Efficient area coverage planning using approximation tiling heuristics for mosaic imaging with agile spacecraft

Mosaic outcomes for an observation from Galileo’s narrow-angle camera on Europa’s surface, specifically Annwn Regio. In this specific case, two heuristics have been used to generate the coverage path: Sidewinder and Online Frontier. On the left, Sidewinder shows poor outcome due to its fixed-size grid discretization, which leads to insufficient adaptability to the changes on observation geometry over time. On the right, Online Frontier exhibits an improved fit of the mosaic, achieved through a dynamic grid discretization. • We determine an instrument’s observation path to cover specific regions of interest • Scarce research has been devoted to address this problem for 2D framing instruments • Approximation algorithms find efficient planning paths for 2D framing instruments • Difficult observational geometries are resolved with the proposed approaches • Heuristics are ranked based on quality, computational burden, and robustness This work focuses on the Area Coverage Planning Problem (ACPP) for optical cameras onboard agile spacecraft in space exploration missions. The objective is to determine the optimal observation path of the camera’s boresight to obtain a mosaic that fully covers a designated Region Of Interest (ROI) on the target’s surface, while considering activity makespan and computational demand. To tackle this problem, four improved heuristics are implemented, each addressing differently the need to create an acquisition plan that dynamically adjusts to the camera’s observation geometry over time. Based on the proposal from a previous study, these heuristics have been further refined to correct spatial distortion and improve computational efficiency. In consequence, the current implementation allows for application to non-convex, irregularly shaped celestial bodies. We have developed a comprehensive program framework with supporting functions and assets to enable the iterative execution of the applied heuristics under diverse observation geometries along the spacecraft’s trajectory, ensuring their robustness and adaptability. The ACPP is a component of a broader scheduling problem for agile spacecraft, which aims to maximize the scientific return while adhering to geometric and operational constraints imposed by both the spacecraft and its payload. In this context, deterministic step-stare algorithms are preferred for their efficiency in balancing accuracy and computational resources. The algorithms are showcased through the simulation of observations from Galileo during one of its flybies over Europa. Arbitrary and diverse ROIs are considered on the target’s surface, allowing for a comprehensive evaluation of the algorithms in different observation geometries. The outcomes are analyzed over coverage completeness, efficient planning and computational burden. Thus, the resulting mosaics provide insights into the optimal usage of the heuristics in specific circumstances.