- Title
- Optimization of Robot Movements Using Genetic Algorithms and Simulation
- Creator
- Zahn, Brendon; Fountain, Jake; Houliston, Trent; Biddulph, Alexander; Chalup, Stephan; Mendes, Alexandre
- Relation
- 23rd Annual RoboCup International Symposium. Proceedings of 23rd Annual RoboCup International Symposium (Sydney, Australia 02-08 July, 2019) p. 466-475
- Publisher Link
- http://dx.doi.org/10.1007/978-3-030-35699-6_38
- Publisher
- Springer Nature
- Resource Type
- conference paper
- Date
- 2019
- Description
- This work describes the optimization of two robot movements in the context of the Humanoid league competition at RoboCup. A multi-objective genetic algorithm (MOGA) was used in conjunction with the real-time physics simulator Gazebo. The motivation for this work was that the NUbots team, from the University of Newcastle, lacked a simulation platform for their soccer-playing robots. Gazebo was the preferred choice of simulator, offering built-in compatibility with the Robot Operating System (ROS). The NUbots robot software, however, uses a proprietary message-passing framework in place of ROS. This work thus describes the pathway to use Gazebo with non-ROS compliant applications. In addition, it describes how MOGA can be used to optimize complex movements in an efficient manner. The two robot movements optimized were a kick script and the walk engine. For the kick script, the resulting optimal configuration improved the kick distance by a factor of six, with 50% less torso sway. For the walk engine, the forward speed increased by 50%, with 38% less torso sway, compared to the manually-tuned walk engine.
- Subject
- simulation; walk engine; optimization; multi-objective
- Identifier
- http://hdl.handle.net/1959.13/1460279
- Identifier
- uon:45916
- Identifier
- ISBN:9783030356989
- Language
- eng
- Reviewed
- Hits: 1057
- Visitors: 1054
- Downloads: 0