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- Genre: Doctoral Dissertation
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
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
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
Description
This dissertation studies the methods to enhance the performance of foldable robots manufactured by laminated techniques. This class of robots are unique in their manufacturing process, which involves cutting and staking up thin layers of different materials with various stiffness. While inheriting the advantages of soft robots -- low weight, affordable manufacturing cost and a fast prototyping process -- a wider range of actuators is available to these mechanisms, while modeling their behavior requires less computational cost.The fundamental question this dissertation strives to answer is how to decode and leverage the effect of material stiffness in these robots. These robots' stiffness is relatively limited due to their slender design, specifically at larger scales. While compliant robots may have inherent advantages such as being safer to work around, this low rigidity makes modeling more complex. This complexity is mostly contained in material deformation since the conventional actuators such as servo motors can be easily leveraged in these robots. As a result, when introduced to real-world environments, efficient modeling and control of these robots are more achievable than conventional soft robots.
Various approaches have been taken to design, model, and control a variety of laminate robot platforms by investigating the effect of material deformation in prototypes while they interact with their working environments. The results obtained show that data-driven approaches such as experimental identification and machine learning techniques are more reliable in modeling and control of these mechanisms. Also, machine learning techniques for training robots in non-ideal experimental setups that encounter the uncertainties of real-world environments can be leveraged to find effective gaits with high performance. Our studies on the effect of stiffness of thin, curved sheets of materials has evolved into introducing a new class of soft elements which we call Soft, Curved, Reconfigurable, Anisotropic Mechanisms (SCRAMs). Like bio-mechanical systems, SCRAMs are capable of re-configuring the stiffness of curved surfaces to enhance their performance and adaptability. Finally, the findings of this thesis show promising opportunities for foldable robots to become an alternative for conventional soft robots since they still offer similar advantages in a fraction of computational expense.
ContributorsSharifzadeh, Mohammad (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Soft robots currently rely on additional hardware such as pumps, high voltage supplies,light generation sources, and magnetic field generators for their operation. These components
resist miniaturization; thus, embedding them into small-scale soft robots is challenging.
This issue limits their applications, especially in hyper-redundant mobile robots. This
dissertation aims at addressing some of the challenges associated with creating miniature,
untethered soft robots that can function without any attachment to external power supplies
or receiving any control signals from outside sources. This goal is accomplished by introducing
a soft active material and a manufacturing method that together, facilitate the
miniaturization of soft robots and effectively supports their autonomous, mobile operation
without any connection to outside equipment or human intervention.
The soft active material presented here is a hydrogel based on a polymer called poly(Nisopropylacrylamide)
(PNIPAAm). This hydrogel responds to changes in the temperature
and responds by expanding or contracting. A major challenge regarding PNIPAAm-based
hydrogels is their slow response. This challenge is addressed by introducing a mixedsolvent
photo-polymerization technique that alters the pore structure of the hydrogel and
facilitates the water transport and thus the rate of volume change. Using this technique,
the re-swelling response time of hydrogels is reduced to 2:4min – over 25 times faster
than hydrogels demonstrated previously. The material properties of hydrogels including
their response rate and Young’s modulus are tuned simultaneously. The one-step photopolymerization
using UV light is performed in under 15 sec, which is a significant improvement
over thermo-polymerization, which takes anywhere between a few minutes to
several hours. Photopolymerization is key towards simplifying recipes, improving access
to these techniques, and making them tractable for iterative design processes.
To address the manufacturing challenges, soft voxel actuators (SVAs) are presented.
SVAs are actuated by electrical currents through Joule heating. SVAs weighing only 100 mg require small footprint microcontrollers for their operation which can be embedded
in the robotic system. The advantages of hydrogel-based SVAs are demonstrated through
different robotic platforms namely a hyper-redundant manipulator with 16 SVAs, an untethered
miniature robot for mobile underwater applications using 8 SVAs, and a gripper
using 32 SVAs.
ContributorsKhodambashi, Roozbeh (Author) / Aukes, Daniel (Thesis advisor) / Sugar, Thomas (Committee member) / Nam, Changho (Committee member) / Arizona State University (Publisher)
Created2021