Browsing by Author "Qureshi, Maaz"

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    Intelligent Multi-Robot Autonomy with Connected AMRs and Manipulators for SMART Factory(s)
    (University of Waterloo, 2025-09-12) Qureshi, Maaz
    The transition toward SMART factories demands robotics systems that go beyond conventional automation to enable intelligent, autonomous, and scalable operations. This thesis presents a unified multi-robot autonomy framework that integrates distributed 3D mapping, 4D radar-based perception, 5G wireless communication, and high-DoF collaborative manipulation to address the challenges of modern industrial environments. The proposed system comprises two novel synergistic verticals: Connected Robotics Architecture for Distributed SLAM Mapping (CRADMap), a distributed volumetric mapping architecture for multi-robot systems using Autonomous Mobile Robots (AMRs), and Radar Antenna Pattern Acquisition through Automated Collaborative Robotics (RAPTAR), a radiation scanning and acquisition platform for radar antenna characterization using collaborative manipulators for enhancing HRI (Human Robot Interaction). CRADMap enables novel volumetric SLAM algorithm development, real-time 3D reconstruction by offloading dense RGB-D and radar data from AMRs to a centralized backend via 5G, where data is fused using COVINS for globally consistent map generation. The novel automation of 4D mmWave radar enhances perception in occluded or cluttered spaces, enabling inspection beyond line-of-sight. RAPTAR automates the traditionally manual process of radiation pattern testing using a 7-DoF torque-controlled cobot equipped with a custom end-effector, executing smooth, azimuth-polar constrained trajectories synchronized with RF data acquisition without the need for anechoic chambers. Together, these systems demonstrate a deployable ROS2 Humble, C++-based software stack, developed and validated through real-world experiments. Key novel contributions include: (i) distributed SLAM for multi-robots (AMRs), (ii) radar-augmented volumetric perception, (iii) Edge compute-enabled data pipelines using 5G, and (iv) automated high-resolution robotic manipulation for radiation measurement. This thesis establishes a practical blueprint for next-generation SMART factories, agents operate collaboratively to perceive, decide, and act autonomously and safely in dynamic, and data-driven industrial ecosystems.