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.
Displaying 1 - 9 of 9
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- All Subjects: robotics
- Creators: Saripalli, Srikanth
- Creators: Dedinsky, Rachel
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
The goal of this project is to use an open-source solution to implement a drone Cyber-Physical System that can fly autonomously and accurately. The proof-of-concept to analyze the drone's flight capabilities is to fly in a pattern corresponding to the outline of an image, a process that requires both stability and precision to accurately depict the image. In this project, we found that building a Cyber-Physical System is difficult because of the tedious and complex nature of designing and testing the hardware and software solutions of this system. Furthermore, we reflect on the difficulties that arose from using open-source hardware and software.
ContributorsDedinsky, Rachel (Co-author) / Lubbers, Harrison James (Co-author) / Shrivastava, Aviral (Thesis director) / Dougherty, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Technological progress in robot sensing, design, and fabrication, and the availability of open source software frameworks such as the Robot Operating System (ROS), are advancing the applications of swarm robotics from toy problems to real-world tasks such as surveillance, precision agriculture, search-and-rescue, and infrastructure inspection. These applications will require the development of robot controllers and system architectures that scale well with the number of robots and that are robust to robot errors and failures. To achieve this, one approach is to design decentralized robot control policies that require only local sensing and local, ad-hoc communication. In particular, stochastic control policies can be designed that are agnostic to individual robot identities and do not require a priori information about the environment or sophisticated computation, sensing, navigation, or communication capabilities. This dissertation presents novel swarm control strategies with these properties for detecting and mapping static targets, which represent features of interest, in an
unknown, bounded, obstacle-free environment. The robots move on a finite spatial grid according to
the time-homogeneous transition probabilities of a Discrete-Time Discrete-State (DTDS) Markov chain model, and they exchange information with other robots within their communication range using a consensus (agreement) protocol. This dissertation extend theoretical guarantees on multi-robot consensus over fixed and time-varying communication networks with known connectivity properties to consensus over the networks that have Markovian switching dynamics and no presumed connectivity. This dissertation develops such swarm consensus strategies for detecting a single feature in the environment, tracking multiple features, and reconstructing a discrete distribution of features modeled as an occupancy grid map.
The proposed consensus approaches are validated in numerical simulations and in 3D physics-based simulations of quadrotors in Gazebo. The scalability of the proposed approaches is examined through extensive numerical simulation studies over different swarm populations and environment sizes.
ContributorsShirsat, Aniket (Author) / Berman, Spring (Thesis advisor) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Saripalli, Srikanth (Committee member) / Gharavi, Lance (Committee member) / Arizona State University (Publisher)
Created2022
Description
There has been a vast increase in applications of Unmanned Aerial Vehicles (UAVs) in civilian domains. To operate in the civilian airspace, a UAV must be able to sense and avoid both static and moving obstacles for flight safety. While indoor and low-altitude environments are mainly occupied by static obstacles, risks in space of higher altitude primarily come from moving obstacles such as other aircraft or flying vehicles in the airspace. Therefore, the ability to avoid moving obstacles becomes a necessity
for Unmanned Aerial Vehicles.
Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate
these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.
for Unmanned Aerial Vehicles.
Towards enabling a UAV to autonomously sense and avoid moving obstacles, this thesis makes the following contributions. Initially, an image-based reactive motion planner is developed for a quadrotor to avoid a fast approaching obstacle. Furthermore, A Dubin’s curve based geometry method is developed as a global path planner for a fixed-wing UAV to avoid collisions with aircraft. The image-based method is unable to produce an optimal path and the geometry method uses a simplified UAV model. To compensate
these two disadvantages, a series of algorithms built upon the Closed-Loop Rapid Exploratory Random Tree are developed as global path planners to generate collision avoidance paths in real time. The algorithms are validated in Software-In-the-Loop (SITL) and Hardware-In-the-Loop (HIL) simulations using a fixed-wing UAV model and in real flight experiments using quadrotors. It is observed that the algorithm enables a UAV to avoid moving obstacles approaching to it with different directions and speeds.
ContributorsLin, Yucong (Author) / Saripalli, Srikanth (Thesis advisor) / Scowen, Paul (Committee member) / Fainekos, Georgios (Committee member) / Thangavelautham, Jekanthan (Committee member) / Youngbull, Cody (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
Description
This project is to develop a new method to generate GPS waypoints for better terrain mapping efficiency using an UAV. To create a map of a desired terrain, an UAV is used to capture images at particular GPS locations. These images are then stitched together to form a complete map of the terrain. To generate a good map using image stitching, the images are desired to have a certain percentage of overlap between them. In high windy condition, an UAV may not capture image at desired GPS location, which in turn interferes with the desired percentage of overlap between images; both frontal and sideways; thus causing discrepancies while stitching the images together. The information about the exact GPS locations at which the images are captured can be found on the flight logs that are stored in the Ground Control Station and the Auto pilot board. The objective is to look at the flight logs, predict the waypoints at which the UAV might have swayed from the desired flight path. If there are locations where flight swayed from intended path, the code should generate a new set of waypoints for a correction flight. This will save the time required for stitching the images together, thus making the whole process faster and more efficient.
ContributorsGhadage, Prasannakumar Prakashrao (Author) / Saripalli, Srikanth (Thesis advisor) / Berman, Spring M (Thesis advisor) / Thangavelautham, Jekanthan (Committee member) / Arizona State University (Publisher)
Created2014
Description
At modern-day intersections, traffic lights and stop signs assist human drivers to cross the intersection safely. Traffic congestion in urban road networks is a costly problem that affects all major cities. Efficiently operating intersections is largely dependent on accuracy and precision of human drivers, engendering a lingering uncertainty of attaining safety and high throughput. To improve the efficiency of the existing traffic network and mitigate the effects of human error in the intersection, many studies have proposed autonomous, intelligent transportation systems. These studies often involve utilizing connected autonomous vehicles, implementing a supervisory system, or both. Implementing a supervisory system is relatively more popular due to the security concerns of vehicle-to-vehicle communication. Even though supervisory systems are a step in the right direction for security, many supervisory systems’ safe operation solely relies on the promise of connected data being correct, making system reliability difficult to achieve. To increase fault-tolerance and decrease the effects of position uncertainty, this thesis proposes the Reliable and Robust Intersection Manager, a supervisory system that uses a separate surveillance system to dependably detect vehicles present in the intersection in order to create data redundancy for more accurate scheduling of connected autonomous vehicles. Adding the Surveillance System ensures that the temporal safety buffers between arrival times of connected autonomous vehicles are maintained. This guarantees that connected autonomous vehicles can traverse the intersection safely in the event of large vehicle controller error, a single rogue car entering the intersection, or a sybil attack. To test the proposed system given these fault-models, MATLAB® was used to create simulations in order to observe the functionality of R2IM compared to the state-of-the-art supervisory system, Robust Intersection Manager. Though R2IM is less efficient than the Robust Intersection Manager, it considers more fault models. The Robust Intersection Manager failed to maintain safety in the event of large vehicle controller errors and rogue cars, however R2IM resulted in zero collisions.
ContributorsDedinsky, Rachel (Author) / Shrivastava, Aviral (Thesis advisor) / Sen, Arunabha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2019