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- All Subjects: robotics
- Genre: Masters Thesis
- Creators: Arizona State University
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
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
Visual navigation is a useful and important task for a variety of applications. As the prevalence of robots increase, there is an increasing need for energy-efficient navigation methods as well. Many aspects of efficient visual navigation algorithms have been implemented in the literature, but there is a lack of work on evaluation of the efficiency of the image sensors. In this thesis, two methods are evaluated: adaptive image sensor quantization for traditional camera pipelines as well as new event-based sensors for low-power computer vision.The first contribution in this thesis is an evaluation of performing varying levels of sensor linear and logarithmic quantization with the task of visual simultaneous localization and mapping (SLAM). This unconventional method can provide efficiency benefits with a trade off between accuracy of the task and energy-efficiency. A new sensor quantization method, gradient-based quantization, is introduced to improve the accuracy of the task. This method only lowers the bit level of parts of the image that are less likely to be important in the SLAM algorithm since lower bit levels signify better energy-efficiency, but worse task accuracy. The third contribution is an evaluation of the efficiency and accuracy of event-based camera intensity representations for the task of optical flow. The results of performing a learning based optical flow are provided for each of five different reconstruction methods along with ablation studies. Lastly, the challenges of an event feature-based SLAM system are presented with results demonstrating the necessity for high quality and high resolution event data. The work in this thesis provides studies useful for examining tradeoffs for an efficient visual navigation system with traditional and event vision sensors. The results of this thesis also provide multiple directions for future work.
ContributorsChristie, Olivia Catherine (Author) / Jayasuriya, Suren (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this work, the problem of multi-object tracking (MOT) is studied, particularly the challenges that arise from object occlusions. A solution based on a principled approximate dynamic programming approach called ADPTrack is presented. ADPTrack relies on existing MOT solutions and directly improves them. When matching tracks to objects at a particular frame, the proposed approach simulates executions of these existing solutions into future frames to obtain approximate track extensions, from which a comparison of past and future appearance feature information is leveraged to improve overall robustness to occlusion-based error. The proposed solution when applied to the renowned MOT17 dataset empirically demonstrates a 0.7% improvement in the association accuracy (IDF1 metric) over a state-of-the-art baseline that it builds upon while obtaining minor improvements with respect to all other metrics. Moreover, it is shown that this improvement is even more pronounced in scenarios where the camera maintains a fixed position. This implies that the proposed method is effective in addressing MOT issues pertaining to object occlusions.
ContributorsMusunuru, Pratyusha (Author) / Bertsekas, Dimitri (Thesis advisor) / Kambhampati, Subbarao (Thesis advisor) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2024
Description
The field of unmanned aerial vehicle, or UAV, navigation has been moving towards collision inclusive path planning, yet work has not been done to consider what a UAV is colliding with, and if it should or not. Therefore, there is a need for a framework that allows a UAV to consider what is around it and find the best collision candidate. The following work presents a framework that allows UAVs to do so, by considering what an object is and the properties associated with it. Specifically, it considers an object’s material and monetary value to decide if it is good to collide with or not. This information is then published on a binary occupancy map that contains the objects’ size and location with respect to the current position of the UAV. The intent is that the generated binary occupancy map can be used with a path planner to decide what the UAV should collide with. The framework was designed to be as modular as possible and to work with conventional UAV's that have some degree of crash resistance incorporated into their design. The framework was tested by using it to identify various objects that could be collision candidates or not, and then carrying out collisions with some of the objects to test the framework’s accuracy. The purpose of this research was to further the field of collision inclusive path planning by allowing UAVs to know, in a way, what they are intending to collide with and decide if they should or not in order to make safer and more efficient collisions.
