In the future, autonomous Unmanned Aerial Vehicles (UAVs) need to work in teams with Unmanned Ground Vehicles (UGVs) to share information and coordinate activities. The private sector and government agencies have implemented UAVs and UGVs for homeland security, reconnaissance, surveillance, data collection, urban planning, and geomatics engineering. Significant research is in progress to support the decision-making process for a Multi-Agent System (MAS) consisting of multiple UGVs and UAVs. The proposed five-year research program will contribute to these efforts by investigating fundamental issues in intelligent control of (MASs, including cooperation, coordination, sensor fusion, collision-free navigation and teleoperation of multiple UGVs and UAVs. MASs with multiple UGVs and UAVs are typical distributed systems. We will use artificial intelligence approaches to develop intelligent control algorithms for the cooperation and coordination of autonomous vehicles. We will also develop technologies for sensor fusion and collision-free navigation because they are critical issues for systems in a dynamic and unknown environment. When using UGVs and UAVs in hazardous or unknown environments, it is often desirable for a human operator to be able to teleoperate the system. Therefore, we will develop teleoperation mechanisms for a team of UAVs and UGVs. Simulation technologies have become important to the development of aerospace vehicles. In this research, we will integrate a high-fidelity UAV model with mobile robots and networked computers as a test bed for the control of multiple UAVs and UGVs. The proposed MAS control technologies will improve the reliability and efficiency of the control of distributed systems. The results will have wide applications in both commercial sectors and the defence industry. Furthermore, the research will provide training for highly qualified personnel in the areas of intelligent systems, control theories, robotics and aerospace, which are highly demanded by Canadian employers.