State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds

Autor:	Hosseini, Mojtaba, Rodriguez, Diego, Behnke, Sven
Rok vydání:	2022
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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 . Comment: Accepted final version. IEEE International Robotic Computing (IRC), Naples, Italy, December 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.11390 Zobrazit plný text záznamu View this record from Arxiv