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Description
Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
ContributorsLiu, Jiangtao (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Mirchandani, Pitu (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lidar has demonstrated its utility in meteorological studies, wind resource assessment, and wind farm control. More recently, lidar has gained widespread attention for autonomous vehicles.
The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.
The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018).
The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.
The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018).
ContributorsZhou, Kai (Author) / Calhoun, Ronald (Thesis advisor) / Chen, Kangping (Committee member) / Tang, Wenbo (Committee member) / Peet, Yulia (Committee member) / Krishnamurthy, Raghavendra (Committee member) / Arizona State University (Publisher)
Created2019
Description
Laser radars or lidar’s have been used extensively to remotely study winds within the atmospheric boundary layer and atmospheric transport. Lidar sensors have become an important tool within the meteorology and the wind energy community. For example, Doppler lidars are used frequently in wind resource assessment, wind turbine control as well as in atmospheric science research. A Time of Flight based (ToF) direct detection lidar sensor is used in vehicles to navigate through complex and dynamic environments autonomously. These optical sensors are used to map the environment around the car accurately for perception and localization tasks that help achieve complete autonomy.
This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices.
At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components.
Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications.
The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety.
This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices.
At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components.
Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications.
The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety.
ContributorsBhaskaran, Sreevatsan (Author) / Calhoun, Ronald J (Thesis advisor) / Dahm, Werner (Committee member) / Huang, Huei-Ping (Committee member) / Chen, Kang Pin (Committee member) / Choukulkar, Aditya (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Arizona Board of Education decides the science curricula for students K-6. The standards lack an in depth knowledge of marine life, marine science, ocean conservation, and more related topics. Through interviews with teachers, faculty, and research on ocean literacy and coral reefs, My Coral Reef Booklet assembles various learning activities to cater to students from a variety of education, financial and impairment backgrounds. My Coral Reef Booklet addresses coral reef basics and how students can play their part in coral reef conservation despite their location.
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsHynds, Janna (Author) / Hedges, Craig (Thesis director) / Senko, Jesse (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
Coral reefs are diverse marine ecosystems, where reef building corals provide both the structure of the habitat as well as the primary production through their symbiotic algae, and alongside algae living on the reef itself, are the basis of the food web of the reef. In this way, coral reefs are the ocean's "forests" and are estimated to support 25% of all marine species. However, due to the large size of a coral reef, the relative inaccessibility and the reliance on in situ surveying methods, our current understanding of reefs is spatially limited. Understanding coral reefs from a more spatially complete perspective will offer insight into the ecological factors that contribute to coral reef vitality. This has become a priority in recent years due to the rapid decline of coral reefs caused by mass bleaching. Despite this urgency, being able to assess the entirety of a coral reef is physically difficult and this obstacle has not yet been overcome. However, similar difficulties have been addressed in terrestrial ecosystems by using remote sensing methods, which apply hyperspectral imaging to assess large areas of primary producers at high spatial resolutions. Adapting this method of remote spectral sensing to assess coral reefs has been suggested, but in order to quantify primary production via hyper spectral imaging, light-use efficiencies (LUEs) of coral reef communities need to be known. LUEs are estimations of the rate of carbon fixation compared to incident absorbed light. Here, I experimentally determine LUEs and report on several parameters related to LUE, namely net productivity, respiration, and light absorbance for the main primary producers in coral reefs surrounding Bermuda, which consist of algae and coral communities. The derived LUE values fall within typical ranges for LUEs of terrestrial ecosystems, with LUE values for coral averaging 0.022 ± 0.002 mol O2 mol photons-1 day-1 at a water flow rate of 17.5 ± 2 cm s^(-1) and 0.049 ± 0.011 mol O2 mol photons-1 day-1 at a flow rate of 32 ± 4 cm s^(-1) LUE values for algae averaged 0.0335 ± 0.0048 mol O2 mol photons-1 day-1 at a flow rate of 17.5 ± 2 cm s^(-1). These values allow insight into coral reef productivity and opens the door for future remote sensing applications.
ContributorsFlesher, David A (Author) / Neuer, Susanne (Thesis director) / Redding, Kevin (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The objective of this project was to research and experimentally test methods of localization, waypoint following, and actuation for high-speed driving by an autonomous vehicle. This thesis describes the implementation of LiDAR localization techniques, Model Predictive Control waypoint following, and communication for actuation on a 2016 Chevrolet Camaro, Arizona State University’s former EcoCAR. The LiDAR localization techniques include the NDT Mapping and Matching algorithms from the open-source autonomous vehicle platform, Autoware. The mapping algorithm was supplemented by that of Google Cartographer due to the limitations of map size in Autoware’s algorithms. The Model Predictive Control for waypoint following and the computer-microcontroller-actuator communication line are described. In addition to this experimental work, the thesis discusses an investigation of alternative approaches for each problem.
