# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 1 Introduction  
Since the first steps, space science has shown interest in Earth remote sensing systems, particularly those deployed on satellites, which provide a repetitive and coherent view of the earth that is invaluable for monitoring short-term and long-term changes and the impact of human activities [1, 2]. In recent years, thanks to technological advancements, Earth observation has become fundamental for everyday life, for instance, it is possible to identify wildfires, eruptions, and more generally natural hazards and disasters [3, 4], also thanks to technological improvements such as distributed systems [5], which are not yet fully exploited in satellite remote sensing. For the branch of science dealing with disaster monitoring, it is of the utmost importance that the location identification of the disaster occurs in a short time, or better that it occurs in real time. However, with the technology on-board current remote sensing missions, such real-time detection of extreme events is not possible. Indeed, the image acquisitions are programmed offline by the ground control and it is not possible to change them on-board. This is due to the fact that guidance must be evaluated in order to minimize some parameters, such as maneuver time or consumption,and it can be performed by means of numerical optimization algorithms which usually require high computational effort. Efforts in designing innovative techniques for planning attitude maneuvers in computationally efficient ways are reported in the recent literature. For instance, [PERSON] [6] propose a genetic algorithm to optimize an attitude guidance planner (\(AGP\)) by imposing boundary constraints. Furthermore, [PERSON] et al. [7] used a metaheuristic method to obtain a time-optimal attitude maneuver by imposing boundary and pointing constraints. Again, in another work, [PERSON] and [PERSON] [8] studied an energy-optimal \(AGP\) under complex constraints by exploiting the solution of a quadratic programming problem. [PERSON] proposed a solution for time-optimal, constrained satellite reorientation maneuvers by using particle swarm optimization (PSO) [9]. In [10] and [11], [PERSON] et al. proposed a method to solve the problem of time-optimal satellite reorientation maneuvers with boundary and path constraints by using the PSO and the inverse dynamics approach. The problem of autonomous on-board AGPs has been already addressed in the recent remote sensing literature, where the ability of taking autonomous decisions has been generally recognized as playing a fundamental role in future missions. For example, the detection of natural disasters (such as forest fires, floods, eruptions, etc.) must occur in real time to avoid large-scale losses and seriously endanger human life. This means that the satellite must be able not only to select by itself the areas of interest to be acquired but also to generate the guidance needed to perform the task. An overview of the problem is well given in [12], where a discussion of current issues and possible solutions is presented. Many researchers are focusing on this problem: in [13], [PERSON] et al. deal with the problem of planning and scheduling operations for a remote sensing satellite constellation; the same problem is addressed in [14], where [PERSON] et al. developed a ground software to automate the optimization of satellite constellation operations and validated the software in an operational mission; [PERSON] et al. designed an autonomous ground planner to acquire ground targets according to emergency levels [15]. An even more cutting-edge solution is proposed in [16], where [PERSON] and [PERSON] designed a satellite task planner by means of a heuristic search algorithm based on a symmetric recurrent neural network. Many other works can be listed as interesting solutions to the problem, such as [17; 18; 19; 20; 21; 22; 23; 24; 25]. All these works report elegant solutions to perform an \(AGP\), but they need a ground segment to implemented them since they require high computational capabilities.