21 research outputs found
Design and Steering Control of a Center-Articulated Mobile Robot Module
This paper discusses the design and steering control for an
autonomous modular mobile robot. The module is designed
with a center-articulated steering joint to minimize the number
of actuators used in the chain. We propose a feedback control
law which allows steering between configurations in the plane
and show its application as a parking control to dock modules
together. The control law is designed by Lyapunov techniques
and relies on the equations of the robot in polar coordinates.
A set of experiments have been carried out to show the
performance of the proposed approach. The design is intended
to endow individual wheeled modules with the capability to
merge and make a single snake-like robot to take advantage
of the benefits of modular robotics
Guaranteed Performance of Nonlinear Pose Filter on SE(3)
This paper presents a novel nonlinear pose filter evolved directly on the
Special Euclidean Group SE(3) with guaranteed characteristics of transient and
steady-state performance. The above-mention characteristics can be achieved by
trapping the position error and the error of the normalized Euclidean distance
of the attitude in a given large set and guiding them to converge
systematically to a small given set. The error vector is proven to approach the
origin asymptotically from almost any initial condition. The proposed filter is
able to provide a reliable pose estimate with remarkable convergence properties
such that it can be fitted with measurements obtained from low-cost measurement
units. Simulation results demonstrate high convergence capabilities and
robustness considering large error in initialization and high level of
uncertainties in measurements. Keywords: Pose, estimator, observer, attitude,
position, estimate, special orthogonal group, special Euclidean group,
prescribed performance, steady-state, transient response, homogeneous
transformation matrix, complimentary filter, mapping, Parameterization,
Representation, Robust, stability, uncertain, Gaussian, noise, vectorial
measurement, vector measurement, translational velocity, angular velocity,
singular value decomposition, rotational matrix, identity, deterministic,
comparison, inertial frame, rigid body, three dimensional, 3D, space, Lie
group, projection, landmark, feature, gyroscope, micro electromechanical
systems, Inertial measurement units, sensor, IMUs, Fixed, moving, orientation,
Roll, Pitch, Yaw, SVD, UAVs, QUAV, unmanned, underwater vehicle, robot, robotic
System, spacecraft, quadrotor, quadcopter, overview, autonomous, xyz, axis,
SO(3), SE(3).Comment: 2019 American Control Conference (ACC
Guaranteed Performance of Nonlinear Pose Filter on SE(3)
This paper presents a novel nonlinear pose filter evolved directly on the
Special Euclidean Group SE(3) with guaranteed characteristics of transient and
steady-state performance. The above-mention characteristics can be achieved by
trapping the position error and the error of the normalized Euclidean distance
of the attitude in a given large set and guiding them to converge
systematically to a small given set. The error vector is proven to approach the
origin asymptotically from almost any initial condition. The proposed filter is
able to provide a reliable pose estimate with remarkable convergence properties
such that it can be fitted with measurements obtained from low-cost measurement
units. Simulation results demonstrate high convergence capabilities and
robustness considering large error in initialization and high level of
uncertainties in measurements. Keywords: Pose, estimator, observer, attitude,
position, estimate, special orthogonal group, special Euclidean group,
prescribed performance, steady-state, transient response, homogeneous
transformation matrix, complimentary filter, mapping, Parameterization,
Representation, Robust, stability, uncertain, Gaussian, noise, vectorial
measurement, vector measurement, translational velocity, angular velocity,
singular value decomposition, rotational matrix, identity, deterministic,
comparison, inertial frame, rigid body, three dimensional, 3D, space, Lie
group, projection, landmark, feature, gyroscope, micro electromechanical
systems, Inertial measurement units, sensor, IMUs, Fixed, moving, orientation,
Roll, Pitch, Yaw, SVD, UAVs, QUAV, unmanned, underwater vehicle, robot, robotic
System, spacecraft, quadrotor, quadcopter, overview, autonomous, xyz, axis,
SO(3), SE(3).Comment: 2019 American Control Conference (ACC
Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests
© 2019 The Author(s) Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. Methods: Patients undergoing primary unilateral total knee arthroplasty (n = 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. Results: Patients separated into 2 clusters (n = 46 and n = 22) with significantly different test completion times (12.6 s vs 21.6 s, P \u3c .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P = .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P = .07). Features with the largest effect size between groups were distinguished as important functional parameters. Conclusion: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters