This thesis presents a modular, low-cost, prototype inverted pendulum platform for undergraduate Aerospace control theory research and education. The platform's default configuration integrates a propeller and reaction wheel as its actuators, with multiple sensors available to measure the pendulum's states. The platform utilizes open-source software and hardware to power the actuators, and uses MATLAB/Simulink to streamline data collection. System identification techniques are applied to the platform, and the mathematical model of the pendulum is verified. Momentum exchange between the reaction wheel and propeller during closed-loop stabilization of the pendulum is demonstrated.

abstract: For a conventional quadcopter system with 4 planar rotors, flight times vary between 10 to 20 minutes depending on the weight of the quadcopter and the size of the battery used. In order to increase the flight time, either the weight of the quadcopter should be reduced or the battery size should be increased. Another way is to increase the efficiency of the propellers. Previous research shows that ducting a propeller can cause an increase of up to 94 % in the thrust produced by the rotor-duct system. This research focused on developing and testing a quadcopter having a centrally ducted rotor which produces 60 % of the total system thrust and 3 other peripheral rotors. This quadcopter will provide longer flight times while having the same maneuvering flexibility in planar movements. / Dissertation/Thesis / Experimental flight test for ductless quadcopter configuration / Masters Thesis Mechanical Engineering 2019

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 88-93).

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 109-115). Also available on the World Wide Web.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20374号 / 工博第4311号 / 新制||工||1668(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 引原 隆士, 教授 山川 宏, 教授 松尾 哲司 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices have emerged as a promising avenue for providing assistance to individuals with hand disabilities, especially those who may not achieve full recovery through surgical interventions. Nevertheless, prevailing exoskeleton glove systems encounter a multitude of challenges spanning design, control, and human-machine interaction. These challenges have given rise to limitations, such as unwieldy bulkiness, an absence of precise force control algorithms, limited portability, and an imbalance between lightweight construction and the essential functionalities required for everyday activities. To address these challenges, this research undertakes a comprehensive exploration of various dimensions within the exoskeleton glove system domain. This includes the intricate design of the finger linkage mechanism, meticulous kinematic analysis, strategic kinematic synthesis, nuanced dynamic modeling, thorough simulation, and adaptive control. The development of two distinct types of series elastic actuators, coupled with the creation of two diverse exoskeleton glove designs based on differing mechanisms, constitutes a pivotal aspect of this study. For the exoskeleton glove integrated with series elastic actuators, a sophisticated dynamic model is meticulously crafted. This endeavor involves the formulation of a mathematical framework to address backlash and the subsequent mitigation of friction forces. The pursuit of accurate force control culminates in the proposition of a data-driven model-free force predictive control policy, compared with a dynamic model-based force control methodology. Notably, the efficacy of the system is validated through meticulous clinical experiments. Meanwhile, the low-profile exoskeleton glove design with a novel mechanism engages in a further reduction of size and weight. This is achieved through the integration of a rigid coupling hybrid mechanism, yielding pronounced advancements in wearability and comfortability. A deep reinforcement learning approach is adopted for the real-time force planning control policies. A simulation environment is built to train the reinforcement learning agent. In summary, this research endeavors to surmount the constraints imposed by existing exoskeleton glove systems. By virtue of advancing mechanism design, innovating control strategies, enriching perception capabilities, and enhancing wearability, the ultimate goal is to augment the functionality and efficacy of these devices within the realm of assistive applications. / Doctor of Philosophy / This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices hold significant promise as valuable aids for patients with hand disabilities who may not achieve full recuperation through surgical interventions. However, the present iteration of exoskeleton glove systems encounters notable limitations in terms of design, control mechanisms, and human-machine interaction. Specifically, prevailing systems often suffer from bulkiness, lack of portability, and an inadequate equilibrium between lightweight construction and the essential functionalities imperative for daily tasks. To address these challenges, this research undertakes a comprehensive exploration of diverse facets within the exoskeleton glove system domain. This encompasses a detailed focus on mechanical design, control strategies, and human-machine interaction. To address wearability and comfort, two distinct exoskeleton glove variations are devised, each rooted in different mechanisms. An innovative data-driven model-free force predictive control policy is posited to enable accurate force regulation. Rigorous clinical experiments are conducted to meticulously validate the efficacy of the system. Furthermore, a novel mechanism is seamlessly integrated into the design of a new low-profile exoskeleton glove, thereby augmenting wearability and comfort by minimizing size and weight. A deep reinforcement learning based control agent, which is trained within a simulation environment, is devised to facilitate real-time autonomous force planning. In summary, the overarching objective of this research lies in rectifying the limitations inherent in existing exoskeleton glove systems. By spearheading advancements in mechanical design, control methodologies, perception capabilities, and wearability, the ultimate aim is to substantially enhance the functionality and overall efficacy of these devices within the sphere of assistive applications.

