Automated grasping of objects of unknown geometry a priori has applications in many industries such as clearing a mine shaft after blasting, agricultural applications such as fruit and vegetable handling, and many roles in the service industry such as fetching items for a handicapped individual. In these roles the system environment is highly unstructured, and the robot must be able to react to different types of objects needing to be grasped. In this thesis a vision guided robotic grasp planner for unstructured environments is presented. An evaluation method for robotic grasping involving two distinct sets of objects is also presented. Both the grasp planner and evaluation metric are evaluated by experimentation using an articulated robotic arm with an eye-in-hand video camera, line laser, and pneumatic gripper. Multiple grasping experiments were performed with the objects in random poses on a modified tabletop deemed the playfield that did not allow objects to rest flat. The grasp planner focused on using a created model of the object from camera observations using silhouetting and line laser data. The object model and its computed convex hull were used to evaluate and select a single facet and point creating a grasping pair for the pneumatic gripper jaws. The grasp was attempted and then evaluated using a secondary camera and the developed evaluation method. iv Grasp success rates ranged from 80.30% (Rectangular Block on playfield 137 attempts) to 97.69% (Hexagonal Nut 173 attempts), with a mean grasp computation time for the hexagonal nut of 0.57s. / Master of Applied Science (MASc)

<p dir="ltr">This dissertation presents the findings and research conducted during my Ph.D. study, which focuses on developing grasp policies for modular end-effectors on mobile manipulation platforms operating in cluttered environments. The primary objective of this research is to enhance the performance and accuracy of robotic manipulation systems in complex, real-world scenarios. The work has potential implications for various domains, including the rapidly growing Industry 4.0 and the advancement of autonomous systems in space habitats.</p><p dir="ltr">The dissertation offers a comprehensive literature review, emphasizing the challenges faced by mobile manipulation platforms in cluttered environments and the state-of-the-art techniques for grasping and manipulation. It showcases the development and evaluation of a Modular End-Effector System (MEES) for mobile manipulation platforms, which includes the investigation of object 6D pose estimation techniques, the generation of a deep learning-based grasping dataset for MEES, the development of a suction cup gripper grasping policy (Sim-Suction), the development of a two-finger grasping policy (Sim-Grasp), and the integration of Modular End-Effector System grasping policy (Sim-MEES). The proposed methodology integrates hardware designs, control algorithms, data-driven methods, and large language models to facilitate adaptive grasping strategies that consider the unique constraints and requirements of cluttered environments.</p><p dir="ltr">Furthermore, the dissertation discusses future research directions, such as further investigating the Modular End-Effector System grasping policy. This Ph.D. study aims to contribute to the advancement of robotic manipulation technology, ultimately enabling more versatile and robust mobile manipulation platforms capable of effectively interacting with complex environments.</p>