An exploration of autonomous systems and their interaction with urban environments. Through three distinct robotic prototypes, this research investigates different approaches to machine intelligence, learning, and environmental adaptation.
Each robot represents a different philosophy: environmental sensing and response, minimal computational systems, and adaptive learning from human behavior patterns.
Quackbot is an autonomous swimming robot designed to enhance urban safety along the Lee River. Equipped with obstacle detection and light-sensing capabilities, it navigates waterways to identify dark areas and illuminate them for pedestrian and cyclist safety.
The robot combines environmental sensing with proactive intervention, representing an approach to urban robotics where machines actively improve public spaces through autonomous decision-making.
The Dumb Spider explores minimal computation through storage-based programming. Operating without sensors or traditional intelligence, this robot relies entirely on pre-programmed movement patterns and mechanical responses.
Despite developing custom software to assist with programming, the project revealed the limitations of purely deterministic robotic behavior and the challenges of creating meaningful interactions without environmental feedback.
Smart Susan demonstrates passive learning through observation of human behavior patterns. This intelligent device monitors user interactions and gradually adapts its responses to anticipate needs, creating an increasingly seamless integration with daily routines.
The concept extends beyond individual learning to collective intelligence—multiple devices sharing behavioral data to create sophisticated prediction networks. For example, retrieving a towel could trigger automatic activation of bathroom lighting and water heating systems.
This approach represents a shift from reactive to predictive home automation, where environmental intelligence emerges from accumulated behavioral understanding.
