The Generic Planning Tool (GPT) is a new software package being developed by the Flight Dynamics team at DLR. In an era where laser communications are becoming more and more relevant to data transmission for space missions, the GPT’s purpose is to compute highly accurate visibility windows and provide a wide variety of support information for both satellite-to-ground and inter-satellite links. What sets the GPT apart from previous products, is its shift from mission-specific to multi-mission and being able to accept various orbit and attitude data formats, thus enabling the support of multiple missions from DLR and external clients with flight dynamics information for mission planning applications. Its two main components are the core libraries written in Fortran, which serve as the powerhouse for the orbital mechanic’s computations, and the microservice architecture, enabled by JSON input/output files and Python scripts, which implement an automatic request-response service accessible over the network. This thesis will present why, how, and which GPT software functionalities were developed and tested during the internship at the German Space Operation Center. / Det generiska planeringsverktyget, Generic planning tool (GPT), är ett nytt mjukvarupaket som utvecklas av den flygdynamiska avdelningen på DLR. I en tid när kommunikation med hjälp av laser blir alltmer relevant vid dataöverföringar för rymduppdrag, är syftet med GPT att beräkna mycket exakta öppningar för dataöverföringar, men också att bidra med en mängd olika sorters användbar information för både ”satellit-till-mark”- och ”satellit-tillsatellit”-länkar. Det som skiljer verktyget från tidigare produkter är dess omvandling från att vara uppdragsspecifik till att kunna hantera multipla uppdrag. I och med GPT:s förmåga att acceptera olika dataformat gällande omloppsbana och orientering, öppnar det upp för att kunna stödja multipla uppdrag från DLR och externa klienter med flygdynamisk information, för applikationer inom uppdragsplanering. GPT:s två huvudsakliga delar är, de centrala biblioteken skrivna i Fortran vilka verkar som ett kraftverk för de orbital-mekaniska beräkningarna, och mikroservice-arkitekturen skapad från JSON input/output-filer och Pythonskript, vilket implementerar en automatisk begär- och svarstjänst tillgänglig via nätverket. Detta examensarbete kommer presentera varför, hur och vilka av GPT:s mjukvarufunktioner som utvecklades och testades under praktikplatsen på German Space Operations Center (GSOC).

Approved for public release; distribution is unlimited / Reissued 30 May 2017 with correction to student's affiliation on title page. / Autonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasures (MCM). A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algo-rithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful frame-work for solving these types of motion planning problems with dynamic constraints and di_x000B_erent performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertainty—generalized optimal control (GenOC)— thus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for di_x000B_erentMCMsearch missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patterns—especially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal mo-tion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.

This paper aims to present the thesis developed in the Doctoral Programin Computer Science and Computational Mathematics of the ICMC/USP. The thesis theme seeks to advance the state of the art by solving the problems of scalability and representation present in mission planning algorithms for Unmanned Aerial Vehicle (UAV). Techniques based on mathematical programming and evolutionary computation are proposed. Articles have been published, submitted or they are in final stages of preparation.These studies report the most significant advances in the representation and scalability of this problem. Mission planners worked on the thesis deal with stochastic problems in non-convex environments,where collision risks or failures in mission planning are treated and limited to a tolerated value. The advances in the representation allowed to solve violations in the risks present in the original literature modeling, besides making the models more realistic when incorporating aspects such as effects of the air resistance. Efficient mathematical modeling techniques allowed to advance from a Mixed Integer Nonlinear Programming (MINLP) model, originally proposed in the literature, to a Mixed Integer Linear Programming (MILP) problem. Modeling as a MILP led to problem solving more efficiently through the branch-and-algorithm. The proposed new representations resulted in improvements from scalability, solving more complex problems within a shorter computational time. In addition, advances in scalability are even more effective when techniques combining mathematical programming and metaheuristics have been applied to the problem. / O presente documento tem por objetivo apresentar a tese desenvolvida no Programade Doutorado em Ciência da Computação e Matemática Computacional do ICMC/USP. O tema da tese busca avançar o estado da arte ao resolver os problemas de escalabilidade e representação presentes em algoritmos de planejamento para missões com Veículos Aéreos Não Tripulados (VANTs). Técnicas baseadas em programação matemática e computação evolutiva são propostas. Artigos foram publicados, submetidos ou se encontram em fase final de elaboração. Esses trabalhos reportamos avanços mais significativos obtidos na representação e escalabilidade deste problema.Os planejadores de missão trabalhados na tese lidam com problemas estocásticos em ambientes não convexos, onde os riscos de colisão ou falhas no planejamento da missão são tratados e limitados a um valor tolerado. Os avanços na representação permitiram solucionar violações nos riscos presentes na modelagem original, além de tornar os modelos mais realistas ao incorporar aspectos como efeitos da resistência do ar. Para isso, técnicas eficientes de modelagem matemática permitiram avançar de um modelo de Programação Não-Linear Inteira Mista(PNLIM), originalmente proposto na literatura, para um problema de Programação Linear Inteira Mista (PLIM). A modelagem como um PLIM levou à resolução do problema de forma mais eficiente através do algoritmo branch-and-cut. As novas representações propostas resultaram em melhorias na escalabilidade, solucionando problemas mais complexos em um tempo computacional menor.Além disso,os avanços em escalabilidade mostraram-se mais efetivos quando técnicas combinando programação matemática e metaheurísticas foram aplicadas ao problema.

