Matching Items (17)
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Description

When rover mission planners are laying out the path for their rover, they use a combination of stereo images and statistical and geological data in order to plot a course for the vehicle to follow for its mission. However, there is a lack of detailed images of the lunar surface

When rover mission planners are laying out the path for their rover, they use a combination of stereo images and statistical and geological data in order to plot a course for the vehicle to follow for its mission. However, there is a lack of detailed images of the lunar surface that indicate the specific presence of hazards, such as craters, and the creation of such crater maps is time-consuming. There is also little known about how varying lighting conditions caused by the changing solar incidence angle affects perception as well. This paper addresses this issue by investigating how varying the incidence angle of the sun affects how well the human and AI can detect craters. It will also see how AI can accelerate the crater-mapping process, and how well it performs relative to a human annotating crater maps by hand. To accomplish this, several sets of images of the lunar surface were taken with varying incidence angles for the same spot and were annotated both by hand and by an AI. The results are observed, and then the AI performance was rated by calculating its resulting precision and recall, considering the human annotations as being the ground truth. It was found that there seems to be a maximum incidence angle for which detect rates are the highest, and that, at the moment, the AI’s detection of craters is poor, but it can be improved. With this, it can inform future and more expansive investigations into how lighting can affect the perception of hazards to rovers, as well as the role AI can play in creating these crater maps.

ContributorsHayashi, Brent Keopele (Author) / Das, Jnaneshwar (Thesis director) / Mahanti, Prasun (Committee member) / Anand, Harish (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Drylands, though one of the largest biomes, are also one of the most understudied biomes on the planet. This leaves scientists with limited understanding of unique life forms that have adapted to live in these arid environments. One such life form is the hypolithic microbial community; these are autotrophic cyanobacteria

Drylands, though one of the largest biomes, are also one of the most understudied biomes on the planet. This leaves scientists with limited understanding of unique life forms that have adapted to live in these arid environments. One such life form is the hypolithic microbial community; these are autotrophic cyanobacteria colonies that can be found on the underside of translucent rocks in deserts. With the light that filters through the rock above them, the microbes can photosynthesize and fix carbon from the atmosphere into the soil. In this study I looked at hypolith-like rock distribution in the Namib Desert by using image recognition software. I trained a Mask R-CNN network to detect quartz rock in images from the Gobabeb site. When the method was analyzed using the entire data set, the distribution of rock sizes between the manual annotations and the network predictions was not similar. When evaluating rock sizes smaller than 0.56 cm2 the method showed statistical significance in support of being a promising data collection method. With more training and corrective effort on the network, this method shows promise to be an accurate and novel way to collect data efficiently in dryland research.

ContributorsCollins, Catherine (Author) / Throop, Heather (Thesis director) / Das, Jnaneshwar (Committee member) / Aparecido, Luiza (Committee member) / School of Earth and Space Exploration (Contributor) / School of Art (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
The ability for aerial manipulators to stay aloft while interacting with dynamic environments is critical for successfully in situ data acquisition methods in arboreal environments. One widely used platform utilizes a six degree of freedom manipulator attached to quadcoper or octocopter, to sample a tree leaf by maintaining the system

The ability for aerial manipulators to stay aloft while interacting with dynamic environments is critical for successfully in situ data acquisition methods in arboreal environments. One widely used platform utilizes a six degree of freedom manipulator attached to quadcoper or octocopter, to sample a tree leaf by maintaining the system in a hover while the arm pulls the leaf for a sample. Other system are comprised of simpler quadcopter with a fixed mechanical device to physically cut the leaf while the system is manually piloted. Neither of these common methods account or compensate for the variation of inherent dynamics occurring in the arboreal-aerial manipulator interaction effects. This research proposes force and velocity feedback methods to control an aerial manipulation platform while allowing waypoint navigation within the work space to take place. Using these methods requires minimal knowledge of the system and the dynamic parameters. This thesis outlines the Robot Operating System (ROS) based Open Autonomous Air Vehicle (OpenUAV) simulations performed on the purposed three degree of freedom redundant aerial manipulation platform.
ContributorsCohen, Daniel (Author) / Das, Jnaneshwar (Thesis advisor) / Marvi, Hamidreza (Committee member) / Saldaña, David (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Autonomous Robots have a tremendous potential to assist humans in environmental monitoring tasks. In order to generate meaningful data for humans to analyze, the robots need to collect accurate data and develop reliable representation of the environment. This is achieved by employing scalable and robust navigation and mapping algorithms that

