Name
Abstract | ||
|---|---|---|
In this paper, we describe the Evo-ROS framework, which is intended to help bridge the gap between the evolutionary and traditional robotics communities. Evo-ROS combines an evolutionary algorithm with individual physics-based evaluations conducted using the Robot Operating System (ROS) and the Gazebo simulation environment. Our goals in developing Evo-ROS are to (1) provide researchers in evolutionary robotics with access to the extensive support for real-world components and capabilities developed by the ROS community and (2) enable ROS developers, and more broadly robotics researchers, to take advantage of evolutionary search during design and testing. We describe the details of the Evo-ROS structure and operation, followed by presentation of a case study using Evo-ROS to optimize placement of sonar sensors on unmanned ground vehicles that can experience reduced sensing capability due to component failures and physical damage. The case study provides insights into the current capabilities and identifies areas for future enhancements.
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Year | Venue | Field |
|---|---|---|
2018 | GECCO (Companion) | Systems engineering,Evolutionary algorithm,Evolutionary robotics,Computer science,Sonar,Fault tolerance,Artificial intelligence,Ground vehicles,Machine learning,Robotics,Robot operating system |
DocType | ISBN | Citations |
Conference | 978-1-4503-5764-7 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Glen A. Simon | 1 | 0 | 0.68 |
| Jared M. Moore | 2 | 28 | 6.82 |
| Anthony J. Clark | 3 | 22 | 5.49 |
| P. K. McKinley | 4 | 1397 | 121.87 |