ContributorsMolnar, Madelyn Helena (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas (Committee member) / Guo, Shenghan (Committee member) / Arizona State University (Publisher)
Created2024
Description
Shape memory alloys (SMAs) are a class of smart materials that can recover their predetermined shape when subjected to an appropriate thermal cycle. This unique property makes SMAs attractive for actuator applications, where the material’s phase transformation can be used to generate controlled motion or force. The actuator design leverages the one-way shape memory effect of NiTi (Nickel-Titanium) alloy wire, which contracts upon heating and recovers its original length when cooled. A bias spring opposes the SMA wire contraction, enabling a cyclical actuation motion. Thermal actuation is achieved through joule heating by passing an electric current through the SMA wire. This thesis presents the design of a compact, lightweight SMA-based actuator, providing controlled and precise motion in various engineering applications. A design of a soft actuator is presented exploiting the responses of the shape memory alloy (SMA) to trigger intrinsically mono-stable shape reconfiguration. The proposed class of soft actuators will perform bending actuation by selectively activating the SMA. The transition sequences were optimized by geometric parameterizations and energy-based criteria. The reconfigured structure is capable of arbitrary bending, which is reported here. The proposed class of robots has shown promise as a fast actuator or shape reconfigurable structure, which will bring new capabilities in future long-duration missions in space or undersea, as well as in bio-inspired robotics.
ContributorsShankar, Kaushik (Author) / Ma, Leixin (Thesis advisor) / Berman, Spring (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2024
Description
Electric vehicles (EVs) have emerged as a promising solution to reduce greenhouse gas emissions and dependency on fossil fuels in the transportation sector. However, limited
battery capacity remains a significant challenge, impacting range and overall performance.
This thesis explores the application of Nonlinear Model Predictive Control (NMPC)
techniques to optimize energy management in EVs. The study begins with a comprehensive
review of existing literature on EV energy optimization strategies and NMPC
methodologies. Subsequently, a detailed model of the EV's dynamics, including the
battery, motor, and vehicle dynamics, is developed to formulate the optimization problem.
The NMPC controller is designed to dynamically adjust the power distribution among
different vehicle components, such as the motor, battery, and regenerative braking system,
while considering constraints such as battery state-of-charge, vehicle speed, and road
conditions. Simulation studies are conducted to evaluate the performance of the proposed
NMPC-based energy optimization strategy under various driving scenarios and compare it
with conventional control strategies. The results demonstrate that NMPC offers superior
performance in terms of energy efficiency, range extension, and overall vehicle dynamics.
The findings of this research contribute to the advancement of energy optimization
techniques for EVs, paving the way for more efficient and sustainable transportation
systems in the future.
ContributorsGangwar, Harsh (Author) / Chen, Yan Dr. (Thesis advisor) / Zhao, Junfeng Dr. (Committee member) / Suo, Dajiang Dr. (Committee member) / Arizona State University (Publisher)
Created2024
Description
Origami, the Japanese art of paper folding, has come a long way from its traditionalroots. It’s now being used in modern engineering and design. In this thesis, I explored
multi-stable origami structures. These structures can hold multiple stable shapes, which
could have a big impact on various technologies. I aim to break down the complex ideas
behind these structures and explain their potential applications in a way that’s easy to understand.
In this research, I looked at the history of origami and recent developments in computational design to create and study multi-stable origami structures. I used computer tools like
parametric modeling software and finite element analysis to come up with new origami
designs. These tools helped me create, improve, and test these designs with a level of
accuracy and speed that hadn’t been possible before.
The process begins with the formulation of design principles rooted in the fundamental
geometry and mechanics of origami. Leveraging mathematical algorithms and optimization
techniques, diverse sets of origami crease patterns are generated, each tailored to exhibit
specific multi-stable behaviors. Through iterative refinement and simulation-driven design,
optimal solutions are identified, leading to the realization of intricate origami morphologies
that defy traditional design constraints.
Furthermore, the technological implications of multi-stable origami structures are explored across a spectrum of applications. In robotics, these structures serve as foundational
building blocks for reconfigurable mechanisms capable of adapting to dynamic environments and tasks. In aerospace engineering, they enable the development of lightweight,
deployable structures for space exploration and satellite deployment. In architecture, they
inspire innovative approaches to adaptive building envelopes and kinetic facades, enhancing sustainability and user experience.
In summary, this thesis presents a comprehensive exploration of multi-stable origami
structures, from their generation through computational design methodologies to their application across diverse technological domains. By pushing the boundaries of traditional
design paradigms and embracing the synergy between art, science, and technology, this
research opens new frontiers for innovation and creativity in the realm of origami-inspired
engineering.
