Matching Items (30)
Description
Regolith excavation systems are the enabling technology that must be developed in order to implement many of the plans for in-situ resource utilization (ISRU) that have been developed in recent years to aid in creating a lasting human presence on the surface of the Moon, Mars, and other celestial bodies. The majority of proposed ISRU excavation systems are integrated onto a wheeled mobility system, however none yet have proposed the use of a screw-propelled vehicle, which has the potential to augment and enhance the capabilities of the excavation system. As a result, CASPER, a novel screw-propelled excavation rover is developed and analyzed to determine its effectiveness as a ISRU excavation system. The excavation rate, power, velocity, cost of transport, and a new parameter, excavation transport rate, are analyzed for various configurations of the vehicle through mobility and excavation tests performed in silica sand. The optimal configuration yielded a 28.4 kg/hr excavation rate and11.2 m/min traverse rate with an overall system mass of 3.4 kg and power draw of26.3 W. CASPER’s mobility and excavation performance results are compared to four notable proposed ISRU excavation systems of various types. The results indicate that this architecture shows promise as an ISRU excavator because it provides significant excavation capability with low mass and power requirements.
ContributorsGreen, Marko (Author) / Marvi, Hamid (Thesis advisor) / Emady, Heather (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2021
Description
As technological advancements in silicon, sensors, and actuation continue, the development of robotic swarms is shifting from the domain of science fiction to reality. Many swarm applications, such as environmental monitoring, precision agriculture, disaster response, and lunar prospecting, will require controlling numerous robots with limited capabilities and information to redistribute among multiple states, such as spatial locations or tasks. A scalable control approach is to program the robots with stochastic control policies such that the robot population in each state evolves according to a mean-field model, which is independent of the number and identities of the robots. Using this model, the control policies can be designed to stabilize the swarm to the target distribution. To avoid the need to reprogram the robots for different target distributions, the robot control policies can be defined to depend only on the presence of a “leader” agent, whose control policy is designed to guide the swarm to a particular distribution. This dissertation presents a novel deep reinforcement learning (deep RL) approach to designing control policies that redistribute a swarm as quickly as possible over a strongly connected graph, according to a mean-field model in the form of the discrete-time Kolmogorov forward equation. In the leader-based strategies, the leader determines its next action based on its observations of robot populations and shepherds the swarm over the graph by probabilistically repelling nearby robots. The scalability of this approach with the swarm size is demonstrated with leader control policies that are designed using two tabular Temporal-Difference learning algorithms, trained on a discretization of the swarm distribution. To improve the scalability of the approach with robot population and graph size, control policies for both leader-based and leaderless strategies are designed using an actor-critic deep RL method that is trained on the swarm distribution predicted by the mean-field model. In the leaderless strategy, the robots’ control policies depend only on their local measurements of nearby robot populations. The control approaches are validated for different graph and swarm sizes in numerical simulations, 3D robot simulations, and experiments on a multi-robot testbed.
ContributorsKakish, Zahi Mousa (Author) / Berman, Spring (Thesis advisor) / Yong, Sze Zheng (Committee member) / Marvi, Hamid (Committee member) / Pavlic, Theodore (Committee member) / Pratt, Stephen (Committee member) / Ben Amor, Hani (Committee member) / Arizona State University (Publisher)
Created2021
Description
JOOEE is a cube-shaped lunar robot with a simple yet robust design. JOOEE ishermetically sealed from its environment with no external actuators. Instead, JOOEE spins
three internal orthogonal flywheels to accumulate angular momentum and uses a solenoid
brake at each wheel to transfer the angular momentum to the body. This procedure allows
JOOEE to jump and hop along the lunar surface. The sudden transfer in angular momentum
during braking causes discontinuities in JOOEE’s dynamics that are best described using
a hybrid control framework. Due to the irregular methods of locomotion, the limited
resources on the lunar surface, and the unique mission objectives, optimal control profiles
are desired to minimize performance metrics such as time, energy, and impact velocity
during different maneuvers. This paper details the development of an optimization tool that
can handle JOOEE’s dynamics including the design of a hybrid control framework,
dynamics modeling and discretization, optimization cost functions and constraints, model
validation, and code acceleration techniques.
