Autonomous underwater vehicle path planning using fitness-based differential evolution algorithm

The enhanced capabilities of autonomous underwater vehicles (AUVs) will facilitate sustainable exploration and utilization of maritime resources through improved precision in underwater mapping, resource extraction, and environmental surveillance. Enhanced navigation and communication systems will bolster the robustness and flexibility of AUVs, opening up new avenues for research and operations in demanding underwater conditions. The objective of this initiative is to optimize the performance of AUVs by developing sophisticated navigation methodologies specifically designed for complex marine environments. To achieve this goal, this paper proposes a modified structure of the well-known metaheuristic called differential evolution (DE). The proposed algorithm is denoted by a fitness-based differential evolution algorithm (FDE). Through the utilization of path planning techniques and the application of the proposed FDE to enhance navigation, this paper seeks to overcome obstacles such as underwater barriers, restricted communication, and limited visibility. These enhancements are anticipated to notably elevate the efficacy and cognitive capabilities of AUVs. The validation of the proposed FDE algorithm is conducted on nine case studies of the path planning of AUV, and the comparison is made with other metaheuristic algorithms. The comparison indicates the effectiveness of the FDE in solving the AUV path planning problem.