<div>This dissertation focuses on developing a series of mathematical models to understand the role of urban transportation system, urban mobility and information dissemination in the spreading process of infectious diseases within metropolitan areas. Urban transportation system serves as the catalyst of disease contagion since it provides the mobility for bringing people to participate in intensive urban activities and has high passenger volume and long commuting time which facilitates the spread of contagious diseases. In light of significant needs in understanding the connection between disease contagion and the urban transportation systems, both macroscopic and microscopic models are developed and the dissertation consists of three main parts. </div><div></div><div>The first part of the dissertation aims to model the macroscopic level of disease spreading within urban transportation system based on compartment models. Nonlinear dynamic systems are developed to model the spread of infectious disease with various travel modes, compare models with and without contagion during travel, understand how urban transportation system may facilitate or impede epidemics, and devise control strategies for mitigating epidemics at the network level. The hybrid automata is also introduced to account for systems with different levels of control and with uncertain initial epidemic size, and reachability analysis is used to over-approximate the disease trajectories of the nonlinear systems. The 2003 Beijing SARS data are used to validate the effectiveness of the model. In addition, comprehensive numerical experiments are conducted to understand the importance of modeling travel contagion during urban disease outbreaks and develop control strategies for regulating the entry of urban transportation system to reduce the epidemic size. </div><div></div><div>The second part of the dissertation develops a data-driven framework to investigate the disease spreading dynamics at individual level. In particular, the contact network generation algorithm is developed to reproduce individuals' contact pattern based on smart card transaction data of metro systems from three major cities in China. Disease dynamics are connected with contact network structures based on individual based mean field and origin-destination pair based mean field approaches. The results suggest that the vulnerability of contact networks solely depends on the risk exposure of the most dangerous individual, however, the overall degree distribution of the contact network determines the difficulties in controlling the disease from spreading. Moreover, the generation model is proposed to depict how individuals get into contact and their contact duration, based on their travel characteristics. The metro data are used to validate the correctness of the generation model, provide insights on monitoring the risk level of transportation systems, and evaluate possible control strategies to mitigate the impacts due to infectious diseases. </div><div></div><div>Finally, the third part of the dissertation focuses on the role played by information in urban travel, and develops a multiplex network model to investigate the co-evolution of disease dynamics and information dissemination. The model considers that individuals may obtain information on the state of diseases by observing the disease symptoms from the people they met during travel and from centralized information sources such as news agencies and social medias. As a consequence, the multiplex networks model is developed with one layer capturing information percolation and the other layer modeling the disease dynamics, and the dynamics on one layer depends on the dynamics of the other layer. The multiplex network model is found to have three stable states and their corresponding threshold values are analytically derived. In the end, numerical experiments are conducted to investigate the effectiveness of local and global information in reducing the size of disease outbreaks and the synchronization between disease and information dynamics is discussed. </div><div></div>

