<p> Astronaut hand fatigue during Extravehicular Activity (EVA) and EVA training is a critical risk in human space exploration. Improved glove designs over the past forty years have reduced hand fatigue, but limitations of the technology prevent major improvements to reduce hand fatigue. Therefore, a mechanism to assist astronauts by reducing hand fatigue was explored. Many organizations have already developed exoskeletons to assist astronauts, but all mechanisms developed required electrically powered actuators and control systems to enhance grip strength. However, astronauts already possess the strength required to actuate the glove; what is needed is a method to reduce fatigue without introducing electromechanical complexity. A passive mechanical system was developed as a proof-of-concept to test the feasibility of an unpowered exoskeleton to maintain static grip around an object. The semi- rigid nature of an inflated pressure glove provided an ideal substrate to mount a mechanism and associated components to allow an astronaut to release his/her grip inside the glove while maintaining attitude, as the mechanism will keep the glove closed around an object.</p><p> Three prototypes were fabricated and tested to evaluate the architecture. The final two prototypes were tested on a real pressure suit glove at Final Frontier Design (FFD), and the third mechanism demonstrated attachment and basic operating principles. At University of California (UC) Davis, pressure glove analogs were fabricated from a baseball batting glove and polystyrene to simulate a real pressure glove without the risk of testing in a reduced pressure environment (i.e. a glove box). Testing of the third prototype showed a reduction in fatigue as measured by Maximum Voluntary Contraction (MVC) grip force over a 30 second period when the mechanism assisted gripping an object.</p>

<p> Robot mapping consists of using a robotic system to create the cartographic representation, or map, of an environment. This environment can have different shapes, sizes, and may be previously known or unknown. With a map, actions such as rescue, security, and construction, can be meticulously planned in terms of space for better efficiency and accuracy. The robotic platform designed in this industrial project is capable of autonomously creating the map of a previously unknown environment. Therefore, no human input is necessary in its operation. It uses wireless technology to communicate with the computing core, eliminating the necessity of cables to exchange data. In order to gather data from the environment, the mapping robot uses multiple sensors, namely three ultrasonic sensors, a gyroscope sensor, and a rotary encoder. A mapping application is created to receive the data and create a map of the environment. </p>

<p> Paralysis or paresis induced by upper motor neuron damage often leaves the lower limbs dysfunctional for basic activities such as walking and climbing stairs. Nearly five and one half million people in the United States, or approximately one in fifty, have some degree of paralysis [4]. The sustained duration and high level of impairment attributed to paralysis motivates research and development for technologies that alleviate the associated deficiencies.</p><p> The Indego exoskeleton (Parker-Hannifin, OH) and Chimera muscle stimulator are mechatronic devices developed for the reanimation of paretic limbs. Indego employs electric motors to actuate an orthosis for the restoration of controlled legged mobility, while the Chimera interfaces with the nervous system through transcutaneous electrical stimulation to administer functional electrical stimulation (FES). Described herein are rehabilitative intervention methods that: 1) enable paraplegics to ascend and descend stairs with a lower limb exoskeleton; 2) enhance exoskeleton assisted walking with supplemental FES to overcome moderate to severe spasticity; and 3) suppress clonus using FES during seated mobility. Chapter 2 describes the hardware developed for and/or employed in this research.</p><p> Chapter 3 describes the development and assessment of a controller for the Indego that enables paraplegics to ascend and descend stairs. The stair controller expands on a previous implementation of predefined trajectory tracking with an emulated passive state that enables gravity to extend the leg until it meets the next stair tread, then a trajectory is calculated in real-time to perform the intended task of stair ascent independent of step height. The ascent and descent controllers were evaluated by three paraplegic users who traversed numerous size stairs safely within two hours of tuning and training. The resulting controller enabled stair climbing with light exertion despite complete paraplegia.</p><p> Subjects with moderate to severe spasticity are typically ineligible for exoskeleton assisted gait due to pathological muscle activation that opposes exoskeleton mediated motion. A novel supplemental stimulation controller was implemented with the Indego exoskeleton and integrated Chimera stimulator in an effort to expand the inclusion criteria of exoskeletons to individuals with severe spasticity whereby FES enhances the synergy between muscles and motors. Chapter 4 explores the effects of spasticity and FES on robot mediated gait for paraplegics and describes the hybrid system&rsquo;s controller that enabled two paraplegic individuals with moderate to severe spasticity to achieve substantially improved gait kinematics.</p><p> Mobility impairment of paraplegia can also entail clonus, a self-exciting reflex that can manifest as involuntary shaking of the ankle, a common pathology experienced during wheelchair propulsion. Chapter 5 expands the frontiers of clonus research with the first reported evaluation methods wheelchair clonus 2 and the efficacy of a novel FES intervention to treat pathological clonus during wheelchair propulsion over rough terrain. The clonus intervention was shown to robustly suppress clonus. The treatment may provide a noninvasive and economical alternative to invasive and commonplace pharmacological interventions. </p><p> The remainder of the introduction serves to provide background information pertaining to the nervous system, neurological impairment and the state of the technologies used to restore deficiencies that arise from comorbidities of paralysis.</p><p>

Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 141-147).

