Matching Items (17)
Filtering by
- All Subjects: robotics
- All Subjects: Mechanical Engineering
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
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
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Floating trash objects are very commonly seen on water bodies such as lakes, canals and rivers. With the increase of plastic goods and human activities near the water bodies, these trash objects can pile up and cause great harm to the surrounding environment. Using human workers to clear out these trash is a hazardous and time-consuming task. Employing autonomous robots for these tasks is a better approach since it is more efficient and faster than humans. However, for a robot to clean the trash objects, a good detection algorithm is required. Real-time object detection on water surfaces is a challenging issue due to nature of the environment and the volatility of the water surface. In addition to this, running an object detection algorithm on an on-board processor of a robot limits the amount of CPU consumption that the algorithm can utilize. In this thesis, a computationally low cost object detection approach for robust detection of trash objects that was run on an on-board processor of a multirotor is presented. To account for specular reflections on the water surface, we use a polarization filter and integrate a specularity removal algorithm on our approach as well. The challenges faced during testing and the means taken to eliminate those challenges are also discussed. The algorithm was compared with two other object detectors using 4 different metrics. The testing was carried out using videos of 5 different objects collected at different illumination conditions over a lake using a multirotor. The results indicate that our algorithm is much suitable to be employed in real-time since it had the highest processing speed of 21 FPS, the lowest CPU consumption of 37.5\% and considerably high precision and recall values in detecting the object.
ContributorsSyed, Danish Faraaz (Author) / Zhang, Wenlong (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Simultaneous localization and mapping (SLAM) has traditionally relied on low-level geometric or optical features. However, these features-based SLAM methods often struggle with feature-less or repetitive scenes. Additionally, low-level features may not provide sufficient information for robot navigation and manipulation, leaving robots without a complete understanding of the 3D spatial world. Advanced information is necessary to address these limitations. Fortunately, recent developments in learning-based 3D reconstruction allow robots to not only detect semantic meanings, but also recognize the 3D structure of objects from a few images. By combining this 3D structural information, SLAM can be improved from a low-level approach to a structure-aware approach. This work propose a novel approach for multi-view 3D reconstruction using recurrent transformer. This approach allows robots to accumulate information from multiple views and encode them into a compact latent space. The resulting latent representations are then decoded to produce 3D structural landmarks, which can be used to improve robot localization and mapping.
ContributorsHuang, Chi-Yao (Author) / Yang, Yezhou (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Manipulator motion planning has conventionally been solved using sampling and optimization-based algorithms that are agnostic to embodiment and environment configurations. However, these algorithms plan on a fixed environment representation approximated using shape primitives, and hence struggle to find solutions for cluttered and dynamic environments. Furthermore, these algorithms fail to produce solutions for complex unstructured environments under real-time bounds. Neural Motion Planners (NMPs) are an appealing alternative to algorithmic approaches as they can leverage parallel computing for planning while incorporating arbitrary environmental constraints directly from raw sensor observations. Contemporary NMPs successfully transfer to different environment variations, however, fail to generalize across embodiments. This thesis proposes "AnyNMP'', a generalist motion planning policy for zero-shot transfer across different robotic manipulators and environments. The policy is conditioned on semantically segmented 3D pointcloud representation of the workspace thus enabling implicit sim2real transfer. In the proposed approach, templates are formulated for manipulator kinematics and ground truth motion plans are collected for over 3 million procedurally sampled robots in randomized environments. The planning pipeline consists of a state validation model for differentiable collision detection and a sampling based planner for motion generation. AnyNMP has been validated on 5 different commercially available manipulators and showcases successful cross-embodiment planning, achieving an 80% average success rate on baseline benchmarks.
ContributorsRath, Prabin Kumar (Author) / Gopalan, Nakul (Thesis advisor) / Yu, Hongbin (Thesis advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2024
Description
The objective of this dissertation is to study the use of metamaterials as narrow-band and broadband selective absorbers for opto-thermal and solar thermal energy conversion. Narrow-band selective absorbers have applications such as plasmonic sensing and cancer treatment, while one of the main applications of selective metamaterials with broadband absorption is efficiently converting solar energy into heat as solar absorbers.
This dissertation first discusses the use of gold nanowires as narrow-band selective metamaterial absorbers. An investigation into plasmonic localized heating indicated that film-coupled gold nanoparticles exhibit tunable selective absorption based on the size of the nanoparticles. By using anodized aluminum oxide templates, aluminum nanodisc narrow-band absorbers were fabricated. A metrology instrument to measure the reflectance and transmittance of micro-scale samples was also developed and used to measure the reflectance of the aluminum nanodisc absorbers (220 µm diameter area). Tuning of the resonance wavelengths of these absorbers can be achieved through changing their geometry. Broadband absorption can be achieved by using a combination of geometries for these metamaterials which would facilitate their use as solar absorbers.