Tails are frequently utilized by animals to enhance their motion agility, dexterity, and versatility, such as a cheetah using its tail to change its body orientation while its legs are all off the ground and a monkey using its tail to stabilize its locomotion on branches. However, limited by technology and application scenarios, most existing legged robots do not include a robotic tail on board. This research aims to explore the possibilities of adding this missing part on legged robots and investigate the tail's functionalities on enhancing the agility, dexterity, and versatility of legged locomotion. In particular, this research focuses on animal-like serpentine tail structure, due to its larger workspace and higher dexterity. The overall research approach consists of two branches: a theoretical branch that focuses on dynamic modeling, analysis, and control of the legged robots with a serpentine robotic tail; and an empirical branch that focuses on hardware development and experiments of novel serpentine robotic tails and novel legged robots with tail. More specifically, the theoretical work includes modeling and control of a general quadruped platform and a general biped platform, equipped with one of the two general serpentine tail structures: an articulated-structure tail or a continuum-structure tail. Virtual work principle-based formulation was used to formulate the dynamic model. Both classic feedback linearization-based control and optimization-based control were used to coordinate the leg motions and the tail motion. Comparative studies on different tail structures as well as numerical analyses on robotic locomotion were performed to investigate the dynamic effects of serpentine robotic tails. The empirical work includes the developments and experiments of two novel serpentine robotic tail mechanisms and one first-of-its-kind quadruped robot ("VT Lemur") equipped with a serpentine robotic tail. To develop these novel robots, a systematic approach based on dynamic analysis was used. Various experiments were then conducted using the robot hardware. Both the theoretical and empirical results showed that the serpentine robotic tail has significant effects on enhancing the agility, dexterity, and versatility of legged robot motion. / Doctor of Philosophy / Quadruped robots have made impressive progresses over the past decade and now can easily achieve complicated, highly dynamic motions, such as the backflip of the MIT Mini Cheetah robot and the gymnastic parkour motions of the Atlas robot from Boston Dynamics, Inc. However, by looking at nature, many animals use tails to achieve highly agile and dexterous motions. For instance, monkeys are observed to use their tails to grasp branches and to balance their bodies during walking. Kangaroos are found to use their tails as additional limbs to propel and assist their locomotion. Cheetahs and kangaroo rats are thought to use their tails to help maneuvering. Therefore, this research aims to understand the fundamental principles behind these biological observations and develop novel legged robots equipped with a serpentine robotic tail. More specifically, this research aims to answer three key questions: (1) what are the functional benefits of adding a serpentine robotic tail to assist legged locomotion, (2) how do animals control their tail motion, and (3) how could we learn from these findings and enhance the agility, dexterity, and versatility of existing legged robots. To answer these questions, both theoretical investigations and experimental hardware testing were performed. The theoretical work establishes general dynamic models of legged robots with either an articulated tail or a continuum tail. A corresponding motion control framework was also developed to coordinate the leg and tail motions. To verify the proposed theoretical framework, a novel quadruped robot with a serpentine robotic tail was developed and tested.

This thesis presents mathematical modeling and control techniques that can be used to predict and specify performance of biologically inspired actuation systems called cellular actuators. Cellular actuators are modular units designed to be connected in bundles in manner similar to human muscle fibers. They are characterized by inherent compliance and large numbers of on-off discrete control inputs. In this thesis, mathematical tools are developed that connect the performance to the physical manifestation of the device. A camera positioner inspired by the human eye is designed to demonstrate how these tools can be used to create an actuator with a useful force-displacement characteristic. Finally, control architectures are presented that use discrete switching inputs to produce smooth motion of these systems despite an innate tendency toward oscillation. These are demonstrated in simulation and experiment.

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