attack some problems underlying the scalability issues associated with the method. I present a new technique for evolving modular networks. I show that the performance of modularity-biased evolution depends heavily on the morphology of the robot’s body and present a new method for co-evolving morphology and modular control.
To be able to reason about the new technique I develop reformulation framework: a general way to describe and reason about metaoptimization approaches. Within this framework I describe a new heuristic for developing metaoptimization approaches that is based on the technique for co-evolving morphology and modularity. I validate the framework by applying it to a practical task of zero-g autonomous assembly of structures with a fleet of small robots.
Although this work focuses on the evolutionary robotics, methods and approaches developed within it can be applied to optimization problems in any domain.
| Identifer | oai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-1957 |
| Date | 01 January 2018 |
| Creators | Bernatskiy, Anton |
| Publisher | ScholarWorks @ UVM |
| Source Sets | University of Vermont |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate College Dissertations and Theses |
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