Matching Items (15)
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- All Subjects: robotics
- Creators: Aukes, Daniel
Description
What if there is a way to integrate prosthetics seamlessly with the human body and robots could help improve the lives of children with disabilities? With physical human-robot interaction being seen in multiple aspects of life, including industry, medical, and social, how these robots are interacting with human becomes even more important. Therefore, how smoothly the robot can interact with a person will determine how safe and efficient this relationship will be. This thesis investigates adaptive control method that allows a robot to adapt to the human's actions based on the interaction force. Allowing the relationship to become more effortless and less strained when the robot has a different goal than the human, as seen in Game Theory, using multiple techniques that adapts the system. Few applications this could be used for include robots in physical therapy, manufacturing robots that can adapt to a changing environment, and robots teaching people something new like dancing or learning how to walk after surgery.
The experience gained is the understanding of how a cost function of a system works, including the tracking error, speed of the system, the robot’s effort, and the human’s effort. Also, this two-agent system, results into a two-agent adaptive impedance model with an input for each agent of the system. This leads to a nontraditional linear quadratic regulator (LQR), that must be separated and then added together. Thus, creating a traditional LQR. This new experience can be used in the future to help build better safety protocols on manufacturing robots. In the future the knowledge learned from this research could be used to develop technologies for a robot to allow to adapt to help counteract human error.
The experience gained is the understanding of how a cost function of a system works, including the tracking error, speed of the system, the robot’s effort, and the human’s effort. Also, this two-agent system, results into a two-agent adaptive impedance model with an input for each agent of the system. This leads to a nontraditional linear quadratic regulator (LQR), that must be separated and then added together. Thus, creating a traditional LQR. This new experience can be used in the future to help build better safety protocols on manufacturing robots. In the future the knowledge learned from this research could be used to develop technologies for a robot to allow to adapt to help counteract human error.
ContributorsBell, Rebecca C (Author) / Zhang, Wenlong (Thesis advisor) / Chiou, Erin (Committee member) / Aukes, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
Robotic rehabilitation for upper limb post-stroke recovery is a developing technology. However, there are major issues in the implementation of this type of rehabilitation, issues which decrease efficacy. Two of the major solutions currently being explored to the upper limb post-stroke rehabilitation problem are the use of socially assistive rehabilitative robots, robots which directly interact with patients, and the use of exoskeleton-based systems of rehabilitation. While there is great promise in both of these techniques, they currently lack sufficient efficacy to objectively justify their costs. The overall efficacy to both of these techniques is about the same as conventional therapy, yet each has higher overhead costs that conventional therapy does. However there are associated long-term cost savings in each case, meaning that the actual current viability of either of these techniques is somewhat nebulous. In both cases, the problems which decrease technique viability are largely related to joint action, the interaction between robot and human in completing specific tasks, and issues in robot adaptability that make joint action difficult. As such, the largest part of current research into rehabilitative robotics aims to make robots behave in more "human-like" manners or to bypass the joint action problem entirely.
ContributorsRamakrishna, Vijay Kambhampati (Author) / Helms Tillery, Stephen (Thesis director) / Buneo, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / W. P. Carey School of Business (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
This dissertation introduces and examines Soft Curved Reconfigurable Anisotropic Mechanisms (SCRAMs) as a solution to address actuation, manufacturing, and modeling challenges in the field of soft robotics, with the aim of facilitating the broader implementation of soft robots in various industries. SCRAM systems utilize the curved geometry of thin elastic structures to tackle these challenges in soft robots. SCRAM devices can modify their dynamic behavior by incorporating reconfigurable anisotropic stiffness, thereby enabling tailored locomotion patterns for specific tasks. This approach simplifies the actuation of robots, resulting in lighter, more flexible, cost-effective, and safer soft robotic systems. This dissertation demonstrates the potential of SCRAM devices through several case studies. These studies investigate virtual joints and shape change propagation in tubes, as well as anisotropic dynamic behavior in vibrational soft twisted beams, effectively demonstrating interesting locomotion patterns that are achievable using simple actuation mechanisms. The dissertation also addresses modeling and simulation challenges by introducing a reduced-order modeling approach. This approach enables fast and accurate simulations of soft robots and is compatible with existing rigid body simulators. Additionally, this dissertation investigates the prototyping processes of SCRAM devices and offers a comprehensive framework for the development of these devices. Overall, this dissertation demonstrates the potential of SCRAM devices to overcome actuation, modeling, and manufacturing challenges in soft robotics. The innovative concepts and approaches presented have implications for various industries that require cost-effective, adaptable, and safe robotic systems. SCRAM devices pave the way for the widespread application of soft robots in diverse domains.
ContributorsJiang, Yuhao (Author) / Aukes, Daniel (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Srivastava, Siddharth (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 the design and testing of a soft robotic device for water utility pipeline inspection. The preliminary findings of this new approach to conventional methods of pipe inspection demonstrate that a soft inflatable robot can successfully traverse the interior space of a range of diameter pipes using pneumatic and without the need to adjust rigid, mechanical components. The robot utilizes inflatable soft actuators with an adjustable radius which, when pressurized, can provide a radial force, effectively anchoring the device in place. Additional soft inflatable actuators translate forces along the center axis of the device which creates forward locomotion when used in conjunction with the radial actuation. Furthermore, a bio-inspired control algorithm for locomotion allows the robot to maneuver through a pipe by mimicking the peristaltic gait of an inchworm. This thesis provides an examination and evaluation of the structure and behavior of the inflatable actuators through computational modeling of the material and design, as well as the experimental data of the forces and displacements generated by the actuators. The theoretical results are contrasted with/against experimental data utilizing a physical prototype of the soft robot. The design is anticipated to enable compliant robots to conform to the space offered to them and overcome occlusions from accumulated solids found in pipes. The intent of the device is to be used for inspecting existing pipelines owned and operated by Salt River Project, a Phoenix-area water and electricity utility provider.
