Many-Objective Maritime Path Planning for Dynamic and Uncertain Environments

摘要

Weather-impacted asset routing is a complex problem, involving nonlinear, non-convex costs due to spatiotemporally varying uncertainty of weather parameters over a finite time horizon. To this end, we have augmented a previously published tool for multiobjective planning and asset routing, termed TMPLAR, with a new approximate dynamic programming-based Pareto optimization (NAPO) algorithm. TMPLAR is a mixed-initiative tool to solve the asset routing problem in dynamic and uncertain environments. It is built upon multi-objective dynamic programming algorithms to route assets in a timely fashion, while considering objectives, such as fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). The asset routing problem is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather and multiple constraints on asset operation. The NAPO algorithm optimizes weather-based objectives in a reasonable amount of time, optimizing arrival and departure times at waypoints, asset speed and bearing. The key algorithmic contribution is a fast approximate method for substantially containing the computational complexity by generating the Pareto-front of the multi-objective shortest path problem for networks with stochastic non-convex edge costs, utilizing approximate dynamic programming and clustering techniques. The proposed algorithm is validated and we compare its performance with the new approach to multi-objective A* (NAMOA*).