Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version

A Tabu Search algorithm for ground station scheduling problem

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Mission planning plays an important role in satellite control systems. Satellites are not autonomously operated in many cases but are controlled by tele-commands transmitted from ground stations. Therefore, mission scheduling is crucial to efficient satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). One common version of this problem is that of ground station scheduling, in which the aim is to compute an optimal planning of communications between satellites and operations teams of Ground Station (GS). Because the communication between SCs and GSs can be done during specific window times, this problem can also be seen as a window time scheduling problem. The required communication time is usually quite smaller than the window of visibility of SCs to GSs, however, clashes are produced, making the problem highly constrained. In this paper we present a Tabu Search (TS) algorithm for the problem, while considering several objective functions, namely, windows fitness, clashes fitness, time requirement fitness, and resource usage fitness. The proposed algorithm is evaluated by a set of problem instances of varying size and complexity generated with the STK simulation toolkit. The computational results showed the efficacy of TS for solving the problem on all considered objectives.Peer ReviewedPostprint (author's final draft

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Understanding Algorithm Performance on an Oversubscribed Scheduling Application

The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithms performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm

Satellite downlink scheduling problem: A case study

The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.Comment: 23 page

Aalto-1 CubeSat Mission Design Optimization

Small satellites are becoming increasingly popular solution for many space organizations and universities, both for commercial applications and scientific experimentation. The advances in electronics and sensor technology miniaturization have reduced the size of the satellites and enabled significant savings in construction and launch cost. However, the reliability and operability of very small satellites is still not in par with bigger satellites, tight budgets during the development and small amount of available energy in space set strong limits. The aim of this thesis is to address the mission planning and scheduling problems of small satellites, especially the multi-objective missions that require advanced planning and scheduling methods to resolve the operational complexities with optimized utilization of available resources. The research compares two different satellite mission planning and scheduling techniques. First technique is the genetic algorithm (GA), a population based optimization of scheduling tasks, in which the fitness function is calculated by the weight factors assigned to each task depending upon the priority of the task. The second technique is heuristic approach using the constraint satisfaction problem (CSP) in which the sequences of actions are constructed based on other constraints, from initial state to desired goal. The optimized solution for the small satellite mission planning and scheduling of various mission phases has been implemented focusing on Aalto-1 CubeSat mission design. A mission simulation software toolbox, utilizing the mentioned optimization techniques, has been developed in order to provide mission analysis tools for CubeSats. Consequently, Aalto-1 CubeSat power budgets, on-board data budgets and communication schemes for UHF and S-band have been analyzed to optimize the mission scheduling and planning for it’s in orbit operations. Furthermore, various design and operation phases have been explained in details to provide an overview of small satellites mission designs and to address the issues related to many CubeSat mission failures