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.

Robust feature tracking is a basic requisite for indoor robot navigation. However, indoor scenes mostly contain poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of non-distinctive features by several planar homographies which are computed using distinctive features. Use of a large number of non-distinctive features within the constraints imposed by planar homographies allows us to gain robustness against the absence of artificial landmarks. Also, the lesser computational cost in estimating these non-distinctive features helps to maintain the efficiency of the proposed method. Our outcome is a robust in door feature tracker. In this dissertation, we will establish that multi-homographic in door feature tracking is feasible with the combination of …

With the recent advancement of Large Language Models (LLMs), environmental perception methods, and frameworks for simplifying robot functionality implementation, robotic receptionists present an exciting opportunity to minimize humanrelated errors. However, most existing humanoid robots are costly and overqualified for such tasks, as they are primarily designed for research applications. To address this, we introduce a standalone, offline, cost-effective smart mobile robot receptionist built on an edge device that efficiently performs a predefined set of tasks while utilizing minimal hardware and computational resources. Our robot receptionist can engage in smooth conversations, recognize and interact with both new and returning users, perform hand gestures, provide navigation assistance through verbal and physical guidance, and autonomously navigate while avoiding static and dynamic obstacles …