• Haldunk.TRO2006

Haldun Komsuoglu





Kod*lab Menu

Internal Links (Login Required)

<< Komsuoglu Publications << Kod*lab Publications

Sensor Data Fusion for Body State Estimation in a Hexapod Robot With Dynamical Gaits

IEEE Transactions on Robotics, 2006

Lin, P-C. and Komsuoglu, H. and Koditschek, D. E.
Department of Electrical and Systems Engineering, University of Pennsylvania

Full PDF | Penn ScholarlyCommons | IEEE Xplore

We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robotís body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences.
BibTeX entry
  author = {Lin, P-C. and Komsuoglu, H. and Koditschek, D. E.},
  title = {Sensor Data Fusion for Body State Estimation in a Hexapod Robot With Dynamical Gaits},
  journal = {IEEE Transactions on Robotics},
  year = {2006},
  volume = {22},
  pages = {932-943},
  number = {5},
  month = {October}
Related Sites
RHex - Robotic Hexapod platform and research page.
LegNet? - Short range wireless sensor network.

wordpress stat

Copyright Kodlab, 2017