Path-planning algorithm for small environmental surveillance unmanned surface vehicles

Abstract

Ports are essential hub facilities that provide support for economic development. However, the construction, development, and operation of ports increase the risk of environmental pollution in marine areas. Small environmental surveillance unmanned surface vehicles (ESUSVs) are being deployed to monitor port environments and prevent pollution. This study proposes a bidirectional elastic force contraction algorithm (BEFCA) and a Lévy flight weighted whale optimization (LFWWOA) and BEFCA hybrid algorithm (LFWWOA-BEFCA) to solve the path planning problem of ESUSVs. BEFCA solves the slow convergence and unsmooth path-characteristic problem of the elastic force contraction algorithm (EFCA) by employing a bidirectional search strategy and ship kinematics to smoothen the turning points in the path, respectively. LFWWOA uses a Lévy flight-based strategy in the global exploration phase of the whale optimization algorithm (WOA) to increase the solution diversity and improves the global and local search performance by modifying the coefficient calculation method and adding adaptive weighting coefficients. Thirteen benchmark functions were selected for the LFWWOA optimization performance experiments and compared with other intelligence algorithms. The results demonstrate that the proposed algorithm achieved the best global performance. Therefore, LFWWOA was used to optimize the BEFCA parameters, which resulted in higher-quality planned paths. Simulation experiments of real scenarios and complex environments showed that the path lengths and algorithm runtimes of BEFCA and LFWWOA-BEFCA outperformed those of the state prediction rapidly exploring random trees (spRRT) and spRRT-informed algorithms, respectively. The planned paths are consistent with the motion characteristics of ESUSVs, which can be used directly for tracking. The findings of this study indicate that shorter travel paths can be planned for ESUSVs in harbors for environmental monitoring, effectively solving the difficulty of tracking the paths of ESUSVs, and reducing energy consumption during the travel process.