8,946 research outputs found
State Estimation for a Humanoid Robot
This paper introduces a framework for state estimation on a humanoid robot
platform using only common proprioceptive sensors and knowledge of leg
kinematics. The presented approach extends that detailed in [1] on a quadruped
platform by incorporating the rotational constraints imposed by the humanoid's
flat feet. As in previous work, the proposed Extended Kalman Filter (EKF)
accommodates contact switching and makes no assumptions about gait or terrain,
making it applicable on any humanoid platform for use in any task. The filter
employs a sensor-based prediction model which uses inertial data from an IMU
and corrects for integrated error using a kinematics-based measurement model
which relies on joint encoders and a kinematic model to determine the relative
position and orientation of the feet. A nonlinear observability analysis is
performed on both the original and updated filters and it is concluded that the
new filter significantly simplifies singular cases and improves the
observability characteristics of the system. Results on simulated walking and
squatting datasets demonstrate the performance gain of the flat-foot filter as
well as confirm the results of the presented observability analysis.Comment: IROS 2014 Submission, IEEE/RSJ International Conference on
Intelligent Robots and Systems (2014) 952-95
Monocular SLAM for a Small-size Humanoid Robot
100學年度研究獎補助論文[[abstract]]The paper presents a algorithm of visual simultaneous localization and mapping (vSLAM) for a small-size humanoid robot. The algorithm includes the procedures of image feature detection, good feature selection, image depth calculation, and feature state estimation. To ensure robust feature detection and tracking, the procedure is improved by utilizing the method of Speeded Up Robust Features (SURF). Meanwhile, the procedures of image depth calculation and state estimation are integrated in an extended Kalman filter (EKF) based estimation algorithm. All the computation schemes of the visual SLAM are implemented on a small-size humanoid robot with low-cost Window-based PC. Experimentation is performed and the results show that the performance of the proposed algorithm is efficient for robot visual SLAM in the environments.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙
Outlier-Robust State Estimation for Humanoid Robots*
Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++package
Humanoid Momentum Estimation Using Sensed Contact Wrenches
This work presents approaches for the estimation of quantities important for
the control of the momentum of a humanoid robot. In contrast to previous
approaches which use simplified models such as the Linear Inverted Pendulum
Model, we present estimators based on the momentum dynamics of the robot. By
using this simple yet dynamically-consistent model, we avoid the issues of
using simplified models for estimation. We develop an estimator for the center
of mass and full momentum which can be reformulated to estimate center of mass
offsets as well as external wrenches applied to the robot. The observability of
these estimators is investigated and their performance is evaluated in
comparison to previous approaches.Comment: Submitted to the 15th IEEE RAS Humanoids Conference, to be held in
Seoul, Korea on November 3 - 5, 201
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
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