ContributorsCopenhaver, Bryce Stone (Author) / Berman, Spring (Thesis director) / Yong, Sze Zheng (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
As robots become mechanically more capable, they are going to be more and more integrated into our daily lives. Over time, human’s expectation of what the robot capabilities are is getting higher. Therefore, it can be conjectured that often robots will not act as human commanders intended them to do. That is, the users of the robots may have a different point of view from the one the robots do.
The first part of this dissertation covers methods that resolve some instances of this mismatch when the mission requirements are expressed in Linear Temporal Logic (LTL) for handling coverage, sequencing, conditions and avoidance. That is, the following general questions are addressed:
* What cause of the given mission is unrealizable?
* Is there any other feasible mission that is close to the given one?
In order to answer these questions, the LTL Revision Problem is applied and it is formulated as a graph search problem. It is shown that in general the problem is NP-Complete. Hence, it is proved that the heuristic algorihtm has 2-approximation bound in some cases. This problem, then, is extended to two different versions: one is for the weighted transition system and another is for the specification under quantitative preference. Next, a follow up question is addressed:
* How can an LTL specified mission be scaled up to multiple robots operating in confined environments?
The Cooperative Multi-agent Planning Problem is addressed by borrowing a technique from cooperative pathfinding problems in discrete grid environments. Since centralized planning for multi-robot systems is computationally challenging and easily results in state space explosion, a distributed planning approach is provided through agent coupling and de-coupling.
In addition, in order to make such robot missions work in the real world, robots should take actions in the continuous physical world. Hence, in the second part of this thesis, the resulting motion planning problems is addressed for non-holonomic robots.
That is, it is devoted to autonomous vehicles’ motion planning in challenging environments such as rural, semi-structured roads. This planning problem is solved with an on-the-fly hierarchical approach, using a pre-computed lattice planner. It is also proved that the proposed algorithm guarantees resolution-completeness in such demanding environments. Finally, possible extensions are discussed.
The first part of this dissertation covers methods that resolve some instances of this mismatch when the mission requirements are expressed in Linear Temporal Logic (LTL) for handling coverage, sequencing, conditions and avoidance. That is, the following general questions are addressed:
* What cause of the given mission is unrealizable?
* Is there any other feasible mission that is close to the given one?
In order to answer these questions, the LTL Revision Problem is applied and it is formulated as a graph search problem. It is shown that in general the problem is NP-Complete. Hence, it is proved that the heuristic algorihtm has 2-approximation bound in some cases. This problem, then, is extended to two different versions: one is for the weighted transition system and another is for the specification under quantitative preference. Next, a follow up question is addressed:
* How can an LTL specified mission be scaled up to multiple robots operating in confined environments?
The Cooperative Multi-agent Planning Problem is addressed by borrowing a technique from cooperative pathfinding problems in discrete grid environments. Since centralized planning for multi-robot systems is computationally challenging and easily results in state space explosion, a distributed planning approach is provided through agent coupling and de-coupling.
In addition, in order to make such robot missions work in the real world, robots should take actions in the continuous physical world. Hence, in the second part of this thesis, the resulting motion planning problems is addressed for non-holonomic robots.
That is, it is devoted to autonomous vehicles’ motion planning in challenging environments such as rural, semi-structured roads. This planning problem is solved with an on-the-fly hierarchical approach, using a pre-computed lattice planner. It is also proved that the proposed algorithm guarantees resolution-completeness in such demanding environments. Finally, possible extensions are discussed.
ContributorsKim, Kangjin (Author) / Fainekos, Georgios (Thesis advisor) / Baral, Chitta (Committee member) / Lee, Joohyung (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2019
Description
A novel underwater, open source, and configurable vehicle that mimics and leverages advances in quad-copter controls and dynamics, called the uDrone, was designed, built and tested. This vehicle was developed to aid coral reef researchers in collecting underwater spectroscopic data for the purpose of monitoring coral reef health. It is designed with an on-board integrated sensor system to support both automated navigation in close proximity to reefs and environmental observation. Additionally, the vehicle can serve as a testbed for future research in the realm of programming for autonomous underwater navigation and data collection, given the open-source simulation and software environment in which it was developed. This thesis presents the motivation for and design components of the new vehicle, a model governing vehicle dynamics, and the results of two proof-of-concept simulation for automated control.
ContributorsGoldman, Alex (Author) / Das, Jnaneshwar (Thesis advisor) / Asner, Greg (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020