Earthwork Haul-Truck Cycle-Time Monitoring – A Case Study

Recent developments in autonomous technologies have motivated practitioners to adopt new technologies in highway and earthwork construction projects. This project set out to (1) identify new and emerging autonomous earthwork technologies and (2) set up a field study to monitor site-level equipment operations at an earthmoving project. The results of the first part of this study are described in a separate report (2015 Conference on Autonomous and Robotic Construction of Infrastructure [CARCI]). The information reported herein presents the results of the site-level monitoring of an earthwork project, where the objective was to quantify haul truck cycle time. The site selected for monitoring was located in Johnston, Iowa, and required grading to build up a residential development. The project involved about 200,000 cubic yards of excavation and placement. Installing a storm sewer and digging a pond were also required for the project. The soils on site were of glacial origin and were generally classified as silty clays. Position tracking devices were installed on the equipment to monitor the time and position of the equipment for several days. Based on statistical analysis (non-parametric) of the haul cycle times for three haul trucks, the results are presented in terms of frequency distributions and accompanying statistical parameters. Recommendations are provided to build on this study so that additional earthwork sites can be evaluated to more broadly quantify the many factors affecting earthwork productivity

PERFORMANCE EVALUATION AND REVIEW FRAMEWORK OF ROBOTIC MISSIONS (PERFORM): AUTONOMOUS PATH PLANNING AND AUTONOMY PERFORMANCE EVALUATION

The scope of this work spans two main areas of autonomy research 1) autonomous path planning and 2) test and evaluation of autonomous systems. Path planning is an integral part of autonomous decision-making, and a deep understanding in this area provides valuable perspective on approaching the problem of how to effectively evaluate vehicle behavior. Autonomous decision-making capabilities must include reliability, robustness, and trustworthiness in a real-world environment. A major component of robot decision-making lies in intelligent path-planning. Serving as the brains of an autonomous system, an efficient and reliable path planner is crucial to mission success and overall safety. A hybrid global and local planner is implemented using a combination of the Potential Field Method (PFM) and A-star (A*) algorithms. Created using a layered vector field strategy, this allows for flexibility along with the ability to add and remove layers to take into account other parameters such as currents, wind, dynamics, and the International Regulations for Preventing Collisions at Sea (COLGREGS). Different weights can be attributed to each layer based on the determined level of importance in a hierarchical manner. Different obstacle scenarios are shown in simulation, and proof-of-concept validation of the path-planning algorithms on an actual ASV is accomplished in an indoor environment. Results show that the combination of PFM and A* complement each other to generate a successfully planned path to goal that alleviates local minima and entrapment issues. Additionally, the planner demonstrates the ability to update for new obstacles in real time using an obstacle detection sensor. Regarding test and evaluation of autonomous vehicles, trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The author introduces the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework for which to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e. providing direct comparisons and quantitative evaluations of varying autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g. for surveying, surveillance, search and rescue). Several case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission

European radiometry buoy and infrastructure (EURYBIA): A contribution to the design of the European copernicus infrastructure for ocean colour system vicarious calibration

In the context of the Copernicus Program, EUMETSAT prioritizes the creation of an ocean color infrastructure for system vicarious calibration (OC-SVC). This work aims to reply to this need by proposing the European Radiometry Buoy and Infrastructure (EURYBIA). EURYBIA is designed as an autonomous European infrastructure operating within the Marine Optical Network (MarONet) established by University of Miami (Miami, FL, USA) based on the Marine Optical Buoy (MOBY) experience and NASA support. MarONet addresses SVC requirements in different sites, consistently and in a traceable way. The selected EURYBIA installation is close to the Lampedusa Island in the central Mediterranean Sea. This area is widely studied and hosts an Atmospheric and Oceanographic Observatory for long-term climate monitoring. The EURYBIA field segment comprises off-shore and on-shore infrastructures to manage the observation system and perform routine sensors calibrations. The ground segment includes the telemetry center for data communication and the processing center to compute data products and uncertainty budgets. The study shows that the overall uncertainty of EURYBIA SVC gains computed for the Sentinel-3 OLCI mission under EUMETSAT protocols is of about 0.05% in the blue-green wavelengths after a decade of measurements, similar to that of the reference site in Hawaii and in compliance with requirements for climate studies

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

A Novel Approach to Determining Real-Time Risk Probabilities in Critical Infrastructure Industrial Control Systems

