An Efficient Autonomous Exploration Framework for Unmanned Surface Vehicles in Unknown Waters

Abstract

The detection of unknown waters has been studied and applied in various fields, such as national defense, military operations, engineering surveying and mapping, and scene reconstruction. To improve exploration efficiency in unknown waters, this paper proposes a framework for autonomous exploration using unmanned surface vehicles (USVs). This framework, comprising a multi-stage exploration strategy and a hierarchical navigation strategy, is designed to mitigate the inherent restrictions between the exploration target point and exploration direction in USV operations. These two strategies are optimized for the exploration target point and feasible navigation route to address the problem of the USV’s limited mobility during exploration. Rapidly exploring random tree (RRT) and boundary detection methods are used in the local layer to find the boundary in front of and behind the USV, and the gain of the target point is optimized. The hierarchical navigation method is implemented in the global layer to plan appropriate navigation paths. The proposed method is tested in simulations in several virtual environments and contrasted with the conventional methods currently in use. The findings indicate that our strategy covers more ground more effectively than other methods (our method achieved an exploration efficiency ranging from 4.9 to 5.3 m2/s, whereas traditional methods ranged from 2.3 to 3.9 m2/s, which demonstrates that our approach can improve exploration efficiency by up to 200% compared to traditional methods), spending less time exploring while significantly reducing collision probability.