Optimal Deployment and Coverage of Robotic Sensors in 3D Vector Spaces based on Fractal Search

The robotic sensor deployment task to achieve maximum converge is one of the main phases in feasibility studies and development of communication infrastructures and environment monitoring systems. In this article, a new approach is proposed to treat the maximum coverage in 3D vector spaces. For this purpose, a new geometric strategy is first presented to compute the area covered by an individual sensor. To maximize the coverage of the robotic network, the fractal search algorithm was employed. This population-based evolutionary algorithm has been proposed based on the growth of the random fractal and demonstrates a robust performance in tackling constrained and unconstrained optimization problems. Then, based on several scenarios and by considering spatial constraints, the efficiency of the fractal search optimizer was compared with other methods in terms of robustness, running time, quality of the coverage results, convergence rate, as well as the statistical test of Wilcoxon. The comprehensive assessment and analysis of the results certify better performance of the proposed approach to maximize the coverage in 3D vector spaces. The proposed approach can obtain the optimal deployment and coverage of the robots by the best convergence rate and computational and statistical precision.