Path planning for autonomous underwater vehicles based on interval optimization in uncertain flow fields

The predictive uncertainty of ocean currents needs to be considered in the path planning of autonomous underwater vehicles (AUV) in oceanic environments. An interval optimization (IO) scheme is presented in this paper to plan time-optimal paths for AUVs operating in oceanic environments with dynamic and uncertain flow fields; interval currents are established to process the uncertainty of forecasted ocean flows, and the IO scheme is used to search for time-optimal paths in the interval current fields. The IO scheme is a two-layer framework: In the outer layer, quantum-behaved particle swarm optimization is used to generate and optimize candidate paths. In the inner layer, the interval response of the candidate path from the outer layer is calculated under interval current fields, and they are converted into a time-optimal fitness value in accordance with the possibility degree of interval numbers. The influence of predictive uncertainty is analyzed via simulation, and the robustness of the IO scheme is verified.

Automated summary

This paper presents an interval optimization scheme for planning time-optimal paths for autonomous underwater vehicles in oceanic environments, considering the uncertainty of ocean flows. The scheme involves an outer layer using quantum-behaved particle swarm optimization to generate paths, and an inner layer calculating the path response under interval current fields. The impact of predictive uncertainty is analyzed through simulation, and the robustness of the scheme is confirmed.