论文标题
国家对驾驶驾驶四足动物的杂种运动的估计
State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds
论文作者
论文摘要
可以通过在平坦的地形上迅速驱动的四倍的机器人可以实现快速，多才多艺的运动，但也能够通过适应其身体姿势和做出步骤来克服挑战的地形。在本文中，我们为四足机器人提供了一种状态估计方法，该机器人具有不可运动的车轮，可实现混合驾驶运动功能。我们为状态估计制定了一个Kalman滤波器（KF），该状态估计将驱动的车轮集成到滤波器方程中，并估算机器人状态（位置和速度）以及使用车轮对上述状态的驾驶贡献。我们的估计方法使我们能够使用微小修改的Mini Cheetah四倍机器人的控制框架。我们测试了我们在模拟和现实世界中以积极驱动的轮子进行积极驱动的车轮增强的方法。实验结果可在https://www.ais.uni-bonn.de/%7ehosseini/se-dsq上获得。
Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotion capabilities. We formulate a Kalman Filter (KF) for state estimation that integrates driven wheels into the filter equations and estimates the robot state (position and velocity) as well as the contribution of driving with wheels to the above state. Our estimation approach allows us to use the control framework of the Mini Cheetah quadruped robot with minor modifications. We tested our approach on this robot that we augmented with actively driven wheels in simulation and in the real world. The experimental results are available at https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq .
