748 research outputs found
Technology transfer: Transportation
The application of NASA derived technology in solving problems related to highways, railroads, and other rapid systems is described. Additional areas/are identified where space technology may be utilized to meet requirements related to waterways, law enforcement agencies, and the trucking and recreational vehicle industries
Evaluation of the propulsion control system of a planetary rover and design of a mast for an elevation scanning laser/multi-detector system
Vertical wheel loads, wheel speeds, and torque relationships are considered in the design of a propulsion system capable of responding to steering, slope climbing, and irregular local terrains. The system developed is applied to the RPI Mars roving vehicle. The mechanical system required to implement the elevation laser scanning/multidetector principle was the design and construction of a mechanical system for implementing the elevation scanning/multidetector principle is also discussed
Determination of the dynamic vehicle model parameters by means of computer vision
This study is devoted to determining the geometric, kinematic and dynamic characteristics of a vehicle. To this purpose, it is proposed to use a complex approach applying the models of deformable body mechanics for describing the oscillatory movements of a vehicle and the computer vision algorithms for processing a series of object images to determine the state parameters of a vehicle on the road. The model of the vehicle vertical oscillations is produced by means of the viscoelastic elements and the dry friction element that fully enough represent the behavior of the sprung masses. The introduced algorithms and models can be used as a part of a complex system for monitoring and controlling the road traffic. In addition, they can determine both the speed of the car and its dynamic parameters and the driving behavior of the individual drivers
Planetary rovers and data fusion
This research will investigate the problem of position estimation for planetary rovers.
Diverse algorithmic filters are available for collecting input data and transforming
that data to useful information for the purpose of position estimation process. The
terrain has sandy soil which might cause slipping of the robot, and small stones and
pebbles which can affect trajectory.
The Kalman Filter, a state estimation algorithm was used for fusing the sensor data
to improve the position measurement of the rover. For the rover application the
locomotion and errors accumulated by the rover is compensated by the Kalman
Filter. The movement of a rover in a rough terrain is challenging especially with
limited sensors to tackle the problem. Thus, an initiative was taken to test drive
the rover during the field trial and expose the mobile platform to hard ground and
soft ground(sand). It was found that the LSV system produced speckle image and
values which proved invaluable for further research and for the implementation of
data fusion.
During the field trial,It was also discovered that in a at hard surface the problem
of the steering rover is minimal. However, when the rover was under the influence
of soft sand the rover tended to drift away and struggled to navigate.
This research introduced the laser speckle velocimetry as an alternative for odometric
measurement. LSV data was gathered during the field trial to further simulate under
MATLAB, which is a computational/mathematical programming software used for
the simulation of the rover trajectory. The wheel encoders came with associated
errors during the position measurement process. This was observed during the
earlier field trials too. It was also discovered that the Laser Speckle Velocimetry
measurement was able to measure accurately the position measurement but at the
same time sensitivity of the optics produced noise which needed to be addressed as
error problem.
Though the rough terrain is found in Mars, this paper is applicable to a terrestrial
robot on Earth. There are regions in Earth which have rough terrains and regions
which are hard to measure with encoders. This is especially true concerning icy
places like Antarctica, Greenland and others.
The proposed implementation for the development of the locomotion system is to
model a system for the position estimation through the use of simulation and collecting data using the LSV. Two simulations are performed, one is the differential
drive of a two wheel robot and the second involves the fusion of the differential drive
robot data and the LSV data collected from the rover testbed. The results have
been positive. The expected contributions from the research work includes a design
of a LSV system to aid the locomotion measurement system.
Simulation results show the effect of different sensors and velocity of the robot. The
kalman filter improves the position estimation process
Accurate vehicle classification including motorcycles using piezoelectric sensors
Thesis (M.S. ECE)--University of Oklahoma, 2012.Includes bibliographical references (leaves 88-90).State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifier devices currently employ inductive loops, piezoelectric sensors, or some combination of both, to aid in the identification of 13 Federal Highway Administration (FHWA) classifications. However, systems using inductive loops have proven unable to accurately classify motorcycles and record pertinent data. Previous investigations undertaken to overcome this problem have focused on classification techniques utilizing inductive loops signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. Most were off-line classification studies with results not directly intended for product development. Vision, infrared, and acoustic classification systems among others have also been explored as possible solutions.
This thesis presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally on the roadway to accurately identify motorcycles from among other vehicles, as well as identify vehicles in the remaining 12 FHWA classifications. An algorithm was formulated and deployed in an embedded system for field testing. Both single element and multi-element piezoelectric sensors were investigated for use as part of the vehicle classification system.
The piezoelectric sensors and vehicle classification system reported in this thesis were subsequently tested at the University of Oklahoma-Tulsa campus. Various vehicle types traveling at limited vehicle speeds were investigated. The newly developed vehicle classification system demonstrated results that met expectation for accurately identifying motorcycles
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