<div>Efficient engine operation is a fundamental control problem in automotive applications. Robust control algorithms are necessary to achieve satisfactory, safe engine performance</div><div>at all operating conditions while reducing emissions. This thesis develops a framework for control architecture design to enable robust air handling system management.</div><div><br></div><div>The first work in the thesis derives a simple physics-based, control-oriented model for turbocharged lean burn engines which is able to capture the critical engine dynamics that are</div><div>needed to design the controller. The control-oriented model is amenable for control algorithm development and includes the impacts of modulation to any combination of four actuators: throttle valve, bypass valve, fuel rate, and wastegate valve. The controlled outputs: engine speed, differential pressure across throttle and air-to-fuel ratio are modeled as functions of selected states and inputs. Two validation strategies, open-loop and closed-loop are used to validate the accuracy of both nonlinear and linear versions of the control-oriented model. The relative gain array is applied to the linearized engine model to understand the degree of interactions between plant inputs and outputs as well as the best input-output pairing as a function of frequency. With strong evidence of high degree of coupling between inputs and outputs, a coordinated multiple-input multiple-output (MIMO) controller is hypothesized to perform better than a single-input single-output (SISO) controller. A framework to design robust model-based H1 MIMO controllers for any given linear plant, while considering state and output multiplicative uncertainties as well as actuator bandwidths is developed. The framework also computes the singular structure value, μ for the uncertain closed-loop system to quantify robustness, both in terms of stability and performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance while also preventing compressor surge and reducing engine emissions. A controller switching methodology using slow-fast controller decomposition and hysteresis at switching points is proposed to smoothly switch control authority between several MIMO controllers. The control design approach is applied to a truth-reference GT-Power engine model to evaluate the closed-loop controller performance. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller.</div><div><br></div><div><div>In the second study, a robust 2-degree of freedom controller that commands eBooster speed to control air-to-fuel ratio, and a robust MIMO coordinated controller to control gas</div><div>exchange process in a diesel engine with electrified air handling architecture are developed. The MIMO controller simultaneously controls engine speed, mass fraction of the recirculated exhaust gas as well as air-to-fuel ratio. The actuators available for control in the engine include the exhaust gas recirculation valve, exhaust throttle valve, fuel injection rate, eBooster speed, eBooster bypass valve. To design the robust eBooster controller, the input-output relationship between eBooster speed and air-to-fuel ratio is estimated using system identification techniques. The robust MIMO controller is synthesized using a physics-based mean value control-oriented engine model that accurately represents the high-fidelity GT-Power model. In the first control strategy, the robust eBooster controller is added to an already existing stock engine control unit while in the second control strategy, the stock engine control unit is replaced with the multiple-input multiple-output controller. The two control architectures are tested under different operating conditions to evaluate the controller performance. Simulation results with the control architectures developed in the thesis are compared to a baseline engine configuration, where the engine operates without eBooster. Although it is observed that both these control algorithms significantly improve engine performance as compared to the baseline configuration, MIMO controller provides the best engine performance overall.</div></div>

Across a range of sectors, including transportation and healthcare, the use of automation to assist humans with increasingly complex tasks is also demanding that such systems are more interactive with human users. Given the role of cognitive factors in human decision-making during their interactions with automation, models enabling human cognitive state estimation and prediction could be used by autonomous systems to appropriately adapt their behavior. However, accomplishing this requires mathematical models of human cognitive state evolution that are suitable for algorithm design. In this thesis, a computational model of coupled human trust and self-confidence dynamics is proposed. The dynamics are modeled as a partially observable Markov decision process that leverages behavioral and self-report data as observations for estimation of the cognitive states. The use of an asymmetrical structure in the emission probability functions enables labeling and interpretation of the coupled cognitive states. The model is trained and validated using data collected from 340 participants. Analysis of the transition probabilities shows that the model captures nuanced effects, in terms of participants' decisions to rely on an autonomous system, that result as a function of the combination of their trust in the automation and self-confidence. Implications for the design of human-aware autonomous systems are discussed, particularly in the context of human trust and self-confidence calibration.

<div>The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and optimal methods of path planning integrated with a market-based task allocation strategy. Further work presents a solution to unmanned ground vehicle path planning within the developed mission planning system framework under incomplete information. Deep reinforcement learning is proposed to solve movement through unknown terrain environment. The final demonstration for Advantage-Actor Critic deep reinforcement learning elicits successful implementation of the proposed model.</div>

Soft materials have gained increasing prominence in science and technology over the last few decades. This shift from traditional rigid materials to soft, compliant materials have led to the emergence of a new class of devices which can interact with humans safely, as well as reduce the disparity in mechanical compliance at the interface of soft human tissue and rigid devices.<br><br>One of the largest application of soft materials has been in the field of flexible electronics, especially in wearable sensors. While wearable sensors for physical attributes such as strain, temperature, etc. have been popular, they lack applications and significance from a healthcare perspective. Point-of-care (POC) devices, on the other hand, provide exceptional healthcare value, bringing useful diagnostic tests to the bedside of the patient. POC devices, however, have been developed for only a limited number of health attributes. In this dissertation I propose and demonstrate wireless, wearable POC devices to measure and communicate the level of various analytes in and the properties of multiple biofluids: blood, urine, wound exudate, and sweat.<br><br>Along with sensors, another prominent area of soft materials application has been in actuators and robots which mimic biological systems not only in their action but also in their soft structure and actuation mechanisms. In this dissertation I develop design strategies to improve upon current soft robots by programming the storage of elastic strain energy. This strategy enables us to fabricate soft actuators capable of programmable and low energy consuming, yet high speed motion. Collectively, this dissertation demonstrates the use of soft compliant materials as the foundation for developing new sensors and actuators for human use and interaction.

