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

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

In pursuit of autonomous distributed satellite systems

Satellite imagery has become an essential resource for environmental, humanitarian, and industrial endeavours. As a means to satisfy the requirements of new applications and user needs, novel Earth Observation (EO) systems are exploring the suitability of Distributed Satellite Systems (DSS) in which multiple observation assets concurrently sense the Earth. Given the temporal and spatial resolution requirements of EO products, DSS are often envisioned as large-scale systems with multiple sensing capabilities operating in a networked manner. Enabled by the consolidation of small satellite platforms and fostered by the emerging capabilities of distributed systems, these new architectures pose multiple design and operational challenges. Two of them are the main pillars of this research, namely, the conception of decision-support tools to assist the architecting process of a DSS, and the design of autonomous operational frameworks based on decentralised, on-board decision-making. The first part of this dissertation addresses the architecting of heterogeneous, networked DSS architectures that hybridise small satellite platforms with traditional EO assets. We present a generic design-oriented optimisation framework based on tradespace exploration methodologies. The goals of this framework are twofold: to select the most optimal constellation design; and to facilitate the identification of design trends, unfeasible regions, and tensions among architectural attributes. Oftentimes in EO DSS, system requirements and stakeholder preferences are not only articulated through functional attributes (i.e. resolution, revisit time, etc.) or monetary constraints, but also through qualitative traits such as flexibility, evolvability, robustness, or resiliency, amongst others. In line with that, the architecting framework defines a single figure of merit that aggregates quantitative attributes and qualitative ones-the so-called ilities of a system. With that, designers can steer the design of DSS both in terms of performance or cost, and in terms of their high-level characteristics. The application of this optimisation framework has been illustrated in two timely use-cases identified in the context of the EU-funded ONION project: a system that measures ocean and ice parameters in Polar regions to facilitate weather forecast and off-shore operations; and a system that provides agricultural variables crucial for global management of water stress, crop state, and draughts. The analysis of architectural features facilitated a comprehensive understanding of the functional and operational characteristics of DSS. With that, this thesis continues to delve into the design of DSS by focusing on one particular functional trait: autonomy. The minimisation of human-operator intervention has been traditionally sought in other space systems and can be especially critical for large-scale, structurally dynamic, heterogeneous DSS. In DSS, autonomy is expected to cope with the likely inability to operate very large-scale systems in a centralised manner, to improve the science return, and to leverage many of their emerging capabilities (e.g. tolerance to failures, adaptability to changing structures and user needs, responsiveness). We propose an autonomous operational framework that provides decentralised decision-making capabilities to DSS by means of local reasoning and individual resource allocation, and satellite-to-satellite interactions. In contrast to previous works, the autonomous decision-making framework is evaluated in this dissertation for generic constellation designs the goal of which is to minimise global revisit times. As part of the characterisation of our solution, we stressed the implications that autonomous operations can have upon satellite platforms with stringent resource constraints (e.g. power, memory, communications capabilities) and evaluated the behaviour of the solution for a large-scale DSS composed of 117 CubeSat-like satellite units.La imatgeria per satèl·lit ha esdevingut un recurs essencial per assolir tasques ambientals, humanitàries o industrials. Per tal de satisfer els requeriments de les noves aplicacions i usuaris, els sistemes d’observació de la Terra (OT) estan explorant la idoneïtat dels Sistemes de Satèl·lit Distribuïts (SSD), on múltiples observatoris espacials mesuren el planeta simultàniament. Degut al les resolucions temporals i espacials requerides, els SSD sovint es conceben com sistemes de gran escala que operen en xarxa. Aquestes noves arquitectures promouen les capacitats emergents dels sistemes distribuïts i, tot i que són possibles gràcies a l’acceptació de les plataformes de satèl·lits petits, encara presenten molts reptes en quant al disseny i operacions. Dos d’ells són els pilars principals d’aquesta tesi, en concret, la concepció d’eines de suport a la presa de decisions pel disseny de SSD, i la definició d’operacions autònomes basades en gestió descentralitzada a bord dels satèl·lits. La primera part d’aquesta dissertació es centra en el disseny arquitectural de SSD heterogenis i en xarxa, imbricant tecnologies de petits satèl·lits amb actius tradicionals. Es presenta un entorn d’optimització orientat al disseny basat en metodologies d’exploració i comparació de solucions. Els objectius d’aquest entorn són: la selecció el disseny de constel·lació més òptim; i facilitar la identificació de tendències de disseny, regions d’incompatibilitat, i tensions entre atributs arquitecturals. Sovint en els SSD d’OT, els requeriments del sistema i l’expressió de prioritats no només s’articulen en quant als atributs funcionals o les restriccions monetàries, sinó també a través de les característiques qualitatives com la flexibilitat, l’evolucionabilitat, la robustesa, o la resiliència, entre d’altres. En línia amb això, l’entorn d’optimització defineix una única figura de mèrit que agrega rendiment, cost i atributs qualitatius. Així l’equip de disseny pot influir en les solucions del procés d’optimització tant en els aspectes quantitatius, com en les característiques dalt nivell. L’aplicació d’aquest entorn d’optimització s’il·lustra en dos casos d’ús actuals identificats en context del projecte europeu ONION: un sistema que mesura paràmetres de l’oceà i gel als pols per millorar la predicció meteorològica i les operacions marines; i un sistema que obté mesures agronòmiques vitals per la gestió global de l’aigua, l’estimació d’estat dels cultius, i la gestió de sequeres. L’anàlisi de propietats arquitecturals ha permès copsar de manera exhaustiva les característiques funcionals i operacionals d’aquests sistemes. Amb això, la tesi ha seguit aprofundint en el disseny de SSD centrant-se, particularment, en un tret funcional: l’autonomia. Minimitzar la intervenció de l’operador humà és comú en altres sistemes espacials i podria ser especialment crític pels SSD de gran escala, d’estructura dinàmica i heterogenis. En els SSD s’espera que l’autonomia solucioni la possible incapacitat d’operar sistemes de gran escala de forma centralitzada, que millori el retorn científic i que n’apuntali les seves propietats emergents (e.g. tolerància a errors, adaptabilitat a canvis estructural i de necessitats d’usuari, capacitat de resposta). Es proposa un sistema d’operacions autònomes que atorga la capacitat de gestionar els sistemes de forma descentralitzada, a través del raonament local, l’assignació individual de recursos, i les interaccions satèl·lit-a-satèl·lit. Al contrari que treballs anteriors, la presa de decisions autònoma s’avalua per constel·lacions que tenen com a objectius de missió la minimització del temps de revisita global