Autonomous Robots have a tremendous potential to assist humans in environmental monitoring tasks. In order to generate meaningful data for humans to analyze, the robots need to collect accurate data and develop reliable representation of the environment. This is achieved by employing scalable and robust navigation and mapping algorithms that facilitate acquiring and understanding data collected from the array of on-board sensors. To this end, this thesis presents navigation and mapping algorithms for autonomous robots that can enable robot navigation in complexenvironments and develop real time semantic map of the environment respectively. The first part of the thesis presents a novel navigation algorithm for an autonomous underwater vehicle that can maintain a fixed distance from the coral terrain while following a human diver. Following a human diver ensures that the robot would visit all important sites in the coral reef while maintaining a constant distance from the terrain reduces heterscedasticity in the measurements. This algorithm was tested on three different synthetic terrains including a real model of a coral reef in Hawaii. The second part of the thesis presents a dense semantic surfel mapping technique based on top of a popular surfel mapping algorithm that can generate meaningful maps in real time. A semantic mask from a depth aligned RGB-D camera was used to assign labels to the surfels which were then probabilistically updated with multiple measurements. The mapping algorithm was tested with simulated data from an RGB-D camera and the results were analyzed.
ContributorsAntervedi, Lakshmi Gana Prasad (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta E (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Despite the rapid adoption of robotics and machine learning in industry, their application to scientific studies remains under-explored. Combining industry-driven advances with scientific exploration provides new perspectives and a greater understanding of the planet and its environmental processes. Focusing on rock detection, mapping, and dynamics analysis, I present technical approaches

Despite the rapid adoption of robotics and machine learning in industry, their application to scientific studies remains under-explored. Combining industry-driven advances with scientific exploration provides new perspectives and a greater understanding of the planet and its environmental processes. Focusing on rock detection, mapping, and dynamics analysis, I present technical approaches and scientific results of developing robotics and machine learning technologies for geomorphology and seismic hazard analysis. I demonstrate an interdisciplinary research direction to push the frontiers of both robotics and geosciences, with potential translational contributions to commercial applications for hazard monitoring and prospecting. To understand the effects of rocky fault scarp development on rock trait distributions, I present a data-processing pipeline that utilizes unpiloted aerial vehicles (UAVs) and deep learning to segment densely distributed rocks in several orders of magnitude. Quantification and correlation analysis of rock trait distributions demonstrate a statistical approach for geomorphology studies. Fragile geological features such as precariously balanced rocks (PBRs) provide upper-bound ground motion constraints for hazard analysis. I develop an offboard method and onboard method as complementary to each other for PBR searching and mapping. Using deep learning, the offboard method segments PBRs in point clouds reconstructed from UAV surveys. The onboard method equips a UAV with edge-computing devices and stereo cameras, enabling onboard machine learning for real-time PBR search, detection, and mapping during surveillance. The offboard method provides an efficient solution to find PBR candidates in existing point clouds, which is useful for field reconnaissance. The onboard method emphasizes mapping individual PBRs for their complete visible surface features, such as basal contacts with pedestals–critical geometry to analyze fragility. After PBRs are mapped, I investigate PBR dynamics by building a virtual shake robot (VSR) that simulates ground motions to test PBR overturning. The VSR demonstrates that ground motion directions and niches are important factors determining PBR fragility, which were rarely considered in previous studies. The VSR also enables PBR large-displacement studies by tracking a toppled-PBR trajectory, presenting novel methods of rockfall hazard zoning. I build a real mini shake robot providing a reverse method to validate simulation experiments in the VSR.
ContributorsChen, Zhiang (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Bell, James (Committee member) / Berman, Spring (Committee member) / Christensen, Philip (Committee member) / Whipple, Kelin (Committee member) / Arizona State University (Publisher)
Created2022
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Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical methods for coping with sparse rewards during reinforcement learning modify the reward landscape so as to better guide the learner. In contrast, this work combines RL with a planner in order