O presente documento tem por objetivo apresentar a tese desenvolvida no programa de doutorado em Ciência da Computação e Matemática Computacional do ICMC/USP. Esta tese aborda o desenvolvimento de sistemas autônomos, de baixo custo, para supervisão de missão e segurança de voo em Veículos Aéreos Não Tripulados (VANTs). A supervisão da missão é assegurada através da implementação de um sistema do tipo Mission Oriented Sensor Array (MOSA), responsável pelo adequado cumprimento da missão. A segurança de voo é garantida pelo sistema In-Flight Awareness (IFA), que visa monitorar o funcionamento da aeronave. Os assuntos missão e segurança são complexos e os sistemas MOSA e IFA foram idealizados e desenvolvidos de forma independente, fundamentando-se na ideia de separação de interesses. O desenvolvimento desses sistemas foi baseado em dois modelos de referência: MOSA e IFA, propostos pela literatura. Em trabalhos anteriores da literatura, alguns sistemas do tipo MOSA e IFA foram propostos para situações específicas de missão. Numa outra abordagem, esta tese propõe um único sistema MOSA e IFA capaz de se adequar a um conjunto distinto de missões. Neste trabalho, foi desenvolvida toda arquitetura de comunicação que integra os sistemas MOSA e IFA. No entanto, apenas esses dois sistemas não são suficientes para fazer a execução da missão com segurança, necessitando-se de um sistema capaz de se comunicar com o Piloto Automático (AP) do VANT. Logo, um sistema capaz de enviar requisições e comandos ao AP foi também implementado. Através desses três sistemas, missões autônomas com desvio de obstáculos puderam ser realizadas sem intervenção humana, mesmo diante de situações críticas ao voo. Assegurar os aspectos de segurança e missão pode se tornar conflitante durante o voo, pois em situações emergenciais deve-se abortar a missão. Diferentes estratégias para planejamento e replanejamento de rotas, baseadas em computação evolutiva e heurísticas, foram desenvolvidas e integradas nos sistemas MOSA e IFA. Os sistemas, aqui propostos, foram validados em quatro etapas: (i) experimentos com o simulador de voo FlightGear; (ii) simulações com a técnica Software-In-The-Loop (SITL); (iii) simulações com a técnica Hardware-In- The-Loop (HITL); (iv) voos reais. Na última etapa, os sistemas foram embarcados em dois modelos de VANTs, desenvolvidos pelo grupo de pesquisa. Durante a experimentação, alguns modelos de pilotos automáticos (APM e Pixhawk), computadores de bordo (Raspberry Pi 3, Intel Edison e BeagleBone Black), planejadores de missão e replanejadores de rotas emergenciais foram avaliados. Ao todo, três planejadores de rotas e oito replanejadores são suportados pela plataforma autônoma. O sistema autônomo desenvolvido permite alterar missões com diferentes características de hardware e de software de forma fácil e transparente, sendo, desse modo, uma arquitetura com características plug and play. / This document aims to present the thesis developed in the doctoral program in Computer Science and Computational Mathematics at ICMC/USP. This thesis addresses the development of low- cost autonomous systems for mission supervision and flight safety in Unmanned Aerial Vehicles (UAVs). The mission supervision is ensured through the implementation of a Mission Oriented Sensor Array (MOSA) system, which is responsible for the proper fulfillment of the mission. The flight safety is guaranteed by the In-Flight Awareness (IFA) system, which aims to monitor the aircraft operation. The mission and safety issues are complex, and the MOSA and IFA systems were idealized and developed independently, based on the idea of separation of concerns. The development of these systems was based on two reference models: MOSA and IFA, proposed in the literature. In previous works of the literature, some MOSA and IFA systems have been proposed for specific mission situations. In another approach, this thesis proposes a single MOSA and IFA system capable of adapting to a distinct set of missions. All the communication architecture that integrates the MOSA and IFA systems were developed in this work. However, only these two systems are not sufficient to carry out the mission safely; a system that can communicate with the AutoPilot (AP) of the UAV its also needed. In this way, a system that is capable of sending commands and requests to the AP was implemented in this work. Through these three systems, autonomous missions with a diversion of obstacles could be carried out without human intervention, even in critical situations to the flight. Ensuring the safety and mission aspects can become conflicting during the flight because in hazards situations the mission must be aborted. Different strategies for path planning and path replanning, based on evolutionary computation and heuristics, were developed and integrated into the MOSA and IFA systems. The systems proposed here were validated in four stages: (i) experiments with FlightGear flight simulator; (ii) simulations using Software-In-The-Loop (SITL); (iii) simulations using Hardware- In-The-Loop (HITL); (iv) real flights. In the last stage, the systems were embedded in two models of UAVs, developed by the research group. During the experiment were evaluated some models of autopilots (APM and Pixhawk), companion computers (Raspberry Pi 3, Intel Edison and BeagleBone Black), mission planners and emergency route planners. In all, three route planners and eight replanners are supported by the autonomous platform. The developed autonomous system allows changing missions with different hardware and software characteristics in an easy and transparent way, being, therefore, an architecture with Plug and play characteristics.