ContributorsAdams, Wade Silas (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern.
First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation.
To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.
First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation.
To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.
ContributorsHaggerty, Zz Mae (Author) / Berman, Spring M (Thesis advisor) / Aukes, Daniel (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2018
Description
Unmanned aerial vehicles (UAVs) have revolutionized various fields, but their use in dynamic environments is still limited due to safety concerns arising from sensor malfunctions and localization errors. Inspired by birds, which exhibit unparalleled maneuverability and adaptability to dynamic environments by synergizing mechanical compliance with control, this research focused on developing a new generation of bio-inspired soft/compliant UAVs with mechanical intelligence that can withstand collisions and enable aerial interaction. The proposed approach is to harness collision energies and switch into the next favorable configuration, which helps retain stability and successfully fly even in the presence of external forces. It investigated various types of active/passive reconfigurable UAVs to demonstrate this idea. The first approach looked into designs of compliant reconfigurable quadrotors by employing springs which can reduce their dimension under external forces, thereby sustaining 2D planar collision forces and enabling flights through narrow gaps in a squeeze-and-fly manner. Next, fabric-based soft UAVs made of pneumatic beams were successfully explored to design lightweight and collision-resilient quadrotors to demonstrate 3D collision-resilience and impact-based perching. This research contributes to thorough modeling of the unique dynamics of these reconfigurable quadrotors and proposes various adaptive and learning-based controllers for robust low-level tracking. Finally, these controllers were integrated into a novel collision-inclusive motion planning framework based-on optimal control theory to perform physical interaction tasks, such as contact-based navigation, mapping, and inspection. In essence, this research redefines safety for UAVs and expands their capabilities for contact-rich tasks.
ContributorsPatnaik, Karishma (Author) / Zhang, Wennlong (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Aukes, Daniel (Committee member) / Arizona State University (Publisher)
Created2023
Description
Foldable robots have gained popularity in recent years due to their versatility and portability. However, the use of composite hinges in these systems has posed challenges in terms of manufacturing complexity and cost. Historically, single material robots were very limited due to the fact that both the link and the hinge are made with the same material and striking a balance with stiffness of the link and flexibility of the hinge has been very difficult. Hinges would undergo fatigue within hundreds of cycles and show non-linear wear and physical properties. This research proposes an innovative approach to simplify foldable robotics by replacing composite hinges with single material flexure hinges. The proposed hinges are manufactured using a CO2 laser cutter and are designed to enhance performance and reduce costs over previous single-layer-hinges. A mathematical model has been developed to predict the behavior of the hinges and tune them to the desired requirements. Experimental results show that the proposed hinges offer improved flexibility and durability compared to single-layer hinges, while reducing the manufacturing cost and complexity associated with multi-layer hinges. This research contributes to the advancement of foldable robotics by offering a simplified and cost-effective solution that can foster innovation in various applications.
ContributorsKanchan, Viraj (Author) / Aukes, Daniel (Thesis advisor) / Redkan, Sangram (Committee member) / Sugar, Thmas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Undulatory locomotion is a unique form of swimming that generates thrust through the propagation of a wave through a fish’s body. The proposed device utilizes a constrained compliant material with a single actuator to generate an undulatory motion. This paper draws inspiration from Anguilliformes and discusses the kinematics and dynamics of wave propagation of an underwater robot. A variety of parameters are explored through modeling and are optimized for thrust generation to better understand the device. This paper validates the theoretical spine behavior through experimentation and provides a path forward for future development in device optimization for various applications. Previous work developed devices that utilized either paired soft actuators or multiple redundant classical actuators that resulted in a complex prototype with intricate controls. The work of this paper contrasts with prior work in that it aims to achieve undulatory motion through passive actuation from a single actively driven point which simplifies the control. Through this work, the goal is to further explore low-cost soft robotics via bistable mechanisms, continuum material properties, and simplified modeling practices.
ContributorsKwan, Anson (Author) / Aukes, Daniel (Thesis advisor) / Zhang, Wenlong (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2023
Description
The goal of reinforcement learning is to enable systems to autonomously solve tasks in the real world, even in the absence of prior data. To succeed in such situations, reinforcement learning algorithms collect new experience through interactions with the environment to further the learning process. The behaviour is optimized by maximizing a reward function, which assigns high numerical values to desired behaviours. Especially in robotics, such interactions with the environment are expensive in terms of the required execution time, human involvement, and mechanical degradation of the system itself. Therefore, this thesis aims to introduce sample-efficient reinforcement learning methods which are applicable to real-world settings and control tasks such as bimanual manipulation and locomotion. Sample efficiency is achieved through directed exploration, either by using dimensionality reduction or trajectory optimization methods. Finally, it is demonstrated how data-efficient reinforcement learning methods can be used to optimize the behaviour and morphology of robots at the same time.
ContributorsLuck, Kevin Sebastian (Author) / Ben Amor, Hani (Thesis advisor) / Aukes, Daniel (Committee member) / Fainekos, Georgios (Committee member) / Scholz, Jonathan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019