This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical methods for coping with sparse rewards during reinforcement learning modify the reward landscape so as to better guide the learner. In contrast, this work combines RL with a planner in order to utilize other information about the environment. As the scope for representing environmental information is limited in RL, this work has conflated a model-free learning algorithm – temporal difference (TD) learning – with a Hierarchical Task Network (HTN) planner to accommodate rich environmental information in the algorithm. In the perpetual sparse rewards problem, rewards reemerge after being collected within a fixed interval of time, culminating in a lack of a well-defined goal state as an exit condition to the problem. Incorporating planning in the learning algorithm not only improves the quality of the solution, but the algorithm also avoids the ambiguity of incorporating a goal of maximizing profit while using only a planning algorithm to solve this problem. Upon occasionally using the HTN planner, this algorithm provides the necessary tweak toward the optimal solution. In this work, I have demonstrated an on-policy algorithm that has improved the quality of the solution over vanilla reinforcement learning. The objective of this work has been to observe the capacity of the synthesized algorithm in finding optimal policies to maximize rewards, awareness of the environment, and the awareness of the presence of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still

Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still able to explore and map the environment. The map can further be used by first responders and cave explorers to access the environments. This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle (UAV), XPLORER that utilizes a novel navigation algorithm for exploration and simultaneous mapping of the environment. The real-time navigation algorithm uses the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor design is discussed, highlighting its improved stability over the previous design. The generated map of the environment can be utilized by autonomous vehicles to navigate the environment. The navigation algorithm is validated in multiple real-time experiments in different scenarios consisting of concave and convex corners and circular objects. Furthermore, the developed mapping framework can serve as an auxiliary input for map generation along with conventional LiDAR or vision-based mapping algorithms. Both the navigation and mapping algorithms are designed to be modular, making them compatible with conventional UAVs also. This research contributes to the development of navigation and mapping techniques for GPS-denied environments, enabling safer and more efficient exploration of challenging territories.
ContributorsPandian Saravanakumaran, Aravind Adhith (Author) / Zhang, Wenlong (Thesis advisor) / Das, Jnaneshwar (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2023
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Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically feasible trajectories and precise control. Various factors, including rotor dynamics, thrust allocation, and control strategies, influence the successful execution of flips. This research introduces an approach for tracking optimal trajectories to execute flip maneuvers while ensuring

