Date of Award
6-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Department
Electrical and Computer Engineering
First Advisor
Andy Wolfe
Abstract
Electricians face significant health and safety hazards when inspecting drop ceilings, including exposure to dust, asbestos, and the risk of falls. To address these challenges, this project proposes a lightweight, autonomous robot capable of inspecting drop ceilings and assisting with wire tracing tasks—thereby distancing electricians from hazardous environments. The robot employs tread-based mobility to navigate fragile ceiling panels, integrated ultrasonic sensors and bumpers for obstacle avoidance, and an antenna system to detect and follow energized wire signals. Visual feedback is provided to the operator through a real-time video feed over a secure NoMachine interface, with manual and semi-autonomous operation modes supported. The system incorporates both hardware-based and digital signal filtering to isolate desired wire signals. Testing in a simulated drop ceiling environment demonstrated the robot’s ability to traverse obstacles, detect target signals, and operate effectively in low-light conditions. This solution has the potential to enhance workplace safety and efficiency for electricians performing drop ceiling inspections.
Recommended Citation
Lawai, Muhammad Ibrahim; Holzmann, Niels; and Mujtaba, Khondakar, "Vector Extension on a RISC-V CPU" (2025). Electrical and Computer Engineering Senior Theses. 114.
https://scholarcommons.scu.edu/elec_senior/114