ContributorsRayala, Sri Ratna Kumar (Author) / Ma, Leixin L (Thesis advisor) / Berman, Spring (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2024
Description
In this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about the robot's hardware and dynamics. In the domain of human-robot interaction, there exist different modalities of conveying information regarding the desired behavior of the robot, most commonly used are demonstrations, and preferences. While it is challenging for non-experts to provide demonstrations of robot behavior, works that consider preferences expressed as trajectory rankings lead to users providing noisy and possibly conflicting information, leading to slow adaptation or system failures. The end user can be expected to be familiar with the dynamics and how they relate to their desired objectives through repeated interactions with the system. However, due to inadequate knowledge about the system dynamics, it is expected that the user would find it challenging to provide feedback on all dimension's of the system's behavior at all times. Thus, the key innovation of this work is to enable users to provide partial instead of completely specified preferences as with traditional methods that learn from user preferences. In particular, I consider partial preferences in the form of preferences over plant dynamic parameters, for which I propose Adaptive User Control (AUC) of robotic systems. I leverage the correlations between the observed and hidden parameter preferences to deal with incompleteness. I use a sparse Gaussian Process Latent Variable Model formulation to learn hidden variables that represent the relationships between the observed and hidden preferences over the system parameters. This model is trained using Stochastic Variational Inference with a distributed loss formulation. I evaluate AUC in a custom drone-swarm environment and several domains from DeepMind control suite. I compare AUC with the state-of-the-art preference-based reinforcement learning methods that are utilized with user preferences. Results show that AUC outperforms the baselines substantially in terms of sample and feedback complexity.
ContributorsBiswas, Upasana (Author) / Zhang, Yu (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Berman, Spring (Committee member) / Liu, Lantao (Committee member) / Arizona State University (Publisher)
Created2023
Description
In nature, some animals have an exoskeleton that provides protection, strength, and stability to the organism, but in engineering, an exoskeleton refers to a device that augments or aids human ability. However, the method of controlling these devices has been a challenge historically. Depending on the objective, control systems for exoskeletons have ranged from devices as simple spring-loaded systems to using sensors such as electromyography (EMG). Despite EMGs being very common, force sensing resistors (FSRs) can be used instead. There are multiple types of exoskeletons that target different areas of the human body, and the targeted area depends on the need of the device. Usually, the devices are developed for either medical or military usage; for this project, the focus is on medical development of an automated elbow joint to assist in rehabilitation. This thesis is a continuation of my ASU Barrett honors thesis, Upper-Extremity Exoskeleton. While working on my honors thesis, I helped develop a design for an upper extremity exoskeleton based on the Wilmer orthosis design for Mayo Clinic. Building upon the design of an orthosis, for the master’s thesis, I developed an FSR control system that is designed using a Wheatstone bridge circuit that can provide a clean reliable signal as compared to the current EMG setup.
ContributorsCarlton, Bryan (Author) / Sugar, Thomas (Thesis advisor) / Aukes, Daniel (Committee member) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
This thesis presents a study on the user adaptive variable impedance control of a wearable ankle robot for robot-aided rehabilitation with a primary focus on enhancing accuracy and speed. The controller adjusts the impedance parameters based on the user's kinematic data to provide personalized assistance. Bayesian optimization is employed to minimize an objective function formulated from the user's kinematic data to adapt the impedance parameters per user, thereby enhancing speed and accuracy. Gaussian process is used as a surrogate model for optimization to account for uncertainties and outliers inherent to human experiments. Student-t process based outlier detection is utilized to enhance optimization robustness and accuracy. The efficacy of the optimization is evaluated based on measures of speed, accuracy, and effort, and compared with an untuned variable impedance controller during 2D curved trajectory following tasks. User effort was measured based on muscle activation data from the tibialis anterior, peroneus longus, soleus, and gastrocnemius muscles. The optimized controller was evaluated on 15 healthy subjects and demonstrated an average increase in speed of 9.85% and a decrease in deviation from the ideal trajectory of 7.57%, compared to an unoptimized variable impedance controller. The strategy also reduced the time to complete tasks by 6.57%, while maintaining a similar level of user effort.
ContributorsManoharan, Gautham (Author) / Lee, Hyunglae (Thesis advisor) / Berman, Spring (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023