Critical Infrastructure Industrial Control Systems are substantially different from their more common and ubiquitous information technology system counterparts. Industrial control systems, such as distributed control systems and supervisory control and data acquisition systems that are used for controlling the power grid, were not originally designed with security in mind. Geographically dispersed distribution, an unfortunate reliance on legacy systems and stringent availability requirements raise significant cybersecurity concerns regarding electric reliability while constricting the feasibility of many security controls. Recent North American Electric Reliability Corporation Critical Infrastructure Protection standards heavily emphasize cybersecurity concerns and specifically require entities to categorize and identify their Bulk Electric System cyber systems; and, have periodic vulnerability assessments performed on those systems. These concerns have produced an increase in the need for more Critical Infrastructure Industrial Control Systems specific cybersecurity research. Industry stakeholders have embraced the development of a large-scale test environment through the Department of Energy’s National Supervisory Control and Data Acquisition Test-bed program; however, few individuals have access to this program. This research developed a physical industrial control system test-bed on a smaller-scale that provided an environment for modeling a simulated critical infrastructure sector performing a set of automated processes for the purpose of exploring solutions and studying concepts related to compromising control systems by way of process-tampering through code exploitation, as well as, the ability to passively and subsequently identify any risks resulting from such an event. Relative to the specific step being performed within a production cycle, at a moment in time when sensory data samples were captured and analyzed, it was possible to determine the probability of a real-time risk to a mock Critical Infrastructure Industrial Control System by comparing the sample values to those derived from a previously established baseline. This research achieved such a goal by implementing a passive, spatial and task-based segregated sensor network, running in parallel to the active control system process for monitoring and detecting risk, and effectively identified a real-time risk probability within a Critical Infrastructure Industrial Control System Test-bed. The practicality of this research ranges from determining on-demand real-time risk probabilities during an automated process, to employing baseline monitoring techniques for discovering systems, or components thereof, exploited along the supply chain

Thermal infrared dust optical depth and coarse-mode effective diameter retrieved from collocated MODIS and CALIOP observations

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 &micro;m (DAOD10&mu;m) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing retrieved Deff with the in-situ measured dust particle size distributions (PSDs) from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10&mu;m retrievals were evaluated first through comparisons with the collocated DAOD10.6&mu;m retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5&deg;&times;2&deg;) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20 % smaller than that over NA. Over NA in summer, we found a ~50 % reduction of the number of retrievals with Deff &gt; 5 &mu;m from 15&deg; W to 35&deg; W and a stable trend of Deff average at 4.4 &mu;m from 35&deg; W throughout the Caribbean Sea (90&deg; W). Over NP in spring, only ~5 % of retrieved pixels with Deff &gt; 5 &mu;m are found from 150&deg; E to 180&deg;, while the mean Deff remains stable at 4.0 &mu;m throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.</p

Control over the Cloud : Offloading, Elastic Computing, and Predictive Control

The thesis studies the use of cloud native software and platforms to implement critical closed loop control. It considers technologies that provide low latency and reliable wireless communication, in terms of edge clouds and massive MIMO, but also approaches industrial IoT and the services of a distributed cloud, as an extension of commercial-of-the-shelf software and systems.First, the thesis defines the cloud control challenge, as control over the cloud and controller offloading. This is followed by a demonstration of closed loop control, using MPC, running on a testbed representing the distributed cloud.The testbed is implemented using an IoT device, clouds, next generation wireless technology, and a distributed execution platform. Platform details are provided and feasibility of the approach is shown. Evaluation includes relocating an on-line MPC to various locations in the distributed cloud. Offloaded control is examined next, through further evaluation of cloud native software and frameworks. This is followed by three controller designs, tailored for use with the cloud. The first controller solves MPC problems in parallel, to implement a variable horizon controller. The second is a hierarchical design, in which rate switching is used to implement constrained control, with a local and a remote mode. The third design focuses on reliability. Here, the MPC problem is extended to include recovery paths that represent a fallback mode. This is used by a control client if it experiences connectivity issues.An implementation is detailed and examined.In the final part of the thesis, the focus is on latency and congestion. A cloud control client can experience long and variable delays, from network and computations, and used services can become overloaded. These problems are approached by using predicted control inputs, dynamically adjusting the control frequency, and using horizontal scaling of the cloud service. Several examples are shown through simulation and on real clouds, including admitting control clients into a cluster that becomes temporarily overloaded