The thesis research aims to further the study of human-robot interaction (HRI) issues, especially regarding the development of an assistive robot designed to help individuals possessing motor impairments. In particular, individuals with amyotrophic lateral sclerosis (ALS), represent a potential user population that possess an array of motor impairment due to the progressive nature of the disease. Through review of the literature, an initial target for robotic assistance was determined to be object retrieval and delivery tasks to aid with dropped or otherwise unreachable objects, which represent a common and significant difficulty for individuals with limited motor capabilities. This thesis research has been conducted as part of a larger, collaborative project between the Georgia Institute of Technology and Emory University. To this end, we developed and evaluated a semi-autonomous mobile healthcare service robot named EL-E. I conducted four human studies involving patients with ALS with the following objectives: 1) to investigate and better understand the practical, everyday needs and limitations of people with severe motor impairments; 2) to translate these needs into pragmatic tasks or goals to be achieved through an assistive robot and reflect these needs and limitations into the robot's design; 3) to develop practical, usable, and effective interaction mechanisms by which the impaired users can control the robot; and 4) and to evaluate the performance of the robot and improve its usability. I anticipate that the findings from this research will contribute to the ongoing research in the development and evaluation of effective and affordable assistive manipulation robots, which can help to mitigate the difficulties, frustration, and lost independence experienced by individuals with significant motor impairments and improve their quality of life.

This project investigated the implementation and evaluation of various collision-free path planning algorithms for the Saab Seaeye eM1-7 6-DOF Electric Manipulator (eManip). The primary goal was to enhance the autonomous performance of the eManip by integrating efficient path planning methodologies, ultimately ensuring the avoidance of collisions and manipulator singularities during underwater operations. Key algorithms examined included the Rapidly-exploring Random Trees (RRT) algorithm and its enhanced variants. Through simulation tests in MATLAB and Gazebo, metrics such as planning time, path length, and the number of explored nodes were evaluated. The results highlighted the robustness of Goal-biased and Bidirectional RRT* (Gb-Bd-RRT*), which consistently performed well across various environments. The research also highlighted the correlation between algorithm effectiveness and specific task attributes, emphasizing their adaptability to complex environments. This research contributes valuable insights into the effectiveness of path planning algorithms, informing the selection and integration of viable strategies for 6-DOF robotic manipulators.