<p dir="ltr">The research presents the structure and a prototype an innovative parallel robotic structure using 3 mobile bases for actuation and hybrid motion. A system identification was performed to verify the model of the robot.</p>

<p dir="ltr">The research study of Design and Modeling of a Balloon Robot with Wheel Paddles for Agricultural Use (Huang, et al. 2023) presented the design, analysis, and simulation of an innovative agricultural robot that integrated a buoyancy system with a helium balloon and wheeled paddles for navigation, aiming to optimize crop health monitoring. The thesis research initiated with a comprehensive examination of the conceptual design, focusing on the robot's buoyancy mechanism and propulsion system. Detailed motion analysis and kinematic studies underpinned the development of a dynamic model, which was rigorously tested through MATLAB simulations. The MATLAB simulations assessed the unmanned vehicle's operational efficiency, maneuverability, and energy consumption in the environment setting of agricultural. The findings of the new design highlighted the robot's potential to surpass traditional agricultural robots in precision and adaptability, mitigating the limitations of ground and aerial alternatives. The thesis study of the balloon robot concluded with strategic recommendations for future enhancements, emphasizing scalability, payload capacity, and environmental adaptability, thus paving the way for advanced agricultural robotics.</p>

<p>Robot grasping is emerging as an active area of research in robotics as the interest in human-robot interaction is gaining worldwide because of diverse industrial settings for sharing tasks and workplaces. It mainly focuses on the quality of generated grasps for object manipulation. However, despite advancements, these methods need to consider the human-robot collaboration settings where robots and humans will have to grasp the same objects concurrently. Therefore, generating robot grasps compatible with human preferences of simultaneously holding an object is necessary to ensure a safe and natural collaboration experience. In this work, we propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-the-art robot grasping methods through simulated and real-robot experiments and user studies. In real robot experiments, our method achieves about 88% success rate in producing stable grasps that allow humans to interact and grasp objects simultaneously in a socially compliant manner. Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.</p> <p>To facilitate the grasping process, we also introduce a vision-language model that works as a pre-processing system before the grasping action takes place. In most settings, the robots are equipped with sensors that allow them to capture the scene, on which the vision model is used to do a detection task and objectify the visible contents in the environment. The language model is used to program the robot to make it possible for them to understand and execute the required sequence of tasks. Using the process of object detection, we build a set of object queries from the sensor image and allow the user to provide an input query for a task to be performed. We then perform a similarity score among these queries to localize the object that needs attention, and once identified, we can use a grasping process for the task at hand.</p>

<p dir="ltr">This thesis adapts and applies methodologies for optimizing the sensing topology of a counter-UAS (CUAS) network to the problem of optimizing the geospatial distribution of emergency response drone bases subject to resource limitations while ensuring alignment with emergency response requirements. The specific context for this work is a 911 call incident response.</p><p dir="ltr">Drone response time, time on scene, and sensor effectiveness are used as network performance metrics to develop a mission planning algorithm that attempts to maximize network response effectiveness. A composite objective function utilizes network response effectiveness and customer-defined region weights that indicate the probability of an incident occurring to represent the performance of the geospatial distribution of 911 drone bases. A Greedy Algorithm iterates upon this objective function to optimize the network topology.</p><p dir="ltr">Previous work [1] suggests that a heuristic based approach utilizing a hexagonal network topology centered around suburban/urban focal points is the preferred method for optimizing the dispatch topology of a 911 response drone network. The optimization strategy deployed here demonstrated an 11% improvement on the objective function compared to this heuristic when tested in Tippecanoe County, IN.</p><p dir="ltr">Previous work [2] also suggests that, of all drones in the design space compliant with FAA Part 107, a single Vertical Take-off and Landing (VTOL) type drone with an ability to transition into fixed wing horizontal flight adhering to specific performance requirements is the preferred drone for executing the emergency response mission. This thesis utilizes the optimization strategy deployed here to test this supposition by comparing the performance of a network with access to only this single drone type to a network with access to multiple types of fixed-wing VTOL drones. Findings indicate that access to only the single type of optimally-sized drone outperforms a network with access to multiple drone types; however, improvements to the greedy algorithm that consider the marginal value of each drone type and across diverse mission types may modify this conclusion.</p>

<p dir="ltr">This paper aims to gain a deep understanding of up-to-date research and development on modular self-reconfigurable robots (MSRs) through a thorough survey of market demands and published works on <i>design methodologies</i>, <i>system integration</i>, <i>advanced controls</i>, and <i>new applications</i>. Some limitations of existing mobile MSR are discussed from the reconfigurability perspective of mechanical structures, and a novel MSR system is proposed to address the identified limitations of existing MSRs. The comprehensive set of <i>Functional Requirements</i> (FRs) of MSRs is discussed, from which the mechanical designs of MSR were created, and the system was prototyped and built for testing. Three main innovations of the designed modules for MSR are to (1) share torque power, (2) customize the size for a given task, and (3) have a low number of actuated motors while still maintain a motion with high <i>Degrees of Freedom</i> (DoF) to overcome the constraints by the power capacities of individual motors; this helps to increase reconfigurability, reduce cost, and reduce the size of conventional MSRs.</p>

<p dir="ltr">Over 150,000 people undergo lower-extremity amputations yearly in the United States. In recent years, multiple efforts have been made to improve the human-robot interaction between amputees and active lower limb prostheses. Using lightweight wearable technologies has been a viable solution to implement algorithms that can estimate gait kinematics and prosthesis users’ intent. Examples of wearable technologies include inertial measurement units, strain gauges, and electromyography sensors. Kinematic and force data is inputted into an Error-State Kalman filter to estimate the inversion-eversion, external-internal, and dorsiflexion-plantarflexion ankle angle. The filter tracked the ankle angle with an accuracy of 0.7724°, 0.8826°, and 1.3520°, respectively. The gait phase was estimated using a linear regression model based on a shank kinematics ground truth pattern with an average normalized accuracy of 97.79 %. A numerical simulation of a gait emulator in the form of a 3-Revolute-Prismatic-Revolute (3-RPR) manipulator. The gait emulator can test lower limb prostheses independent of human subjects, eliminating many hurdles associated with human subject testing. The manipulator was simulated with two control strategies: a traditional PID and a hybrid PID + Active Force Control controller (AFC). The hybrid PID+AFC provided higher accuracy in tracking the desired end-effector trajectory due to improved disturbance rejection. A low-cost surface electromyography (sEMG) platform was developed to robustly acquire sEMG signals, with an overall component cost of 35.06 US$. The sEMG platform integrates directly into a Micro:bit microcontroller through an expansion board. During testing with human subjects, sEMG Micro:bit platform had a reported average signal-to-noise ratio of 24.7 dB.</p>