Enhancing Swarm Robotics with Sensor Fusion, Localization,Communication

Abstract

This thesis explores the development of an indoor localization system for swarm robotics. The project focused on implementing the dead reckoning algorithm using IMU and encoder data to estimate the robots' positions accurately. Despite initial attempts with Wi-Fi fingerprinting, the instability of Wi-Fi signals led to the adoption of sensor fusion techniques, particularly an Extended Kalman Filter (EKF), to enhance localization accuracy. Communication between swarm robots was facilitated using ESP8266 microcontrollers, establishing a reliable master-slave network. The results demonstrate the system's effectiveness, with discussions on future improvements, such as integrating alternative sensors like UWB and advanced communication protocols, to further enhance the system's robustness and accuracy.