In the last decade, the development and deployment of Uninhabited Airborne Vehicles (UAVs) has increased dramatically. This has in turn increased the desire to operate UAVs in civilian-airspace. Current UAV platforms can be integrated into civilian-airspace, with other air traffic, however they place a high burden on their human operators in order to do so. In order to meet the competing objectives of improved integration and low operator workload it will be necessary to increase the intelligence on-board the UAV. This thesis presents the results of the research which has been conducted into increasing the on-board intelligence of the UAV. The intent in increasing the on-board intelligence is to improve the ability of a UAV to integrate into civilian-airspace whilst also reducing the workload placed upon the UAV's operator. The research has focused upon increasing the intelligence in two key areas: mission planning; and mission piloting. Mission planning is the process of determining how to fly from one location to another, whilst avoiding entities (eg. airspace boundaries and terrain) on the way. Currently this task is typically performed by a trained human operator. This thesis presents a novel multidisciplinary approach for enabling a UAV to perform, on-board, its own mission planning. The novel approach draws upon techniques from the 3D graphics and robotics fields in order to enable the UAV to perform its own mission planning. This enables the UAV's operator to provide the UAV with the locations (waypoints) to fly to. The UAV will then determine for itself how to reach the locations safely. This relieves the UAV's operator of the burden of performing the mission planning for the UAV. As part of this novel approach to on-board mission planning, the UAV constructs and maintains an on-board situational awareness of the airspace environment. Through techniques drawn from the 3D graphics field the UAV becomes capable of constructing and interacting with a 3D digital representation of the civilian-airspace environment. This situational awareness is a fundamental component of enabling the UAV to perform its own mission planning and piloting. The mission piloting research has focused upon the areas of collision avoidance and communications. These are tasks which are often handled by a human operator. The research identified how these processes can be performed on-board the UAV through increasing the on-board intelligence. A unique approach to collision avoidance was developed, which was inspired by robotics techniques. This unique approach enables the UAV to avoid collisions in a manner which adheres to the applicable Civil Aviation Regulations, as defined by the Civil Aviation Safety Authority (CASA) of Australia. Furthermore, the collision avoidance algorithms prioritise avoiding collisions which would result in a loss of life or injury. Finally, the communications research developed a natural language-based interface to the UAV. Through this interface, the UAV can be issued commands and can also be provided with updated situational awareness information. The research focused upon addressing issues related to using natural language for a civilian-airspace-integrated UAV. This area has not previously been addressed. The research led to the definition of a vocabulary targeted towards a civilian-airspace-integrated UAV. This vocabulary caters for the needs of both Air Traffic Controllers and general UAV operators. This requires that the vocabulary cater for a diverse range of skill levels. The research established that a natural language-based communications system could be applied to a civilian-airspace-integrated UAV for both command and information updates. The end result of this research has been the development of the Intelligent Mission Planner and Pilot (IMPP). The IMPP represents the practical embodiment of the novel algorithms developed throughout the research. The IMPP was used to evaluate the performance of the algorithms which were developed. This testing process involved the execution of over 3000 hours of simulated flights. The testing demonstrated the high performance of the algorithms developed in this research. The research has led to the successful development of novel on-board situational awareness, mission planning, collision avoidance and communications capabilities. This thesis presents the development, implementation and testing of these capabilities. The algorithms which provide these capabilities go beyond the existing body of knowledge and provide a novel contribution to the established research. These capabilities enable the UAV to perform its own mission planning, avoid collisions and receive natural language-based communications. This provides the UAV with a direct increase in the intelligence on-board the UAV, which is the core objective of this research. This increased on-board intelligence improves the integration of the UAV into civilian-airspace whilst also reducing the operator's workload.

