Path Planning and Communication Strategies to Enable Connectivity in Robotic Systems

There has been considerable interest in the area of communication-aware robotics in recent years, where the sensing, communication and motion objectives of robotic systems are jointly optimized. One particular open problem in this area is that of exploiting the mobility of unmanned vehicles in order to improve or satisfy communication objectives in realistic communication environments. Progress in this field could not only affect robust networked operation of unmanned vehicles but also would improve communication systems of the future (e.g. 5G), thus contributing to both areas of robotics and communications. This mobility-enabled connectivity and communication is the main area of interest in this dissertation.This dissertation is focused on path planning and communication strategies for robotic systems seeking to satisfy certain communication objectives in realistic communication environments experiencing path loss, shadowing and multipath fading. We consider realistic communication environments by leveraging and incorporating a probabilistic channel prediction framework that allows the robots to predict the channel quality at unvisited locations. This thesis then contributes to the area of mobility and connectivity through three main topics 1) energy-optimal distributed beamforming, 2) finding the statistics of the distance traveled until connectivity, and 3) path planning for connectivity. First, in energy-optimal distributed beamforming, we utilize the motion of a group of initially unconnected mobile robots to enable new forms of connectivity. More specifically, we co-optimize their locations and transmission powers to cooperatively enable connectivity through distributed beamforming. We further bring a foundational theoretical understanding to robotic distributed beamforming. Next, in finding the statistics of the distance traveled until connectivity, we analytically characterize the probability density function of the distance traveled by an initially unconnected robot until it gets connected to a remote node as it moves along a given path. We utilize tools from the stochastic differential equation literature to develop this characterization. Finally, in path planning for connectivity, we actively plan the path of a mobile robot such that it finds a connected spot with a minimum expected traveled distance (i.e., energy). The scenario considered in this part is in fact a more general one, and tackles the problem of path planning on a graph to minimize the expected cost incurred until the successful completion of a task. This framework has applications beyond path planning for connectivity, in areas such as celestial body imaging, human-robot collaboration, and search scenarios. We bring a foundational understanding to this problem. We show how this problem is inherently hard to solve (NP-complete) and also propose a path planner, based on a game-theoretic framework, that provides an asymptotic optimality guarantee.Overall, this thesis proposes novel strategies for utilizing the mobility of unmanned vehicles and enabling connectivity while considering the underlying energy constraints. We also provide a rigorous theoretical analysis of the aforementioned problems using a wide range of tools from communications theory, game theory, optimal control and time series literature. Moreover, through extensive realistic numerical studies using real channel parameters/data, we show the efficiency and performance of our proposed approaches

Sun Protection Intervention for Urban Youth

Introduction: Skin cancer is less prevalent in persons with darker skin color compared to those with light skin, but contrarily is associated with greater mortality rates. Experts agree this is primarily due to late detection. Challenges in early detection include low public awareness, uncommon presentation, lower index of suspicion among health care providers, and decreased access to specialty care. To address some of these barriers, we designed a survey-based study to discover current beliefs, perceptions, and attitudes of urban youth towards sun protection before and after an educational intervention. Methods: In 2022, 17 children living in metro Detroit participated in an educational presentation regarding sun protection and completed pre- and post- surveys. Statistical significance was calculated via Chi-square tests. Results: After education, there was a significant increase in likelihood of sunscreen application before going outdoors (p \u3c 0.05). There was no significant difference after education regarding sunscreen beliefs (p = 0.463), sunscreen application frequency (p= 0.835), reapplication of sunscreen (p=1.074), daily sunscreen application (p=0.099), alternative sun protection (p=0.863), and likelihood of sharing about sun protection (p=0.227). Conclusions: This study was paired with a program that encouraged children to be active outside. The aim was to increase the likelihood of sunscreen application before going outdoors. In the pre-survey, more people said they would tell family and friends, but this was not reflected in the post survey. Future directions could include an interactive activity to share educational information and reinforce objectives

Targeting angiotensin II type 2 receptor pathways to treat neuropathic pain and inflammatory pain

Introduction: Neuropathic pain and chronic inflammatory pain are large unmet medical needs. Over the past two decades, numerous 'pain targets' have been identified for analgesic drug discovery. Despite promising results in rodent pain models, many compounds modulating such targets lacked efficacy in clinical trials. An exception is oral EMA401, a small-molecule angiotensin II type 2 receptor (AT(2)R) antagonist