<div>In this thesis, the design and implementation of an autonomous system that will equip a multi-rotor unmanned aerial vehicle (UAV) for visual detection and tracking of other UAVs is presented. The results from detection and tracking are used for real-time motion planning.</div><div><br></div><div>The goal is to effectively detect unwanted UAVs, track them and finally capture them with a net. Having a net that traps the UAVs and enables dragging intruders to another location is of great importance, since these could be carrying dangerous loads.</div><div><br></div><div>The project consists of three main tasks: object detection using a stereo camera, video tracking using a Kalman filter based algorithm, and lastly executing an optimal flight plan to aim a net at the detected intruder UAV. The computer vision, motion tracking and planning algorithms are implemented as ROS nodes what makes them executable on a reduced size onboard computer that is installed on the aerial vehicle.</div><div><br></div><div>Previous work related to this project consists of either a UAV detection system with computationally heavy algorithms or a tracking algorithm that does not include information about the dynamics of the UAVs. For the capture methods, previous ideas do not consider autonomous decisions or an optimized method to guarantee capture. In this thesis, these three aspects are considered to develop a simple solution that can be mounted on any commercially available UAV.</div>

<div>There are millions of individuals in the world who currently experience limited mobility as a result of aging, stroke, injuries to the brain or spinal cord, and certain neurological diseases. Robotic Assistive Devices (RADs) have shown superiority in helping people with limited mobility by providing physical movement assistance. However, RADs currently existing on the market for people with limited mobility are still far from intelligent.</div><div><br></div><div>Learning control strategies are developed in this study to make a Cable-Driven Assistive Device (CDAD) intelligent in assisting a human joint (e.g., a knee joint, an ankle joint, or a wrist joint). CDADs are a type of RADs designed based on Cable-Driven Parallel Robots (CDPRs). A PID–FNN control strategy and DDPG-based strategies are proposed to allow a CDAD to learn physical human-robot interactions when controlling the pose of the human joint. Both pose-tracking and trajectory-tracking tasks are designed to evaluate the PID–FNN control strategy and the DDPG-based strategies through simulations. Simulations are conducted in the Gazebo simulator using an example CDAD with three degrees of freedom and four cables. Simulation results show that the proposed PID–FNN control strategy and DDPG-based strategies work in controlling a CDAD with proper learning.</div>

