Search

The collaboration of heterogeneous multiple robots has been shown to be capable of significantly enhancing the system redundancy, autonomy, robustness, and so on. However, realizing the collaboration with specific tasks in practice often requires the development of sophisticated mechanisms which are envisioned to exploit distinct benefits of the heterogeneous platforms. Thus, we propose a novel air–ground cooperative framework in this paper for the task of multi-target searching under an unknown urban environment. In particular, a group of unmanned aerial vehicles (UAVs) is employed to operate above the urban area to provide surveillance from a global perspective. Under the guidance of UAVs, multiple teams of unmanned ground vehicles (UGVs) are deployed to conduct the target searching missions. The UAVs’ and UGVs’ searching strategies are devised correspondingly leveraging on their own advantageous features. Finally, an ingenious integration of UAVs’ and UGVs’ searching operations is established by a notion of the upper confidence bound. Simulation results are provided to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This study is aimed at reducing the occurrence of oil spill accidents in the engine room of ships and carries out risk prevention for the equipment of the port ships, thereby reducing pollution to the marine ecological environment. Firstly, the concepts and principles of cluster analysis and ship automatic identification system are expounded. Secondly, the data information collected by the ship's automatic identification system (AIS) is combined with density-based cluster analysis. The accident area and extent at different stages of the ship's engine room equipment are classified. Finally, cluster analysis is used to evaluate the risk of equipment of the port ship engine room. The results show that there are 43000 ship operation information points in port a, the average operating speed of ships is 9 kilometers, and the fastest operation speed is 16.9 kilometers. In addition, many ship routes in port a need to take risk prevention measures to minimize the impact between ships and reduce the risk of oil leakage in the engine room. The proposed cabin model can easily and quickly analyze the orientation information of the ship and classify all the data into different types according to the surrounding information points. AIS can realize the information transfer between ships and between ship and shore. Information such as the position, speed, and direction of the ship needs to be accurately known to ensure safety at sea. These data need to relate to some terminals and networks to form a maritime monitoring network. The ship AIS based on cluster analysis can cluster the areas where the ship's speed and direction change significantly in the port area, effectively preventing accidents. Scientific risk prevention measures can effectively reduce the oil leakage risk of ship engine room equipment, improve the working efficiency of marine engines, and provide a strong foundation for the entire marine ecological environment protection. [ABSTRACT FROM AUTHOR]