Abstract
In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.
Highlights
The demand for the exploration and development of ocean coerce human beings to employ surface and underwater vehicles to explore and study water environments [1]
Zhu et al [16] have simplified the calculations of the shunting equation and proposed an improved algorithm based on the biological inspired neural network (BINN) algorithm for the AUV complete coverage path planning, which can effectively reduce the path planning time and improve the efficiency
With the aim of addressing the aforementioned problems, a complete coverage neural network (CCNN) algorithm is proposed for the path planning of a unmanned surface vehicle (USV)
Summary
The demand for the exploration and development of ocean coerce human beings to employ surface and underwater vehicles to explore and study water environments [1]. Yang [13] has proposed the biological inspired neural network (BINN) algorithm and applied it to the complete coverage path planning of cleaning robots. Zhu et al [16] have simplified the calculations of the shunting equation and proposed an improved algorithm based on the BINN algorithm for the AUV complete coverage path planning, which can effectively reduce the path planning time and improve the efficiency. Zhao et al [17] have proposed a new optimal decision formula to solve the problem of the local path yaw, and applied it to the complete coverage path planning of USVs. The BINN algorithm is mainly used for mobile robots on land (such as cleaning robots). With the aim of addressing the aforementioned problems, a complete coverage neural network (CCNN) algorithm is proposed for the path planning of a USV.
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