In pursuit of autonomous distributed satellite systems

A la pàgina 265 diu: "In an effort to facilitate the reproduction of results, both the source code of the simulation environment and the configuration files that were prepared for the design characterisation are available in an open repository: https://github.com/carlesaraguz/aeossSatellite imagery has become an essential resource for environmental, humanitarian, and industrial endeavours. As a means to satisfy the requirements of new applications and user needs, novel Earth Observation (EO) systems are exploring the suitability of Distributed Satellite Systems (DSS) in which multiple observation assets concurrently sense the Earth. Given the temporal and spatial resolution requirements of EO products, DSS are often envisioned as large-scale systems with multiple sensing capabilities operating in a networked manner. Enabled by the consolidation of small satellite platforms and fostered by the emerging capabilities of distributed systems, these new architectures pose multiple design and operational challenges. Two of them are the main pillars of this research, namely, the conception of decision-support tools to assist the architecting process of a DSS, and the design of autonomous operational frameworks based on decentralised, on-board decision-making. The first part of this dissertation addresses the architecting of heterogeneous, networked DSS architectures that hybridise small satellite platforms with traditional EO assets. We present a generic design-oriented optimisation framework based on tradespace exploration methodologies. The goals of this framework are twofold: to select the most optimal constellation design; and to facilitate the identification of design trends, unfeasible regions, and tensions among architectural attributes. Oftentimes in EO DSS, system requirements and stakeholder preferences are not only articulated through functional attributes (i.e. resolution, revisit time, etc.) or monetary constraints, but also through qualitative traits such as flexibility, evolvability, robustness, or resiliency, amongst others. In line with that, the architecting framework defines a single figure of merit that aggregates quantitative attributes and qualitative ones-the so-called ilities of a system. With that, designers can steer the design of DSS both in terms of performance or cost, and in terms of their high-level characteristics. The application of this optimisation framework has been illustrated in two timely use-cases identified in the context of the EU-funded ONION project: a system that measures ocean and ice parameters in Polar regions to facilitate weather forecast and off-shore operations; and a system that provides agricultural variables crucial for global management of water stress, crop state, and draughts. The analysis of architectural features facilitated a comprehensive understanding of the functional and operational characteristics of DSS. With that, this thesis continues to delve into the design of DSS by focusing on one particular functional trait: autonomy. The minimisation of human-operator intervention has been traditionally sought in other space systems and can be especially critical for large-scale, structurally dynamic, heterogeneous DSS. In DSS, autonomy is expected to cope with the likely inability to operate very large-scale systems in a centralised manner, to improve the science return, and to leverage many of their emerging capabilities (e.g. tolerance to failures, adaptability to changing structures and user needs, responsiveness). We propose an autonomous operational framework that provides decentralised decision-making capabilities to DSS by means of local reasoning and individual resource allocation, and satellite-to-satellite interactions. In contrast to previous works, the autonomous decision-making framework is evaluated in this dissertation for generic constellation designs the goal of which is to minimise global revisit times. As part of the characterisation of our solution, we stressed the implications that autonomous operations can have upon satellite platforms with stringent resource constraints (e.g. power, memory, communications capabilities) and evaluated the behaviour of the solution for a large-scale DSS composed of 117 CubeSat-like satellite units.La imatgeria per satèl·lit ha esdevingut un recurs essencial per assolir tasques ambientals, humanitàries o industrials. Per tal de satisfer els requeriments de les noves aplicacions i usuaris, els sistemes d’observació de la Terra (OT) estan explorant la idoneïtat dels