Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Turaga, Pavan
- Creators: Bazzi, Rida A
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dynamic software update (DSU) enables a program to update while it is running. DSU aims to minimize the loss due to program downtime for updates. Usually DSU is done in three steps: suspending the execution of an old program, mapping the execution state from the old program to a new one, and resuming execution of the new program with the mapped state. The semantic correctness of DSU depends largely on the state mapping which is mostly composed by developers manually nowadays. However, the manual construction of a state mapping does not necessarily ensure sound and dependable state mapping. This dissertation presents a methodology to assist developers by automating the construction of a partial state mapping with a guarantee of correctness.
This dissertation includes a detailed study of DSU correctness and automatic state mapping for server programs with an established user base. At first, the dissertation presents the formal treatment of DSU correctness and the state mapping problem. Then the dissertation presents an argument that for programs with an established user base, dynamic updates must be backward compatible. The dissertation next presents a general definition of backward compatibility that specifies the allowed changes in program interaction between an old version and a new version and identified patterns of code evolution that results in backward compatible behavior. Thereafter the dissertation presents formal definitions of these patterns together with proof that any changes to programs in these patterns will result in backward compatible update. To show the applicability of the results, the dissertation presents SitBack, a program analysis tool that has an old version program and a new one as input and computes a partial state mapping under the assumption that the new version is backward compatible with the old version.
SitBack does not handle all kinds of changes and it reports to the user in incomplete part of a state mapping. The dissertation presents a detailed evaluation of SitBack which shows that the methodology of automatic state mapping is promising in deal with real world program updates. For example, SitBack produces state mappings for 17-75% of the changed functions. Furthermore, SitBack generates automatic state mapping that leads to successful DSU. In conclusion, the study presented in this dissertation does assist developers in developing state mappings for DSU by automating the construction of state mappings with a correctness guarantee, which helps the adoption of DSU ultimately.
This dissertation includes a detailed study of DSU correctness and automatic state mapping for server programs with an established user base. At first, the dissertation presents the formal treatment of DSU correctness and the state mapping problem. Then the dissertation presents an argument that for programs with an established user base, dynamic updates must be backward compatible. The dissertation next presents a general definition of backward compatibility that specifies the allowed changes in program interaction between an old version and a new version and identified patterns of code evolution that results in backward compatible behavior. Thereafter the dissertation presents formal definitions of these patterns together with proof that any changes to programs in these patterns will result in backward compatible update. To show the applicability of the results, the dissertation presents SitBack, a program analysis tool that has an old version program and a new one as input and computes a partial state mapping under the assumption that the new version is backward compatible with the old version.
SitBack does not handle all kinds of changes and it reports to the user in incomplete part of a state mapping. The dissertation presents a detailed evaluation of SitBack which shows that the methodology of automatic state mapping is promising in deal with real world program updates. For example, SitBack produces state mappings for 17-75% of the changed functions. Furthermore, SitBack generates automatic state mapping that leads to successful DSU. In conclusion, the study presented in this dissertation does assist developers in developing state mappings for DSU by automating the construction of state mappings with a correctness guarantee, which helps the adoption of DSU ultimately.
ContributorsShen, Jun (Author) / Bazzi, Rida A (Thesis advisor) / Fainekos, Georgios (Committee member) / Neamtiu, Iulian (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2015
Description
Multi-sensor fusion is a fundamental problem in Robot Perception. For a robot to operate in a real world environment, multiple sensors are often needed. Thus, fusing data from various sensors accurately is vital for robot perception. In the first part of this thesis, the problem of fusing information from a LIDAR, a color camera and a thermal camera to build RGB-Depth-Thermal (RGBDT) maps is investigated. An algorithm that solves a non-linear optimization problem to compute the relative pose between the cameras and the LIDAR is presented. The relative pose estimate is then used to find the color and thermal texture of each LIDAR point. Next, the various sources of error that can cause the mis-coloring of a LIDAR point after the cross- calibration are identified. Theoretical analyses of these errors reveal that the coloring errors due to noisy LIDAR points, errors in the estimation of the camera matrix, and errors in the estimation of translation between the sensors disappear with distance. But errors in the estimation of the rotation between the sensors causes the coloring error to increase with distance.
On a robot (vehicle) with multiple sensors, sensor fusion algorithms allow us to represent the data in the vehicle frame. But data acquired temporally in the vehicle frame needs to be registered in a global frame to obtain a map of the environment. Mapping techniques involving the Iterative Closest Point (ICP) algorithm and the Normal Distributions Transform (NDT) assume that a good initial estimate of the transformation between the 3D scans is available. This restricts the ability to stitch maps that were acquired at different times. Mapping can become flexible if maps that were acquired temporally can be merged later. To this end, the second part of this thesis focuses on developing an automated algorithm that fuses two maps by finding a congruent set of five points forming a pyramid.
Mapping has various application domains beyond Robot Navigation. The third part of this thesis considers a unique application domain where the surface displace- ments caused by an earthquake are to be recovered using pre- and post-earthquake LIDAR data. A technique to recover the 3D surface displacements is developed and the results are presented on real earthquake datasets: El Mayur Cucupa earthquake, Mexico, 2010 and Fukushima earthquake, Japan, 2011.
On a robot (vehicle) with multiple sensors, sensor fusion algorithms allow us to represent the data in the vehicle frame. But data acquired temporally in the vehicle frame needs to be registered in a global frame to obtain a map of the environment. Mapping techniques involving the Iterative Closest Point (ICP) algorithm and the Normal Distributions Transform (NDT) assume that a good initial estimate of the transformation between the 3D scans is available. This restricts the ability to stitch maps that were acquired at different times. Mapping can become flexible if maps that were acquired temporally can be merged later. To this end, the second part of this thesis focuses on developing an automated algorithm that fuses two maps by finding a congruent set of five points forming a pyramid.
Mapping has various application domains beyond Robot Navigation. The third part of this thesis considers a unique application domain where the surface displace- ments caused by an earthquake are to be recovered using pre- and post-earthquake LIDAR data. A technique to recover the 3D surface displacements is developed and the results are presented on real earthquake datasets: El Mayur Cucupa earthquake, Mexico, 2010 and Fukushima earthquake, Japan, 2011.
ContributorsKrishnan, Aravindhan K (Author) / Saripalli, Srikanth (Thesis advisor) / Klesh, Andrew (Committee member) / Fainekos, Georgios (Committee member) / Thangavelautham, Jekan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2016