Recently, solar energy harvesting has become a topic of considerable research investigation due to it being an environmentally conscious alternative to fossil fuels. The next section discusses the steady-state temperature measurement of a lab-scale multilayer solar absorber, named metafilm. A lab-scale experimental setup is developed to characterize the solar thermal performance of selective solar absorbers. Under a concentration factor of 20.3 suns, a steady-state temperature of ~500 degrees Celsius was achieved for the metafilm compared to 375 degrees Celsius for a commercial black absorber under the same conditions. Thermal durability testing showed that the metafilm could withstand up to 700 degrees Celsius in vacuum conditions and up to 400 degrees Celsius in atmospheric conditions with little degradation of its optical and radiative properties. Moreover, cost analysis of the metafilm found it to cost significantly less ($2.22 per square meter) than commercial solar coatings ($5.41-100 per square meter).
Finally, this dissertation concludes with recommendations for further studies like using these selective metamaterials and metafilms as absorbers and emitters and using the aluminum nanodiscs on glass as selective filters for photovoltaic cells to enhance solar thermophotovoltaic energy conversion.
This dissertation first discusses the use of gold nanowires as narrow-band selective metamaterial absorbers. An investigation into plasmonic localized heating indicated that film-coupled gold nanoparticles exhibit tunable selective absorption based on the size of the nanoparticles. By using anodized aluminum oxide templates, aluminum nanodisc narrow-band absorbers were fabricated. A metrology instrument to measure the reflectance and transmittance of micro-scale samples was also developed and used to measure the reflectance of the aluminum nanodisc absorbers (220 µm diameter area). Tuning of the resonance wavelengths of these absorbers can be achieved through changing their geometry. Broadband absorption can be achieved by using a combination of geometries for these metamaterials which would facilitate their use as solar absorbers.
Recently, solar energy harvesting has become a topic of considerable research investigation due to it being an environmentally conscious alternative to fossil fuels. The next section discusses the steady-state temperature measurement of a lab-scale multilayer solar absorber, named metafilm. A lab-scale experimental setup is developed to characterize the solar thermal performance of selective solar absorbers. Under a concentration factor of 20.3 suns, a steady-state temperature of ~500 degrees Celsius was achieved for the metafilm compared to 375 degrees Celsius for a commercial black absorber under the same conditions. Thermal durability testing showed that the metafilm could withstand up to 700 degrees Celsius in vacuum conditions and up to 400 degrees Celsius in atmospheric conditions with little degradation of its optical and radiative properties. Moreover, cost analysis of the metafilm found it to cost significantly less ($2.22 per square meter) than commercial solar coatings ($5.41-100 per square meter).
Finally, this dissertation concludes with recommendations for further studies like using these selective metamaterials and metafilms as absorbers and emitters and using the aluminum nanodiscs on glass as selective filters for photovoltaic cells to enhance solar thermophotovoltaic energy conversion.
ContributorsAlshehri, Hassan (Author) / Wang, Liping (Thesis advisor) / Phelan, Patrick (Committee member) / Rykaczewski, Konrad (Committee member) / Wang, Robert (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Origami and kirigami, the technique of generating three-dimensional (3D) structures from two-dimensional (2D) flat sheets, are now more and more involved in scientific and engineering fields. Therefore, the development of tools for their theoretical analysis becomes more and more important. Since much effort was paid on calculations based on pure mathematical consideration and only limited effort has been paid to include mechanical properties, the goal of my research is developing a method to analyze the mechanical behavior of origami and kirigami based structures. Mechanical characteristics, including nonlocal effect and fracture of the structures, as well as elasticity and plasticity of materials are studied. For calculation of relative simple structures and building of structures’ constitutive relations, analytical approaches were used. For more complex structures, finite element analysis (FEA), which is commonly applied as a numerical method for the analysis of solid structures, was utilized. The general study approach is not necessarily related to characteristic size of model. I believe the scale-independent method described here will pave a new way to understand the mechanical response of a variety of origami and kirigami based structures under given mechanical loading.
ContributorsLv, Cheng (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongbin (Committee member) / Wang, Liping (Committee member) / Mignolet, Marc (Committee member) / Hildreth, Owen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Origami and Kirigami are two traditional art forms in the world. Origami, from
‘ori’ meaning folding, and ‘kami’ meaning paper is the art of paper folding. Kirigami, from ‘kiri’ meaning cutting, is the art of the combination of paper cutting and paper folding. In this dissertation, Origami and kirigami concepts were successively utilized in making stretchable lithium ion batteries and three-dimensional (3D) silicon structure which both provide excellent mechanical characteristics.
‘ori’ meaning folding, and ‘kami’ meaning paper is the art of paper folding. Kirigami, from ‘kiri’ meaning cutting, is the art of the combination of paper cutting and paper folding. In this dissertation, Origami and kirigami concepts were successively utilized in making stretchable lithium ion batteries and three-dimensional (3D) silicon structure which both provide excellent mechanical characteristics.
ContributorsSong, Zeming (Author) / Jiang, Hanqing (Thesis advisor) / Dai, Lenore (Committee member) / Yu, Hongbin (Committee member) / He, Ximin (Committee member) / Arizona State University (Publisher)
Created2016
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