In the scope of the master's project which is documented with the present thesis a mission planning tool (MPT) for SONATE was developed and implemented. After a thorough research on the current state of the art of MPTs and taking especially the early stage of the SONATE mission into account, it was decided to develop a generic timeline-based MPT. In contrast to existing MPTs a system is envisioned which is both powerful, regarding advanced features like resource control, and applicable for small satellite missions regarding the overall complexity and the associated configuration and training effort. Although it was obvious from an early stage that this vision cannot be reached in the scope of this project, it was kept during the project definition, object oriented analysis and early design stages in order to allow future extensions. Also the decision to develop the MPT on top of the Eclipse Rich Client Platform is mainly due to the argument of future extensibility. The MPT, which is released with this thesis, hence is a very basic generic timeline-based MPT omitting all possible advanced features like resource control or procedure validation, but featuring all essential parts of a MPT, i.e. modelling of procedures, scheduling of activities, and the generation of telecommand sequences. Furthermore, the user is supported by an intuitive graphical user interface. The thesis documents the development process, thus giving a broad understanding of the design and the implementation. For specific details of the implementation one may also refer to the separate technical documentation, while a user handbook included as appendix. The characteristics of the SONATE mission as a technology demonstrator for highly autonomous systems raise several important questions regarding the overall mission planning process. Therefore, besides the actual development of the MPT, those questions are discussed in a theoretical manner in the scope of this thesis, taking also account of the general emergence of highly autonomous satellites systems.Three concepts, Safe Planning, Sigma Resource Propagation, and Direct Telemetry Feedback, are proposed to face the challenges rising from the foreseen alternation of phases of classical mission operations and phases of autonomous operations of the satellite. Concluding the thesis, the final software product's features and capabilities are verified against the previously defined requirements and thus the overall success of the project is determined to be a 100% success fulfilling all primary project objectives. Finally, several fields for further research on the topic in general and work on the MPT itself are identified and outlined to pave the way for follow-up projects. / SONATE

This paper aims to present the thesis developed in the Doctoral Programin Computer Science and Computational Mathematics of the ICMC/USP. The thesis theme seeks to advance the state of the art by solving the problems of scalability and representation present in mission planning algorithms for Unmanned Aerial Vehicle (UAV). Techniques based on mathematical programming and evolutionary computation are proposed. Articles have been published, submitted or they are in final stages of preparation.These studies report the most significant advances in the representation and scalability of this problem. Mission planners worked on the thesis deal with stochastic problems in non-convex environments,where collision risks or failures in mission planning are treated and limited to a tolerated value. The advances in the representation allowed to solve violations in the risks present in the original literature modeling, besides making the models more realistic when incorporating aspects such as effects of the air resistance. Efficient mathematical modeling techniques allowed to advance from a Mixed Integer Nonlinear Programming (MINLP) model, originally proposed in the literature, to a Mixed Integer Linear Programming (MILP) problem. Modeling as a MILP led to problem solving more efficiently through the branch-and-algorithm. The proposed new representations resulted in improvements from scalability, solving more complex problems within a shorter computational time. In addition, advances in scalability are even more effective when techniques combining mathematical programming and metaheuristics have been applied to the problem. / O presente documento tem por objetivo apresentar a tese desenvolvida no Programade Doutorado em Ciência da Computação e Matemática Computacional do ICMC/USP. O tema da tese busca avançar o estado da arte ao resolver os problemas de escalabilidade e representação presentes em algoritmos de planejamento para missões com Veículos Aéreos Não Tripulados (VANTs). Técnicas baseadas em programação matemática e computação evolutiva são propostas. Artigos foram publicados, submetidos ou se encontram em fase final de elaboração. Esses trabalhos reportamos avanços mais significativos obtidos na representação e escalabilidade deste problema.Os planejadores de missão trabalhados na tese lidam com problemas estocásticos em ambientes não convexos, onde os riscos de colisão ou falhas no planejamento da missão são tratados e limitados a um valor tolerado. Os avanços na representação permitiram solucionar violações nos riscos presentes na modelagem original, além de tornar os modelos mais realistas ao incorporar aspectos como efeitos da resistência do ar. Para isso, técnicas eficientes de modelagem matemática permitiram avançar de um modelo de Programação Não-Linear Inteira Mista(PNLIM), originalmente proposto na literatura, para um problema de Programação Linear Inteira Mista (PLIM). A modelagem como um PLIM levou à resolução do problema de forma mais eficiente através do algoritmo branch-and-cut. As novas representações propostas resultaram em melhorias na escalabilidade, solucionando problemas mais complexos em um tempo computacional menor.Além disso,os avanços em escalabilidade mostraram-se mais efetivos quando técnicas combinando programação matemática e metaheurísticas foram aplicadas ao problema.

