Department Publications

Solar photovoltaic (PV) systems have become increasingly popular as a sustainable energy source due to the growing need for renewable energy on a global scale. However, the buildup of dust and dirt on solar panels frequently compromises the effectiveness of these systems. Existing cleaning methods often require manual labor, exhibit low efficiency, and are restricted to either a single cleaning method or operational mode. To address these issues, we designed and implemented a semi-autonomous solar panel cleaning robot capable of both dry and wet cleaning. The robot can autonomously navigate on solar panels with a tilt of up to 20 or be manually controlled using a mobile app, and it operates for approximately 75 minutes on a single charge. This study enhances key aspects of the robotic system, integrating advanced edge detection and transition mechanisms using both ultrasonic sensors and image processing techniques. We implemented a robust localization system based on bus-bar detection for precise navigation, upgraded the water supply mechanism and refined the orientation estimation algorithm.

This concept paper proposes an architecture that integrates agentic AI models with digital twin representations for each subsystem component in an RF transceiver. The digital twins, constructed using machine learning techniques such as deep learning and reinforcement learning, are paired with specialized, autonomous AI agent(s) capable of independent reasoning, planning, and adaptive action. Agentic AI models go beyond traditional rule-based/symbolic AI to autonomously select optimal strategies, synthesize data from diverse sources, and interact with both virtual and real-world environments. Ideas for simulations and experimental validations are proposed for future work.

The quadrotor is an underactuated, nonlinear system that presents significant challenges in both modeling and control design. This work develops a decoupled control framework based on the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor. A Linear Quadratic Gaussian (LQG) regulator is implemented for control, with two extended Kalman filters employed for state estimation in the respective dynamic subsystems. The full design process, from dynamic modeling to flight simulation presented in detail. Key elements include nonlinear simulation, model linearization, state-space representation, feedforward compensation, Linear Quadratic Regulator (LQR) gain tuning, actuator dynamics, sensor noise, LQG design, and extended Kalman filter. The limitations of applying linear control to a nonlinear system are also presented.