Contributors:
Autonomous Systems Lab
Robotic Systems Lab
Home page - rsl.ethz.ch/robots-media/starleth.html
Contributors:
Autonomous Systems Lab
Robotic Systems Lab
Home page - rsl.ethz.ch/robots-media/starleth.html
StarlETH 3D trotting on treadmill with obstacles
Published on Aug 24, 2012
Trotting experiments with StarlETH on a treadmill with up to 0.7m/s (2.5km/h) over small obstacles. This quadrupedal robot (24kg, 0.2m segment lenght) is driven by 12 series elastic torque actuators and works with onboard state estimation. An external motion capture system is used to continuously adapt the the treadmill velocity.
walking and running experiments with the quadruped robot StarlETH
Published on Jan 3, 2013
This movie summarizes some achievements with our quadrupedal robot StarlETH. This machine is driven by 12 high compliant series elastic acutators. The control strategy for these sequences is based on hierarchical task-space inverse dynamics.
Published on Jun 5, 2013
StarlETH was demonstrated at the IEEE International Conference on Robotics and Automation (ICRA) in Karlsruhe, Germany. StarlETH is a electrically driven quadruped robot able to cope with unperceived obstacles with several centimeters in height. StarlETH is in active development at the Autonomous Systems Lab at ETH Zurich, Switzerland.
Detection of Slippery Terrain with a Heterogeneous Team of Legged Robots
Published on Feb 15, 2014
See Detection of slippery terrain with a heterogeneous team of legged robots, UC Berkeley and ETH ZurichThis video shows a heterogneous team of legged robots conducting a joint locomotion and perception task. StarlETH, a large and highly capable quadruped uses the VelociRoACH as a remote probe to detect regions of slippery terrain. StarlETH localizes the VelociRoACH using internal state estimation (IMU and leg kinematics) and visual tracking (ARTag). While StarlETH is remote controlled, the position and orientation of the VelociRoACH is feedback controlled to stay at a desired position in front of StarlETH. A Support Vector Machines (SVM) is used to identify slippery regions with the VelociRoACH. The data for the SVM is based on data from both the external observation from StarlETH and the internal sensory data of VelociRoACH. The slippage classifier is able to detect slippery spots with 92% (125/135) accuracy using of only four features from the available data.
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