Cooperative localization and control for heterogeneous mobile robots

This thesis contributes to the UTOPIA project, which focuses on developing cooperative localization and control strategies for heterogeneous mobile robots in agriculture. The research involves designing an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV), each with distinct sensors and maneuvering capabilities. These differences require a robust localization strategy for effective cooperation. The Extended Kalman Filter (EKF) was tested in various configurations to improve localization accuracy. Enhancements were first achieved through simulations and later validated in experiments. The EKF's performance was assessed in three cooperative missions, showing that integrating more sensor data into the EKF significantly reduced the Root Mean Square Error (RMSE), improving localization accuracy. While homogeneous robot systems benefit from consistent localization with similar sensors, heterogeneous systems face challenges due to varying sensor capabilities. This research underscores the importance of robust localization for effective cooperation in diverse robotic systems. UAVs, despite their fast movement and wide vision, are limited by load capacity and battery life, whereas UGVs offer greater load capacity and battery longevity but move slower. These findings highlight the need for effective cooperative localization and control strategies in complex environments.