<p> In dense cluttered environments, autonomous physical agents will face many challenges including limited routes, obstructed sensors, and limited communication. Equipping the agents with inter-agent communication alleviates some of the issues, but providing a mechanism for forming groups allows the agents to work together efficiently by avoiding congestion in tight areas and providing redundancy to accomplish a task. This thesis presents a framework for decentralized collaborative group formations and a framework for augmenting that with a more strategic centralized approach. This thesis will investigate a strategy for the formation of hierarchical ad-hoc groups that provide a simple interface for joining and splitting groups. After formation these groups will use peer to peer algorithms to share sensor data and perform distributed task allocation within the group. The groups can either be controlled by a static base-station or use a decentralized framework if communication to the base-station is lost. When communication is restored, the peer to peer algorithms will be used to spread the data to as many agents as possible to avoid data loss. A radio propagation model is also presented to simulate communication in indoor and simulated environments, as well as estimated propagation for use in path planning. This framework will also allow the agent's high level decision making to modify its role depending on group consensus. </p>

<p> Control of miniature mobile robots in unconstrained environments is an ongoing challenge. Miniature robots often exhibit nonlinear dynamics and obstacle avoidance introduces significant complexity in the control problem. In order to allow for coordinated movements, the robots must know their location relative to the other robots; this is challenging for very small robots operating under severe power and size constraints. This drastically reduces on-board digital processing power and suggests the need for a robust, compact distance sensor and a mixed-signal control system using Extended Kalman Filtering and Randomized Receding Horizon Control to support decentralized coordination of autonomous mini-robots. Error analysis of the sensor suggests that system clock timing jitter is the dominant contributor for sensor measurement uncertainty. Techniques for system identification of model parameters and the design of a mixed-signal computer for mobile robot position estimation are presented. </p>

<p>Sequential prediction problems arise commonly in many areas of robotics and information processing: e.g., predicting a sequence of actions over time to achieve a goal in a control task, interpreting an image through a sequence of local image patch classifications, or translating speech to text through an iterative decoding procedure. </p><p> Learning predictors that can reliably perform such sequential tasks is challenging. Specifically, as predictions influence future inputs in the sequence, the data-generation process and executed predictor are inextricably intertwined. This can often lead to a significant mismatch between the distribution of examples observed during training (induced by the predictor used to generate training instances) and test executions (induced by the learned predictor). As a result, naively applying standard supervised learning methods&mdash;that assume independently and identically distributed training and test examples&mdash;often leads to poor test performance and compounding errors: inaccurate predictions lead to untrained situations where more errors are inevitable. </p><p> This thesis proposes general iterative learning procedures that leverage interactions between the learner and teacher to provably learn good predictors for sequential prediction tasks. Through repeated interactions, our approaches can efficiently learn predictors that are robust to their own errors and predict accurately during test executions. Our main approach uses existing no-regret online learning methods to provide strong generalization guarantees on test performance. </p><p> We demonstrate how to apply our main approach in various sequential prediction settings: imitation learning, model-free reinforcement learning, system identification, structured prediction and submodular list predictions. Its efficiency and wide applicability are exhibited over a large variety of challenging learning tasks, ranging from learning video game playing agents from human players and accurate dynamic models of a simulated helicopter for controller synthesis, to learning predictors for scene understanding in computer vision, news recommendation and document summarization. We also demonstrate the applicability of our technique on a real robot, using pilot demonstrations to train an autonomous quadrotor to avoid trees seen through its onboard camera (monocular vision) when flying at low-altitude in natural forest environments. </p><p> Our results throughout show that unlike typical supervised learning tasks where examples of good behavior are sufficient to learn good predictors, interaction is a fundamental part of learning in sequential tasks. We show formally that some level of interaction is necessary, as without interaction, no learning algorithm can guarantee good performance in general. </p>

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1198. Adviser: Francesco Bullo. Includes bibliographical references (leaves 150-158) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3247. Adviser: Seth Hutchinson. Includes bibliographical references (leaves 132-136) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. / Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3248. Adviser: Mark W. Spong. Includes bibliographical references (leaves 112-116) Available on microfilm from Pro Quest Information and Learning.