Optimization of Robot Movements Using Genetic Algorithms and Simulation

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.