This is a bachelor thesis about the design and development of an observation and sensing module for the Deci Zebro robot that is being developed at the EEMCS faculty of Delft University of Technology. This part of the thesis is about the development of a cliff- and obstacle detection system, as well as the designanddevelopmentofafittingplasticenclosureforthemoduleandthedesignofaPCBthatintegrates the module’s electronics. The report explores and compares different types of distance sensors and concludes that the best option is to use two infrared-based distance sensors on the left and the right of the robot for cliff detection, and a rotating ultrasonic transceiver on a servo motor to detect obstacles. For easy debugging and to allow communication with nearby humans, a LED ring was also fitted to the top of the module. The design process and the implementation of these components is described in detail. The module’s enclosure is designed using SolidWorks software and afterwards printed using a 3D-printer. The PCB is designed using the opensource KiCad software.

Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the spiking neural network (SNN). In addition to being used for memory, memristive memory can be read out passively and thus also replaces digital-to-analog circuitry. In this thesis, two solutions are proposed: one is based on a generalized memristor, the other is based on ferroelectric memory. Both solutions are implemented and simulated in SystemC AMS and tested with a SNN. As a final test, both memory solutions are integrated into a full-sized SNN and simulated against the MNIST dataset. The simulation results validate the capabilities of memristive and ferroelectric memory when it comes to providing a sensible weight storage solution for neuromorphic systems.