This thesis explores the design and development of a bio-inspired robotic module that enhances intuitive human interaction within a swarm robotics context. The work addresses a research gap in Human-Swarm Interaction by focusing on how individual swarm robots can express emotions and respond to humans in meaningful, non-verbal ways, drawing inspiration from both domesticated animals, like dogs, and arthropods. The project integrates sensory and expressive components such as eyes, antennae, and body movement into a modular "symbiote" that can be mounted on existing robots. Through iterative prototyping, user studies, and expert consultations, the research identifies key emotional states and corresponding expressive behaviors, culminating in a module that communicates through movement of its appendages. The module supports real-time interaction and demonstrates the potential for robots to form more natural and intuitive relationships with human users, especially in exhibition environments like TU Delft’s Cyber Zoo. The findings contribute to the fields of bio-inspired design, swarm robotics, and human-robot interaction by offering a novel approach to enhancing emotional legibility and engagement in robotic swarms.

This thesis shows the detailed design of the control and software of a DC microgrid of a tiny house community on the roof of a high-rise building in the city of Rotterdam in the Netherlands, consisting of twelve tiny houses powered by solar and wind energy. This thesis is part of a project with two other subgroups, focusing on the microgrid design and the powerline communication.

First, an introduction to the problem is given together with a description of the tiny house community. After that, the general program of requirements is presented, as well as the requirements of this subgroup. Next, an artificial neural network design is presented, which is used to forecast solar and wind generation and energy demand. The designed dense neural network resulted in predictions with mean errors of 10.11%, 12.56%, and 6.95% as a fraction of the maximum value for solar generation, wind generation, and energy demand, respectively. The predictions functioned as an input for the model predictive controller, which used them to place restrictions on appliances in the community when necessary, to reduce dependency on the main power grid of Rotterdam. Using a mathematical optimization algorithm, a simulation of one year showed that the controller could reduce the grid dependency up to 25%, compared to simulating without the controller. The conclusion summarises the achieved results, discusses whether the requirements are met, and considers possible future works.