Decision Tree-Like Dynamic Conditional Stand-Up Routines for NAO Robots
摘要
A reliable stand-up movement is particularly important for humanoid robots. This becomes even more important in competitions such as the Standard Platform League (SPL), as a lying robot is an obstacle for other robots on the field and can neither effectively defend nor score a goal. Therefore, we have developed a system enabling the NAO robots to stand up reliably even with worn-out joints and on challenging floors. Here, we combine conditions influenced by the environment with an online-learned score for different parts of the stand-up movement. This enables us to stand up more reliably on a wide range of floor conditions.