Date of Award

June 2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Engineering

First Advisor

Mohammad H Rahman

Committee Members

Mohammad H Rahman, Ilya V Avdeev, Inga Wang, Michael J Nosonovsky, Brahim Brahmi

Keywords

Assistive Robotics, Extended reach, Inverse kinematics, Performance indices, Robot optimization, ROS2

Abstract

The growing prevalence of Upper or Lower Extremities Dysfunctions (ULED), often linked to central nervous disorders such as stroke, Spinal Cord Injury (SCI), and Multiple Sclerosis (MS), underscores the urgent need for innovative support solutions. Over 5.35 million Americans currently live with ULED, a situation that places a significant socioeconomic burden on families and society. Despite invaluable support from caregivers and family members, the need for more scalable, practical solutions persists.

Wheelchair-mounted assistive robots emerge as a promising alternative in this context. These devices, offering continuous and reliable assistance, significantly alleviate caregiver fatigue and enhance the independence and quality of life for individuals with ULED. Capable of ceaseless operation and providing assistance with various Activities of Daily Living (ADLs), these robots foster an unprecedented level of autonomy for those affected by ULED.

This dissertation initiates with the presentation of core design constraints for assistive robots, derived from a thorough analysis of user needs and requirements. These constraints include the necessity of custom workspace designs for facilitating essential ADLs, the importance of foldability in the design of assistive robots, and an assessment of the robot's weight handling capacity, set to a benchmark of 3 kg. These elements collectively underscore the delicate balance required in assistive robot design, which calls for improved maneuverability, efficient energy consumption, accommodation of ADLs, optimal space usage, and appropriate weight handling capacity.

With these constraints as a foundation, the dissertation advances a methodology for the optimal design of assistive robots. Central to this methodology is the maximization of workspace coverage, a vital aspect for effectively enabling ADLs. In service of this aim, ADLs are classified into seven distinct workspaces, creating a nuanced understanding of the diverse physical environments these activities entail.

To further augment the design methodology, the dissertation introduces HUNTER, an advanced inverse kinematics algorithm. Capable of handling singularities and providing solutions beyond the predefined workspace, HUNTER deepens the interaction between the robot and the user. This advancement, in turn, amplifies the versatility and safety of assistive robots and holds significant potential for improving the user experience.

Following the establishment of the methodology and the introduction of HUNTER, the dissertation details the testing of the optimal robot through the implementation of a robot control library in ROS2. This library incorporates the inverse kinematics of HUNTER, controls the motors using EtherCAT (a communication protocol that enhances speed and secures real-time implementation), and applies a collision detection algorithm for the user's safety.

Finally, the dissertation covers the testing of the robot, validating its performance under the previously established design constraints. This comprehensive approach to design, testing, and validation underscores the potential for more effective and versatile assistive robotic solutions for individuals living with ULED.

Available for download on Sunday, February 25, 2024

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