Acrobatic maneuvers of quadrotors present unique challenges concerning trajectorygeneration, control, and execution. Specifically, the flip maneuver requires dynamically feasible trajectories and precise control. Various factors, including rotor dynamics, thrust allocation, and control strategies, influence the successful execution of flips. This research introduces an approach for tracking optimal trajectories to execute flip maneuvers while ensuring system stability autonomously. Model Predictive Control (MPC) designs the controller, enabling the quadrotor to plan and execute optimal trajectories in real-time, accounting for dynamic constraints and environmental factors. The utilization of predictive models enables the quadrotor to anticipate and adapt to changes during aggressive maneuvers. Simulation-based evaluations were conducted in the ROS and Gazebo environments. These evaluations provide valuable insights into the quadrotor’s behavior, response time, and tracking accuracy. Additionally, real-time flight experiments utilizing state- of-the-art flight controllers, such as the PixHawk 4, and companion computers, like the Hardkernel Odroid, validate the effectiveness of the proposed control algorithms in practical scenarios. The conducted experiments also demonstrate the successful execution of the proposed approach. This research’s outcomes contribute to quadrotor technology’s advancement, particularly in acrobatic maneuverability. This opens up possibilities for executing maneuvers with precise timing, such as slingshot probe releases during flips. Moreover, this research demonstrates the efficacy of MPC controllers in achieving autonomous probe throws within no-fly zone environments while maintaining an accurate desired range. Field application of this research includes probe deployment into volcanic plumes or challenging-to-access rocky fault scarps, and imaging of sites of interest. along flight paths through rolling or pitching maneuvers of the quadrotor, to use sensorsuch as cameras or spectrometers on the quadrotor belly.
Contributorsjain, saransh (Author) / Das, Jnaneshwar (Thesis advisor) / Zhang, Wenlong (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2023
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Drylands make up more than 45% of the Earth’s land surface and are essential to agriculture and understanding global carbon and elemental cycling. This thesis presents an analysis of atmospheric relative humidity (RH) and temperature (T) as they impact soil moisture and water content at two dryland sites. In particular,

Drylands make up more than 45% of the Earth’s land surface and are essential to agriculture and understanding global carbon and elemental cycling. This thesis presents an analysis of atmospheric relative humidity (RH) and temperature (T) as they impact soil moisture and water content at two dryland sites. In particular, this thesis assesses the likelihood and impact of non-rainfall moisture (NRM) sources on dryland soils. This work also includes a discussion of the development and testing of a novel environmental sensing network, using custom nodes called EarthPods, and recommendations for the collection of future data from dryland sites to better understand NRM events in these regions. An analysis of weather conditions at two drylands sites suggest that nighttime RH is frequently high enough for NRM events to occur. Thesis results were unable to detect changes in soil water content based on historical weather data, likely due to instrument limitations (depth and sensitivity of soil moisture probes) and the small changes in soil moisture during NRM events. However, laboratory tests of EarthPod soil moisture sensors indicated strong sensitivity to T. Characterization of these T sensitivities provide opportunities to calibrate and correct soil moisture estimates using these sensors in the future. This work provides the foundation for larger biogeochemical sampling campaigns focusing on NRM in dryland systems.
ContributorsHanan, Desmond (Author) / Trembath-Reichert, Elizabeth (Thesis advisor) / Das, Jnaneshwar (Committee member) / Throop, Heather (Committee member) / Arizona State University (Publisher)
Created2021
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Description

Exploration of icy moons in the search for extra-terrestrial life is becoming a major focus in the NASA community. As such, the Exobiology Extant Life Surveyor (EELS) robot has been proposed to survey Saturn's Moon, Enceladus. EELS is a snake-like robot that will use helically grousered wheels to propel itself

Exploration of icy moons in the search for extra-terrestrial life is becoming a major focus in the NASA community. As such, the Exobiology Extant Life Surveyor (EELS) robot has been proposed to survey Saturn's Moon, Enceladus. EELS is a snake-like robot that will use helically grousered wheels to propel itself forward through the complex terrains of Enceladus. This moon's surface is composed of a mixture of snow and ice. Mobility research in these types of terrains is still under-explored, but must be done for the EELS robot to function. As such, this thesis will focus on the methodologies required to effectively simulate wheel interaction with cohesive media from a computational perspective. Three simulation tools will be briefly discussed: COMSOL Multiphysics, EDEM-ADAMS, and projectChrono. Next, the contact models used in projectChrono will be discussed and the methodology used to implement a custom Johnson Kendall Roberts (JKR) collision model will be explained. Finally, initial results from a cone penetrometer test in projectChrono will be shown. Qualitatively, the final simulations look correct, and further work is being done to quantitatively validate them as well as simulate more complex screw geometries.

ContributorsMick, Darwin (Author) / Marvi, Hamidreza (Thesis director) / Das, Jnaneshwar (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05