<div>Self-organization in a multi-robot system is a spontaneous process where some form of overall order arises from local interactions between robots in an initially disordered system. Cooperative coordination strategies for self-organization promote teamwork to complete a task while increasing the total utility of the system. In this dissertation, we apply prosocial behavioral concepts such as altruism and cooperation in multi-robot systems and investigate their effects on overall system performance on given tasks. We stress the significance of this research in long-term applications involving minimal to no human supervision, where self-sustainability of the multi-robot group is of utmost importance for the success of the mission at hand and system re-usability in the future.</div><div><br></div><div>For part of the research, we take bio-inspiration of cooperation from the huddling behavior of Emperor Penguins in the Antarctic which allows them to share body heat and survive one of the harshest environments on Earth as a group. A cyclic energy sharing concept is proposed for a convoying structured multi-robot group inspired from penguin movement dynamics in a huddle with carefully placed induction coils to facilitate directional energy sharing with neighbors and a position shuffling algorithm, allowing long-term survival of the convoy as a group in the field. Simulation results validate that the cyclic process allows individuals an equal opportunity to be at the center of the group identified as the most energy conserving position, and as a result robot groups were able to travel over 4 times the distance during convoying with the proposed method without any robot failing as opposed to without the shuffling and energy sharing process. </div><div><br></div><div>An artificial potential based Adaptive Inter-agent Spacing (AIS) control law is also proposed for efficient energy distribution in an unstructured multi-robot group aimed at long-term survivability goals in the field. By design, as an altruistic behavior higher energy bearing robots are dispersed throughout the group based on their individual energy levels to counter skewed initial distributions for faster group energy equilibrium attainment. Inspired by multi-huddle merging and splitting behavior of Emperor Penguins, a clustering and sequential merging based systematic energy equilibrium attainment method is also proposed as a supplement to the AIS controller. The proposed system ensures that high energy bearing agents are not over crowded by low energy bearing agents. The AIS controller proposed for the unstructured energy sharing and distribution process yielded 55%, 42%, 23% and 33% performance improvements in equilibrium attainment convergence time for skewed, bi-modal, normal and random initial agent resource level distributions respectively on a 2D plane using the proposed energy distribution method over the control method of no adaptive spacing. Scalability analysis for both energy sharing concepts confirmed their application with consistently improved performances different sized groups of robots. Applicability of the AIS controller as a generalized resource distribution method under certain constraints is also discussed to establish its significance in various multi-robot applications.</div><div><br></div><div>A concept of group based survival from damaging directional external stimuli is also adapted from the Emperor Penguin huddling phenomenon where individuals on the damaging stimuli side continuously relocate to the leeward side of the group following the group boundary using Gaussian Processes Machine Learning based global health-loss rate minima estimations in a distributed manner. The method relies on cooperation from all robots where individuals take turns being sheltered by the group from the damaging external stimuli. The distributed global health loss rate minima estimation allowed the development of two settling conditions. The global health loss rate minima settling method yielded 12.6%, 5.3%, 16.7% and 14.2% improvement in average robot health over the control case of no relocation, while an optimized health loss rate minima settling method further improved on the global health loss rate settling method by 3.9%, 1.9%, 1.7% and 0.6% for robot group sizes 26, 35, 70 and 107 respectively.</div><div><br></div><div>As a direct application case study of collaboration in multi-robot systems, a distributed shape formation strategy is proposed where robots act as beacons to help neighbors settle in a prescribed formation by local signaling. The process is completely distributed in nature and does not require any external control due to the cooperation between robots. Beacon robots looking for a robot to settle as a neighbor and continue the shape formation process, generates a surface gradient throughout the formed shape that allow robots to determine the direction of the structure forming frontier along the dynamically changing structure surface and eventually reach the closest beacon. Simulation experiments validate complex shape formation in 2D and 3D using the proposed method. The importance of group collaboration is emphasized in this case study without which the shape formation process would not be possible, without a centralized control scheme directing individual agents to specific positions in the structure. </div><div> </div><div>As the final application case study, a collaborative multi-agent transportation strategy is proposed for unknown objects with irregular shape and uneven weight distribution. Although, the proposed system is robust to single robot object transportation, the proposed methodology of transport is focused on robots regulating their effort while pushing objects from an identified pushing location hoping other robots support the object moment on the other end of the center of mass to prevent unintended rotation and create an efficient path of the object to the goal. The design of the object transportation strategy takes cooperation cues from human behaviors when coordinating pushing of heavy objects from two ends. Collaboration is achieved when pushing agents can regulate their effort with one another to maintain an efficient path for the object towards the set goal. Numerous experiments of pushing simple shapes such as disks and rectangular boxes and complex arbitrary shapes with increasing number of robots validate the significance and effectiveness of the proposed method. Detailed robustness studies of changing weight of objects during transportation portrayed the importance of cooperation in multi-agent systems in countering unintended drift effects of the object and maintain a steady efficient path to the goal. </div><div><br></div><div>Each case study is presented independent of one another with the Penguin huddling based self-organizations in response to internal and external stimuli focused on fundamental self-organization methods, and the structure formation and object transportation strategies focused on cooperation in specific applications. All case studies are validated by relevant simulation and experiments to establish the effectiveness of altruistic and cooperative behaviors in multi-robot systems.</div>

