Efficient path planning of AUVs for container ship oil spill detection in coastal areas

Oil spills due to accidental leakage from Container Ships in a Marine environment poses a major challenge to aquatic living organisms and affects human health by increasing the environmental pollution. Autonomous Underwater Vehicles (AUV's) are deployed for detection and cleaning of oil spills. As the coastal areas are vast and lengthy, effective path planning of deployed AUVs is necessary. Many path planning algorithms for AUVs have been proposed in literature using approaches which necessitate search through large distance. The AUVs have to travel through a large distance to detect the spills in the search area resulting in more energy consumption. In this article we propose an efficient approach for determination of optimal AUV paths for detection of oil spills resulting in reduced search distance for a given area. The proposed approach implements a hybrid evolutionary optimization algorithm named Whale Cuckoo Search Optimization Algorithm (WCSOA) that results in increase in the search space, global search ability and the convergence speed. With the proposed approach the overall search delay and energy consumption of AUVs are reduced. It is observed from the simulation results obtained that the proposed approach provides reduction in AUV energy consumption by 57% compared to the existing Boustrophedon and WOA approach.