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Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
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Mobile Paving System (MPS): A New Large Scale Freeform Fabrication Method
In the last decade, significant opportunities for automation have been identified in the area of
construction. Soaring labor and material costs have driven multiple research efforts in
construction automation. In this paper, we present a novel means for construction automation
that involves the fusion of the rapid prototyping, controls and mechatronics technologies. The
resultant autonomous construction mechanism has been designed for commercial applications.
Mobile Paving System (MPS) is a new freeform fabrication process which is capable of rapidly
producing variable profiles such as curbs and sidewalks out of materials like cement and asphalt.
Path generation and guidance of the construction operation is controlled by a mobile robot. This
article presents an overview of research and development efforts that are aimed at establishing
the feasibility and the potential of the process.Mechanical Engineerin
FLAT2D: Fast localization from approximate transformation into 2D
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment
Analysing the effects of sensor fusion, maps and trust models on autonomous vehicle satellite navigation positioning
This thesis analyzes the effects of maps, sensor fusion and trust models on autonomous vehicle satellite positioning. The aim is to analyze the localization improvements that commonly used sensors, technologies and techniques provide to autonomous vehicle positioning. This thesis includes both survey of localization techniques used by other research and their localization accuracy results as well as experimentation where the effects of different technologies and techniques on lateral position accuracy are reviewed. The requirements for safe autonomous driving are strict and while the performance of the average global navigation satellite system (GNSS) receiver alone may not prove to be adequate enough for accurate positioning, it may still provide valuable position data to an autonomous vehicle. For the vehicle, this position data may provide valuable information about the absolute position on the globe, it may improve localization accuracy through sensor fusion and it may act as an independent data source for sensor trust evaluation. Through empirical experimentation, the effects of sensor fusion and trust functions with an inertial measurement unit (IMU) on GNSS lateral position accuracy are measured and analyzed. The experimentation includes the measurements from both consumer-grade devices mounted on a traditional automobile and high-end devices of a truck that is capable of autonomous driving in a monitored environment. The maps and LIDAR measurements used in the experiments are prone to errors and are taken into account in the analysis of the data
Development of bent-up triangular tab shear transfer (BTTST) enhancement in cold-formed steel (CFS)-concrete composite beams
Cold-formed steel (CFS) sections, have been recognised as an important
contributor to environmentally responsible and sustainable structures in developed
countries, and CFS framing is considered as a sustainable 'green' construction material
for low rise residential and commercial buildings. However, there is still lacking of data
and information on the behaviour and performance of CFS beam in composite
construction. The use of CFS has been limited to structural roof trusses and a host of nonstructural applications. One of the limiting features of CFS is the thinness of its section
(usually between 1.2 and 3.2 mm thick) that makes it susceptible to torsional,
distortional, lateral-torsional, lateral-distortional and local buckling. Hence, a reasonable
solution is resorting to a composite construction of structural CFS section and reinforced
concrete deck slab, which minimises the distance from the neutral-axis to the top of the
deck and reduces the compressive bending stress in the CFS sections. Also, by arranging
two CFS channel sections back-to-back restores symmetricity and suppresses lateraltorsional and to a lesser extent, lateral-distortional buckling. The two-fold advantages
promised by the system, promote the use of CFS sections in a wider range of structural
applications. An efficient and innovative floor system of built-up CFS sections acting
compositely with a concrete deck slab was developed to provide an alternative composite
system for floors and roofs in buildings. The system, called Precast Cold-Formed SteelConcrete Composite System, is designed to rely on composite actions between the CFS
sections and a reinforced concrete deck where shear forces between them are effectively
transmitted via another innovative shear transfer enhancement mechanism called a bentup triangular tab shear transfer (BTTST). The study mainly comprises two major
components, i.e. experimental and theoretical work. Experimental work involved smallscale and large-scale testing of laboratory tests. Sixty eight push-out test specimens and
fifteen large-scale CFS-concrete composite beams specimens were tested in this program.
In the small-scale test, a push-out test was carried out to determine the strength and
behaviour of the shear transfer enhancement between the CFS and concrete. Four major
parameters were studied, which include compressive strength of concrete, CFS strength,
dimensions (size and angle) of BTTST and CFS thickness. The results from push-out test
were used to develop an expression in order to predict the shear capacity of innovative
shear transfer enhancement mechanism, BTTST in CFS-concrete composite beams. The
value of shear capacity was used to calculate the theoretical moment capacity of CFSconcrete composite beams. The theoretical moment capacities were used to validate the
large-scale test results. The large-scale test specimens were tested by using four-point
load bending test. The results in push-out tests show that specimens employed with
BTTST achieved higher shear capacities compared to those that rely only on a natural
bond between cold-formed steel and concrete and specimens with Lakkavalli and Liu
bent-up tab (LYLB). Load capacities for push-out test specimens with BTTST are
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relatively higher as compared to the equivalent control specimen, i.e. by 91% to 135%.
When compared to LYLB specimens the increment is 12% to 16%. In addition, shear
capacities of BTTST also increase with the increase in dimensions (size and angle) of
BTTST, thickness of CFS and concrete compressive strength. An equation was
developed to determine the shear capacity of BTTST and the value is in good agreement
with the observed test values. The average absolute difference between the test values
and predicted values was found to be 8.07%. The average arithmetic mean of the
test/predicted ratio (n) of this equation is 0.9954. The standard deviation (a) and the
coefficient of variation (CV) for the proposed equation were 0.09682 and 9.7%,
respectively. The proposed equation is recommended for the design of BTTST in CFSconcrete composite beams. In large-scale testing, specimens employed with BTTST
increased the strength capacities and reduced the deflection of the specimens. The
moment capacities, MU ) e X p for all specimens are above Mu>theory and show good agreement
with the calculated ratio (>1.00). It is also found that, strength capacities of CFS-concrete
composite beams also increase with the increase in dimensions (size and angle) of
BTTST, thickness of CFS and concrete compressive strength and a CFS-concrete
composite beam are practically designed with partial shear connection for equal moment
capacity by reducing number of BTTST. It is concluded that the proposed BTTST shear
transfer enhancement in CFS-concrete composite beams has sufficient strength and is
also feasible. Finally, a standard table of characteristic resistance, P t a b of BTTST in
normal weight concrete, was also developed to simplify the design calculation of CFSconcrete composite beams
Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information
Accurately and precisely knowing the location of the vehicle is a critical requirement for
safe and successful autonomous driving. Recent studies suggest that error for map-based localization
methods are tightly coupled with the surrounding environment. Considering this relationship, it
is therefore possible to estimate localization error by quantifying the representation and layout of
real-world phenomena. To date, existing work on estimating localization error have been limited
to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D
geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven
map evaluation factors were defined for 2D geographic information in a vector format, and random
forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo.
In the best model, the results show that it is possible to estimate autonomous vehicle localization
error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm
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