Surgeon expertise can be difficult to capture through direct robot programming. Deep imitation learning (DIL) is a popular method for teaching robots to autonomously execute tasks through learning from demonstrations. DIL approaches have been previously applied to surgical automation. However, previous approaches do not consider the full range of robot dexterous motion required in general surgical task, by leaving out tooltip rotation changes or modeling one robotic arm only. Hence, they are not directly applicable for tasks that require rotation and dual-arm collaboration such as debridement. We propose to address this limitation by formulating a DIL approach for the execution of dual-arm surgical tasks including changes in tooltip orientation, position and gripper actions.<br><br>In this thesis, a framework for multi-step surgical task automation is designed and implemented by leveraging deep imitation learning. The framework optimizes Recurrent Neural Networks (RNNs) for the execution of the whole surgical tasks while considering tooltip translations, rotations as well as gripper actions. The network architecture proposed implicitly optimizes for the interaction between two robotic arms as opposed to modeling each arm independently. The networks were trained directly from the human demonstrations and do not require to create task specific hand-crafted models or to manually segment the demonstrations.<br><br>The proposed framework was implemented and evaluated in simulation for two relevant surgical tasks, the peg transfer task and the surgical debridement. The tasks were tested under random initial conditions to challenge the robustness of the networks to generalize to variable settings. The performance of the framework was assessed using task and subtask success as well as a set of quantitative metrics. Experimental evaluation showed favorable results for automating surgical tasks under variable conditions for the surgical debridement, which obtained a task success rate comparable to the human task success. For the peg transfer task, the framework displayed moderate overall task success. Quantitative metrics indicate that the robot generated trajectories possess similar or better motion economy that the human demonstrations.

<div><div><div><p>Globally, there are more than 727 million people aged 65 years and older in the world, and the elderly population is expected to grow more than double in the next three decades. Families search for affordable and quality care for their senior loved ones will have an effect on the care-giving profession. A personal robot assistant could help with daily tasks such as carrying things for them and keeping track of their routines, relieving the burdens of human caregivers. Performing mentioned tasks usually requires the robot to autonomously navi- gate. An autonomous navigation robot should collect the knowledge of its surroundings by mapping the environment, find its position in the map and calculate trajectories by avoiding obstacles. Furthermore, to assign specific tasks which are in various locations, robot has to categorize the rooms in addition to memorizing the respective coordinates. In this research, methods have been developed to achieve autonomous navigation and room categorization of a mobile robot within indoor environments. A Simultaneously Localization and Map- ping (SLAM) algorithm has been used to build the map and localize the robot. Gmapping, a method of SLAM, was applied by utilizing an odometry and a 2D Light Detection and Ranging (LiDAR) sensor. The trajectory to achieve the goal position by an optimal path is provided by path planning algorithms, which is divided into two parts namely, global and local planners. Global path planning has been produced by DIJKSTRA and local path planning by Dynamic Window Approach (DWA). While exploring new environments with Gmapping and trajectory planning algorithms, rooms in the generated map were classified by a powerful deep learning algorithm called Convolutional Neural Network (CNN). Once the environment is explored, the robots localization in the 2D space is done by Adaptive Monte Carlo Localization (AMCL). To utilize and test the methods above, Gazebo software by The Robotic Operating System (ROS) was used and simulations were performed prior to real life experiments. After the trouble-shooting and feedback acquired from simulations, the robot was able to perform above tasks and later tested in various indoor environments. The environment was mapped successfully by Gmapping and the robot was located within the map by AMCL. Compared to the theoretical maximum efficient path, the robot was able to plan the trajectory with acceptable deviation. In addition, the room names were classified with minimum of 85% accuracy by CNN algorithm. Autonomous navigation results show that the robot can assist elderly people in their home environment by successfully exploring, categorizing and navigating between the rooms.</p></div></div></div>