ContributorsBreaux, Christopher (Author) / Yong, Sze Z (Thesis advisor) / Marvi, Hamid (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many medical procedures, like surgeries, deal with the physical manipulation of sensitive internal tissues. Over time, new medical tools and techniques have been developed to improve the safety and efficacy of these procedures. Despite the leaps and bounds of progress made up to the present day, three major obstacles (among others) persist, bleeding, pain, and the risk of infection. Advances in minimally invasive treatments have transformed many formerly risky surgical procedures into very safe and highly successful routines. Minimally invasive surgeries are characterized by small incision profiles compared to the large incisions in open surgeries, minimizing the aforementioned issues. Minimally invasive procedures lead to several benefits, such as shorter recovery time, fewer complications, and less postoperative pain. In minimally invasive surgery, doctors use various techniques to operate with less damage to the body than open surgery. Today, these procedures have an established, successful history and promising future. Steerable needles are one of the tools proposed for minimally invasive operations. Needle steering is a method for guiding a long, flexible needle through curved paths to reach targets deep in the body, eliminating the need for large incisions. In this dissertation, we present a new needle steering technology: magnetic needle steering. This technology is proposed to address the limitations of conventional needle steering that hindered its clinical applications. Magnetic needle steering eliminates excessive tissue damage, restrictions of the minimum radius of curvature, and the need for a complex nonlinear model, to name a few. It also allows fabricating the needle shaft out of soft and tissue-compliant materials.
This is achieved by first developing an electromagnetic coil system capable of producing desired magnetic fields and gradients; then, a magnetically actuated needle is designed, and its effectiveness is experimentally evaluated. Afterward, the scalability of this technique was tested using permanent magnets controlled with a robotic arm.
Furthermore, different configurations of permanent magnets and their influence on the magnetic field are investigated, enabling the possibility of designing a desired magnetic field for a specific surgical procedure and operation on a particular organ. Finally, potential future directions towards animal studies and clinical trials are discussed.
ContributorsIlami, Mahdi (Author) / Marvi, Hamid (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Nikkhah, Mehdi (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Current robotic systems have difficulties traversing and interacting with complex and deformable terrains, such as sand, mud, and water. This research intends to find hierarchical concepts that can be implemented into robotic systems for efficient locomotion by studying animal interactions with these terrains. Due to specific biological characteristics and environmental factors, the basilisk lizard is one animal that can transition easily between many types of terrain. This research will investigate the dynamics and kinematics of the basilisk lizard as it runs on the surface of water. Specifically, a fluid dynamic force platform has been designed and developed that will directly measure the forces exerted by the animal’s feet as it runs across the water. This platform will be used in conjunction with a motion capture system to characterize the basilisk lizard movements. This report examines the design and development of the force platform, the characterization of the frequencies of the platform leading to validation, and presents observations from preliminary lizard experiments with the setup.
ContributorsGambatese, Marcus B (Author) / Marvi, Hamid (Thesis director) / Bagheri, Hosain (Committee member) / Mechanical and Aerospace Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Advancements in the field of design and control of lower extremity robotics requires a comprehensive understanding of the underlying mechanics of the human ankle. The ankle joint acts as an essential interface between the neuromuscular system of the body and the physical world, especially during locomotion. This paper investigates how the modulation of ankle stiffness is altered throughout the stance phase of the gait cycle depending on the environment the ankle is interacting with. Ten young healthy subjects with no neurological impairments or history of ankle injury were tested by walking over a robotic platform which collected torque and position data. The platform performed a perturbation on the ankle at 20%, 40%, and 60% of their stance phase in order to estimate ankle stiffness and evaluate if the environment plays a role on its modulation. The platform provided either a rigid environment or a compliant environment in which it was compliant and deflected according to the torque applied to the platform. Subjects adapted in different ways to achieve balance in the different environments. When comparing the environments, subjects modulated their stiffness to either increase, decrease, or remain the same. Notably, stiffness as well as the subjects’ center of pressure was found to increase with time as they transitioned from late loading to terminal stance (heel strike to toe-off) regardless of environmental conditions. This allowed for a model of ankle stiffness to be developed as a function of center of pressure, independent of whether a subject is walking on the rigid or compliant environment. The modulation of stiffness parameters characterized in this study can be used in the design and control of lower extremity robotics which focus on accurate biomimicry of the healthy human ankle. The stiffness characteristics can also be used to help identify particular ankle impairments and to design proper treatment for individuals such as those who have suffered from a stroke or MS. Changing environments is where a majority of tripping incidents occur, which can lead to significant injuries. For this reason, studying healthy ankle behavior in a variety of environments is of particular interest.
