Space Debris Clustering and Avoidance Using Chrono-Spatial Computational Frameworks

This paper investigates the utilization of Spatio-Temporal data structures, their applications and how they can be implemented. This study also explores the temporal data management of spatial data that is dynamic in nature. Moreover, this integration of appropriate temporal aspects to the spatial data structures like Quad Tree is explored, thus the concept of Spatio-temporal data structures is developed. One of the primary takeaways from this paper is that it incorporates convex hull algorithm into the cluster management systems. This integration is exemplified in the context of spacecraft path optimization during orbital missions and the clustering of space debris in the atmosphere of the earth. The interaction will allow users to model rocket trajectories, so it would be possible to identify the optimum outputs.