A Hierarchical Architecture Using Biased Min-Consensus for USV Path Planning
Peng Yao, Rui Zhao, Qian Zhu
Abstract
This paper proposes a hierarchical architecture using the biased min-consensus (BMC) method, to solve the path planning problem of unmanned surface vessel (USV). We take the fixed-point monitoring mission as an example, where a series of intermediate monitoring points should be visited once by USV. The whole framework incorporates the low-level layer planning the standard path between any two intermediate points, and the high-level fashion determining their visiting sequence. First, the optimal standard path in terms of voyage time and risk measure is planned by the BMC protocol, given that the corresponding graph is constructed with node state and edge weight. The USV will avoid obstacles or keep a certain distance safely, and arrive at the target point quickly. It is proven theoretically that the state of the graph will converge to be stable after finite iterations, i.e., the optimal solution can be found by BMC with low calculation complexity. Second, by incorporating the constraint of intermediate points, their visiting sequence is optimized by BMC again with the reconstruction of a new virtual graph based on the former planned results. The extensive simulation results in various scenarios also validate the feasibility and effectiveness of our method for autonomous navigation.