ContributorsBliss, Clayton F (Author) / Lee, Hyunglae (Thesis director) / Marvi, Hamid (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The entirely soft-tissue anatomy of the octopus arm provides the animal with a large amount of freedom of movement, while still allowing the specimen to support itself despite the lack of a skeletal system. This is made possible through the use of various muscle layers within the octopus arm, which act as muscular hydrostats. Magnetic Resonance imaging of the octopus arm was employed to view the muscle layers within the octopus arm and observe trends and differences in these layers at the proximal, middle, and distal portions of the arms. A total of 39 arms from 6 specimens were imaged to give 112 total imaged sections (38 proximal, 37 middle, 37 distal). Significant increases in both the internal longitudinal muscle layer and the nervous core were found between the proximal and middle, proximal and distal, and middle and distal sections of the arms. This could reflect selection for these structures distally in the octopus arm for predator or other noxious stimuli avoidance. A significant decrease in the transverse muscle layer was found in the middle and distal sections of the arms. This could reflect selection for elongation in the proximal portion of the octopus arm or could be the result of selection for the internal longitudinal muscle layer and nervous core distally. Previous studies on Octopus vulgaris showed a preference for using the proximal arms in the pushing movement of crawling and a preference for using the anterior arms in exploring behaviors (Levy et al., 2015 and Byrne et al., 2006). Differences between the anterior and posterior arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, reflecting a lack of structure-function relationships. This could also be due to a low sample size. Differences between the left and right arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, supporting previous evidence that left versus right eye and arm preferences in octopus are not population-wide, but individual. Some slight trends can be found for individual arms, but the sample size was too small to make definitive statements regarding differences among specific arms.
ContributorsRoy, Cayla C (Author) / Fisher, Rebecca (Thesis director) / Marvi, Hamid (Committee member) / Cherry, Brian (Committee member) / Watts College of Public Service & Community Solut (Contributor) / School of Life Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Mechanical impedance is a concept that is used to model biomechanical propertiesof human joints. These models can then be utilized to provide insight into the inner
workings of the human neuromuscular system or to provide insight into how to best
design controllers for robotic applications that either attempt to mimic capabilities of
the human neuromuscular system or physically interact with it. To further elucidate
patterns and properties of how the human neuromuscular system modulates mechanical
impedance at the human ankle joint, multiple studies were conducted. The first
study was to assess the ability of linear regression models to characterize the change
in stiffness - a component of mechanical impedance - seen at the human ankle during
the stance phase of walking in the Dorsiflexion-Plantarflexion (DP) direction. A
collection of biomechanical variables were used as input variables. The R^2 value of
the best performing model was 0.71. The second and third studies were performed to
showcase the ability of a newly developed twin dual-axis platform, which goes beyond
the limits of a single dual-axis platform, to quantify bilateral stiffness properties. The
second study quantified the bilateral mechanical stiffness of the human ankle joint
for healthy able-bodied subjects during the stance phase of walking and during quiet
standing in both the DP and inversion-eversion directions. Subjects showed a high
level of subject specific symmetry. Lastly, a similar bilateral ankle characterization
study was conducted on a set of subjects with multiple sclerosis, but only during
quiet standing and in the DP direction. Results showed a high level of discrepancy
between the subject’s most-affected and least-affected limbs with a larger range and
variance than in the healthy population.
ContributorsRussell, Joshua (Author) / Lee, Hyunglae (Thesis advisor) / Honeycutt, Claire (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2022
Description
As the explorations beyond the Earth's boundaries continue to evolve, researchers and engineers strive to develop versatile technologies capable of adapting to unknown space conditions. For instance, the utilization of Screw-Propelled Vehicles (SPVs) and robotics that utilize helical screws propulsion to transverse planetary bodies is a growing area of interest. An example of such technology is the Extant Exobiology Life Surveyor (EELS), a snake-like robot currently developed by the NASA Jet Propulsion Laboratory (JPL) to explore the surface of Saturn’s moon, Enceladus. However, the utilization of such a mechanism requires a deep and thorough understanding of screw mobility in uncertain conditions. The main approach to exploring screw dynamics and optimal design involves the utilization of Discrete Element Method (DEM) simulations to assess interactions and behavior of screws when interacting with granular terrains. In this investigation, the Simplified Johnson-Kendall-Roberts (SJKR) model is implemented into the utilized simulation environment to account for cohesion effects similar to what is experienced on celestial bodies like Enceladus. The model is verified and validated through experimental and theoretical testing. Subsequently, the performance characteristics of screws are explored under varying parameters, such as thread depth, number of screw starts, and the material’s cohesion level. The study has examined significant relationships between the parameters under investigation and their influence on the screw performance.
ContributorsAbdelrahim, Mohammad (Author) / Marvi, Hamid (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2023
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023