Sistemes de Satèl·lit Distribuïts (SSD), on múltiples observatoris espacials mesuren el planeta simultàniament. Degut al les resolucions temporals i espacials requerides, els SSD sovint es conceben com sistemes de gran escala que operen en xarxa. Aquestes noves arquitectures promouen les capacitats emergents dels sistemes distribuïts i, tot i que són possibles gràcies a l’acceptació de les plataformes de satèl·lits petits, encara presenten molts reptes en quant al disseny i operacions. Dos d’ells són els pilars principals d’aquesta tesi, en concret, la concepció d’eines de suport a la presa de decisions pel disseny de SSD, i la definició d’operacions autònomes basades en gestió descentralitzada a bord dels satèl·lits. La primera part d’aquesta dissertació es centra en el disseny arquitectural de SSD heterogenis i en xarxa, imbricant tecnologies de petits satèl·lits amb actius tradicionals. Es presenta un entorn d’optimització orientat al disseny basat en metodologies d’exploració i comparació de solucions. Els objectius d’aquest entorn són: la selecció el disseny de constel·lació més òptim; i facilitar la identificació de tendències de disseny, regions d’incompatibilitat, i tensions entre atributs arquitecturals. Sovint en els SSD d’OT, els requeriments del sistema i l’expressió de prioritats no només s’articulen en quant als atributs funcionals o les restriccions monetàries, sinó també a través de les característiques qualitatives com la flexibilitat, l’evolucionabilitat, la robustesa, o la resiliència, entre d’altres. En línia amb això, l’entorn d’optimització defineix una única figura de mèrit que agrega rendiment, cost i atributs qualitatius. Així l’equip de disseny pot influir en les solucions del procés d’optimització tant en els aspectes quantitatius, com en les característiques dalt nivell. L’aplicació d’aquest entorn d’optimització s’il·lustra en dos casos d’ús actuals identificats en context del projecte europeu ONION: un sistema que mesura paràmetres de l’oceà i gel als pols per millorar la predicció meteorològica i les operacions marines; i un sistema que obté mesures agronòmiques vitals per la gestió global de l’aigua, l’estimació d’estat dels cultius, i la gestió de sequeres. L’anàlisi de propietats arquitecturals ha permès copsar de manera exhaustiva les característiques funcionals i operacionals d’aquests sistemes. Amb això, la tesi ha seguit aprofundint en el disseny de SSD centrant-se, particularment, en un tret funcional: l’autonomia. Minimitzar la intervenció de l’operador humà és comú en altres sistemes espacials i podria ser especialment crític pels SSD de gran escala, d’estructura dinàmica i heterogenis. En els SSD s’espera que l’autonomia solucioni la possible incapacitat d’operar sistemes de gran escala de forma centralitzada, que millori el retorn científic i que n’apuntali les seves propietats emergents (e.g. tolerància a errors, adaptabilitat a canvis estructural i de necessitats d’usuari, capacitat de resposta). Es proposa un sistema d’operacions autònomes que atorga la capacitat de gestionar els sistemes de forma descentralitzada, a través del raonament local, l’assignació individual de recursos, i les interaccions satèl·lit-a-satèl·lit. Al contrari que treballs anteriors, la presa de decisions autònoma s’avalua per constel·lacions que tenen com a objectius de missió la minimització del temps de revisita global.Postprint (published version

A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration

A genetic algorithm based task scheduling system for logistics service robots

The demand for autonomous logistics service robots requires an efficient task scheduling system in order to optimise cost and time for the robot to complete its tasks. This paper presents a Genetic algorithm (GA) based task scheduling system for a ground mobile robot that is able to find a global near-optimal travelling path to complete a logistics task of pick-and-deliver items at various locations. In this study, the chromosome representation and the fitness function of GA is carefully designed to cater for a single load logistics robotic task. Two variants of GA crossover are adopted to enhance the performance of the proposed algorithm. The performance of the scheduling is compared and analysed between the proposed GA algorithms and a conventional greedy algorithm in a virtual map and a real map environments that turns out the proposed GA algorithms outperform the greedy algorithm by 40% to 80% improvement

Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1

The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments