Evolutionary approaches to dynamic earth observation satellites mission planning under uncertainty

Mission planning for Earth Observation Satellite operators typically implies dynamically altering how requests from different customers are prioritised in order to meet expected deadlines. A request corresponds to a given area of interest to capture on Earth. This exercise is challenging for different reasons. First, satellites are limited by maneuvers and power consumption constraints resulting in a limited surface that can be covered at each orbit. Consequently, many requests are in competition with each other and so all of them cannot be treated at each orbit. When a request priority is boosted, it may incidentally penalise surrounding requests. Second, there are several uncertain factors such as weather (in particular cloud cover) and future incoming requests that can impact the completion progress of the requests. With order books of increasing size and the planned operations of a growing number of satellites in a close future, there is a clear need for a decision support method. In this paper, we investigate the potential of Evolutionary Algorithms (EAs) for this problem and propose several approaches to optimise request priorities based on Local Search and Population-Based Incremental Learning (PBIL). Using a certified algorithm to decode request priorities into satellite actions and a satellite simulator developed by Airbus, we are able to realistically evaluate the potential of these methods and benchmark them against operator baselines. Experiments on several scenarios and order books show that EAs can outperform baselines and significantly improve operations both in terms of delay reduction and successful image capture. While black-box approaches yield significant improvements over the baseline when there are many delayed requests in the order books, introducing domain knowledge is required to handle cases with fewer delayed requests.