Abstract |
Center of Mass (CoM) estimation realizes a crucial role in legged locomotion. Most walking
pattern generators and real-time gait stabilizers commonly assume that the CoM position
and velocity are available for feedback. In this thesis we present one of the first
3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme
fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended
Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external
forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain
and the body’s angular momentum rate and thus effectively couples the frontal with
the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing.
Nevertheless, it is common practice to transform the measurements to a world frame
of reference and estimate the CoM with respect to the world frame. Consequently, the
robot’s base and support foot pose are mandatory and need to be co-estimated. To this
end, we extend a well-established in literature floating mass estimator to account for the
support foot dynamics and fuse kinematic-inertial measurements with the Error State
Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In
such a way, a cascade state estimation scheme consisting of a base and a CoM estimator
is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ
Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic
drift caused by slippage during walking. Unfortunately, such measurements suffer
from outliers in a dynamic environment, since frequently it is assumed that only the
robot is inmotion and the world around is static. Thus, we introduce the Robust Gaussian
ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior
knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW
is robustified and is suitable for dynamic human environments. In order to reinforce further
research endeavors, SEROW is released to the robotic community as an open-source
ROS/C++ package.
Up to date control and state estimation schemes readily assume that feet contact status
is known a priori. Contact detection is an important and largely unexplored topic in
contemporary humanoid robotics research. In this thesis, we elaborate on a broader question:
in which gait phase is the robot currently in? To this end, we propose a holistic framework
based on unsupervised learning from proprioceptive sensing that accurately and efficiently
addresses this problem. More specifically, we robustly detect one of the three gaitphases,
namely Left Single Support (LSS), Double Support (DS), and Right Single Support (RSS) utilizing joint encoder, IMU, and F/T measurements. Initially, dimensionality reduction
with Principal Components Analysis (PCA) or autoencoders is performed to extract
useful features, obtain a compact representation, and reduce the noise. Next, clustering
is performed on the low-dimensional latent space with GaussianMixtureModels (GMMs)
and three dense clusters corresponding to the gait-phases are obtained. Interestingly, it is
demonstrated that the gait phase dynamics are low-dimensional which is another indication
pointing towards locomotion being a low dimensional skill. Accordingly, given that
the proposed framework utilizes measurements fromsensors that are commonly available
on humanoids nowadays, we offer the Gait-phase Estimation Module (GEM), an opensource
ROS/Python implementation to the robotic community.
SEROW and GEM have been quantitatively and qualitatively assessed in terms of accuracy
and efficiency both in simulation and under real-world conditions. Initially, a simulated
robot in MATLAB and NASA’s Valkyrie humanoid robot in ROS/Gazebo were employed
to establish the proposed schemes with uneven/rough terrain gaits. Subsequently,
the proposed schemes were integrated on a) the small size NAO humanoid robot v4.0 and
b) the adult size WALK-MAN v2.0 for experimental validation. With NAO, SEROW was implemented
on the robot to provide the necessary feedback for motion planning and realtime
gait stabilization to achieve omni-directional locomotion even on outdoor/uneven
terrains. Additionally, SEROW was used in footstep planning and also in Visual SLAM
with the same robot. Regarding WALK-MAN v2.0, SEROW was executed onboard with
kinematic-inertial and F/T data to provide base and CoM feedback in real-time. Furthermore,
VO has also been considered to correct the kinematic drift while walking and facilitate
possible footstep planning. GEM was also employed to estimate the gait phase in
WALK-MAN’s dynamic gaits.
Summarizing, a robust nonlinear state estimator is proposed for humanoid robot walking.
Nevertheless, this scheme can be readily extended to other type of legged robots such as quadrupeds, since they share the same fundamental principles.
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