Recent attempts to reprogram a Roomba to be used as a simple agent have led to interesting behavior. Observation has shown that the behavior of the Roomba is not only dependent on the precepts of the Roomba, but also relies heavily on the uncontrollable environmental conditions that the Roomba is placed in. Ultimately this makes the Roomba a great platform to test and teach aspects of artificial intelligence. This paper will show how most of the tested environmental conditions are mitigated by a learning agent that will adjust behavior dependent on the precepts that are received.

The aim of this thesis was to learn and summarize basic information about IoT systems, which protocols are used and introduction of Google Home system. The first part of the thesis describes the individual parts of the system --- microcontrollers, sensors, light elements and possible systems for backend. In the implementation part of the thesis was designed a solution for smart home controling using Google Home technology. Based on data from the weather station, the system controls and adjusts the intensity of outdoor lighting and controls the blinds. Furthermore, a control module for gate and garage door control is implemented. The system also takes care of vacuum cleaning in the house using iRobot Roomba vacuum cleaners with custom Wi-Fi module. The final part of the thesis summarizes the achieved results.