Abstract: Problems in ocean observation tasks rely on manual planning and simple strategic planning. These problems include planning difficulties, high computational costs, and low resource utilization. To solve such problems, this paper proposes three robust indicators: the task completion rate, task resource consumption, and multi-modality collaborative observation rate. The paper also fully considers various constraints in terms of the marine environment, energy, communications, and other aspects. The paper establishes a robust planning model for ocean observation tasks and presents the design for a robust planning algorithm for these tasks based on the classic multi-objective optimization algorithm — the ant colony algorithm. The results of simulation experiments show that, when the number of observation tasks and amount of available observation resources exceed the capability of manual calculation, the robust planning scheme derived using the proposed method completed more tasks with lower costs, a higher resource use rate, and better algorithm performance than planning schemes generated by simple strategies.

Key words: ocean observation, task planning, robustness, ant colony algorithm