Incidents of hydraulic or oil spills in the oceans/seas or ports occur with some regularity during the exploitation, production and transportation of petroleum products. Immediate, safe, effective and environmentally friendly measures must be adopted to reduce the impact of the oil spill on marine life. Due to the difficulty to detect and clean these areas, semi-autonomous vehicles can make a significant contribution by implementing a cooperative and coordinated response. The paper proposes a concept study of Hybrid Monitoring Detection and Cleaning System (HMDCS-UV) for a maritime region using semiautonomous unmanned vehicles. This system is based on a cooperative decision architecture for an unmanned aerial vehicle to monitor and detect dirty zones (i.e., hydraulic spills), and clean them up using a swarm of unmanned surface vehicles. The proposed solutions were implemented in a real cloud and were evaluated using different simulation scenarios. Experimental results show that the proposed HMDCS-UV can detect and reduce the level of hydraulic pollution in maritime regions with a significant gain in terms of energy consumption.
This article proposes an approach which deals with the problem of monitoring ocean pollution and cleaning dirty zones using autonomous unmanned vehicles. The authors present a cooperative agent-based planning approach for heterogeneous unmanned vehicles with different roles. Unmanned aerial vehicles (UAVs) monitor multiple ocean regions and unmanned surface vehicles (USVs) tackle the cleaning of dirty zones. Due to the rapid deployment of these unmanned vehicles, and the increase of ocean pollution, it is convenient to use a fleet of unmanned vehicles. Thus, most of the existing studies deal with the monitoring of different zones, the detection of the polluted zones and then the cleaning of the zones. In order to optimize this process, the authors' solution aims to use one UAV and one USV to reduce the pollution level of the ocean zones while taking into account the problem of fault tolerance related to these vehicles.
AbstractThis work addresses the issue of ocean monitoring and clean-up of polluted zones, as well as the notion of trajectory planning and fault tolerance for semi-autonomous unmanned vehicles. A hybrid approach for unmanned aerial vehicles (UAVs) is introduced to monitor the ocean region and cooperate with swarm of unmanned surface vehicles (USVs) to clean dirty zones. The paper proposes two solutions that apply to trajectory planning from the base of life to the dirty zone for swarm USVs. The first solution is performed by a modified Genetic Algorithm (GA), and the second uses a modified Ant Algorithm (AA). The proposed solutions were both implemented in the simulation with different scenarios for the dirty zone. This approach detects and reduces the pollution level in ocean zones while taking into account the problem of fault tolerance related to unmanned cleaning vehicles.
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