Date of Graduation

7-2026

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering (MSME)

Degree Level

Graduate

Department

Mechanical Engineering

Advisor/Mentor

Wejinya, Uche

Committee Member

Armand, Mehran

Second Committee Member

McCann, Roy

Keywords

Grain Probe; Grain Sampling; Grain Testing; Robotics; RRP Manipulator

Abstract

This thesis focuses on the modeling, design, and early testing of a spherical revolute revolute prismatic (RRP) robotic manipulator for probe based applications. The system is motivated by agricultural grain probing, where a probe is positioned over a truck bed and inserted into the grain to collect samples as trucks enter storage facilities, ports, hubs, or transfer points. A spherical RRP configuration is useful for this type of task because it provides a large accessible workspace while maintaining a relatively simple mechanical structure. A model based design approach is used to develop the system, beginning with forward and inverse kinematics to describe the motion of the end effector and determine the joint variables required for a desired probe position. The system dynamics are then developed to estimate the force and torque requirements at each joint, providing a foundation for actuator selection, mechanical design, and future control development. A scaled prototype was developed in three dimensional modeling software with an emphasis on off the shelf components, availability, and cost. A simulation environment was also used to validate movement and mechanics early on. During physical testing, the system showed the limitations of relying only on idealized models, as friction, mechanical losses, and other non-ideal effects increased the required force and torque needed for motion. These results show that model based design provides a strong starting point for robotic system development, but successful implementation requires iterative refinement, conservative actuator selection, and consideration of real world system behavior. This work provides the foundation for future work in closed loop control, vision based feedback, simulation refinement, and automation of probe based robotic systems.

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