Optimization of Robot Movements Using Genetic Algorithms and Simulation

被引：1

作者：

Zahn, Brandon ^{[1
]}

Fountain, Jake ^{[1
]}

Houliston, Trent ^{[1
]}

Biddulph, Alexander ^{[1
]}

Chalup, Stephan ^{[1
]}

Mendes, Alexandre ^{[1
]}

机构：

[1] Univ Newcastle, Fac Engn & Built Environm, Sch Elect Engn & Comp, Callaghan, NSW 2308, Australia

来源：

ROBOT WORLD CUP XXIII, ROBOCUP 2019 | 2019年 / 11531卷

关键词：

Simulation; Walk engine; Optimization; Multi-objective;

D O I：

10.1007/978-3-030-35699-6_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：466 / 475

页数：10