17,202 research outputs found
Ambulatory position and orientation tracking fusing magnetic and inertial sensing
This paper presents the design and testing of a portable magnetic system combined with miniature inertial sensors for ambulatory 6 degrees of freedom ( DOF) human motion tracking. The magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source. Based on the measured signals, a processor calculates the relative positions and orientations between source and sensor. Magnetic actuation requires a substantial amount of energy which limits the update rate with a set of batteries. Moreover, the magnetic field can easily be disturbed by ferromagnetic materials or other sources. Inertial sensors can be sampled at high rates, require only little energy and do not suffer from magnetic interferences. However, accelerometers and gyroscopes can only measure changes in position and orientation and suffer from integration drift. By combing measurements from both systems in a complementary Kalman filter structure, an optimal solution for position and orientation estimates is obtained. The magnetic system provides 6 DOF measurements at a relatively low update rate while the inertial sensors track the changes position and orientation in between the magnetic updates. The implemented system is tested against a lab-bound camera tracking system for several functional body movements. The accuracy was about 5 mm for position and 3 degrees for orientation measurements. Errors were higher during movements with high velocities due to relative movement between source and sensor within one cycle of magnetic actuation
Inertial sensor-based knee flexion/extension angle estimation
A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth’s magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed
On sensor fusion for airborne wind energy systems
A study on filtering aspects of airborne wind energy generators is presented.
This class of renewable energy systems aims to convert the aerodynamic forces
generated by tethered wings, flying in closed paths transverse to the wind
flow, into electricity. The accurate reconstruction of the wing's position,
velocity and heading is of fundamental importance for the automatic control of
these kinds of systems. The difficulty of the estimation problem arises from
the nonlinear dynamics, wide speed range, large accelerations and fast changes
of direction that the wing experiences during operation. It is shown that the
overall nonlinear system has a specific structure allowing its partitioning
into sub-systems, hence leading to a series of simpler filtering problems.
Different sensor setups are then considered, and the related sensor fusion
algorithms are presented. The results of experimental tests carried out with a
small-scale prototype and wings of different sizes are discussed. The designed
filtering algorithms rely purely on kinematic laws, hence they are independent
from features like wing area, aerodynamic efficiency, mass, etc. Therefore, the
presented results are representative also of systems with larger size and
different wing design, different number of tethers and/or rigid wings.Comment: This manuscript is a preprint of a paper accepted for publication on
the IEEE Transactions on Control Systems Technology and is subject to IEEE
Copyright. The copy of record is available at IEEEXplore library:
http://ieeexplore.ieee.org
RIDI: Robust IMU Double Integration
This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, or turning). Our algorithm regresses a velocity vector from
the history of linear accelerations and angular velocities, then corrects
low-frequency bias in the linear accelerations, which are integrated twice to
estimate positions. We have acquired training data with ground-truth motions
across multiple human subjects and multiple phone placements (e.g., in a bag or
a hand). The qualitatively and quantitatively evaluations have demonstrated
that our algorithm has surprisingly shown comparable results to full Visual
Inertial navigation. To our knowledge, this paper is the first to integrate
sophisticated machine learning techniques with inertial navigation, potentially
opening up a new line of research in the domain of data-driven inertial
navigation. We will publicly share our code and data to facilitate further
research
Differential correction and preliminary orbit determination for lunar satellite orbits
Differential correction and preliminary orbit calculation for lunar satellite orbit
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Independent analysis of the orbits of Pioneer 10 and 11
Independently developed orbit determination software is used to analyze the
orbits of Pioneer 10 and 11 using Doppler data. The analysis takes into account
the gravitational fields of the Sun and planets using the latest JPL
ephemerides, accurate station locations, signal propagation delays (e.g., the
Shapiro delay, atmospheric effects), the spacecrafts' spin, and maneuvers. New
to this analysis is the ability to utilize telemetry data for spin, maneuvers,
and other on-board systematic effects. Using data that was analyzed in prior
JPL studies, the anomalous acceleration of the two spacecraft is confirmed. We
are also able to put limits on any secondary acceleration (i.e., jerk) terms.
The tools that were developed will be used in the upcoming analysis of recently
recovered Pioneer 10 and 11 Doppler data files.Comment: 22 pages, 5 figures; accepted for publication in IJMP
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