<div>The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and optimal methods of path planning integrated with a market-based task allocation strategy. Further work presents a solution to unmanned ground vehicle path planning within the developed mission planning system framework under incomplete information. Deep reinforcement learning is proposed to solve movement through unknown terrain environment. The final demonstration for Advantage-Actor Critic deep reinforcement learning elicits successful implementation of the proposed model.</div>

<p>Numerical trajectory optimization is helpful not only for mission planning but also design</p> <p>space exploration and quantifying vehicle performance. Direct methods for solving the opti-</p> <p>mal control problems, which first discretize the problem before applying necessary conditions</p> <p>of optimality, dominate the field of trajectory optimization because they are easier for the</p> <p>user to set up and are less reliant on a forming a good initial guess. On the other hand,</p> <p>many consider indirect methods, which apply the necessary conditions of optimality prior to</p> <p>discretization, too difficult to use for practical applications. Indirect methods though provide</p> <p>very high quality solutions, easily accessible sensitivity information, and faster convergence</p> <p>given a sufficiently good guess. Those strengths make indirect methods especially well-suited</p> <p>for generating large data sets for system analysis and worth revisiting.</p> <p>Recent advancements in the application of indirect methods have already mitigated many</p> <p>of the often cited issues. Automatic derivation of the necessary conditions with computer</p> <p>algebra systems have eliminated the manual step which was time-intensive and error-prone.</p> <p>Furthermore, regularization techniques have reduced problems which traditionally needed</p> <p>many phases and complex staging, like those with inequality path constraints, to a signifi-</p> <p>cantly easier to handle single arc. Finally, continuation methods can circumvent the small</p> <p>radius of convergence of indirect methods by gradually changing the problem and use previ-</p> <p>ously found solutions for guesses.</p> <p>The new optimal control problem solver Giuseppe incorporates and builds upon these</p> <p>advancements to make indirect methods more accessible and easily used. It seeks to enable</p> <p>greater research and creative approaches to problem solving by being more flexible and</p> <p>extensible than previous solvers. The solver accomplishes this by implementing a modular</p> <p>design with well-defined internal interfaces. Moreover, it allows the user easy access to and</p> <p>manipulation of component objects and functions to be use in the way best suited to solve</p> <p>a problem.</p> <p>A new technique simplifies and automates what was the predominate roadblock to using</p> <p>continuation, the generation of an initial guess for the seed solution. Reliable generation of</p> <p>a guess sufficient for convergence still usually required advanced knowledge optimal contrtheory or sometimes incorporation of an entirely separate optimization method. With the</p> <p>new method, a user only needs to supply initial states, a control profile, and a time-span</p> <p>over which to integrate. The guess generator then produces a guess for the “primal” problem</p> <p>through propagation of the initial value problem. It then estimates the “dual” (adjoint)</p> <p>variables by the Gauss-Newton method for solving the nonlinear least-squares problem. The</p> <p>decoupled approach prevents poorly guessed dual variables from altering the relatively easily</p> <p>guess primal variables. As a result, this method is simpler to use, faster to iterate, and much</p> <p>more reliable than previous guess generation techniques.</p> <p>Leveraging the continuation process also allows for greater insight into the solution space</p> <p>as there is only a small marginal cost to producing an additional nearby solutions. As a</p> <p>result, a user can quickly generate large families of solutions by sweeping parameters and</p> <p>modifying constraints. These families provide much greater insight in the general problem</p> <p>and underlying system than is obtainable with singular point solutions. One can extend</p> <p>these analyses to high-dimensional spaces through construction of compound continuation</p> <p>strategies expressible by directed trees.</p> <p>Lastly, a study into common convergence explicates their causes and recommends mitiga-</p> <p>tion strategies. In this area, the continuation process also serves an important role. Adaptive</p> <p>step-size routines usually suffice to handle common sensitivity issues and scaling constraints</p> <p>is simpler and out-performs scaling parameters directly. Issues arise when a cost functional</p> <p>becomes insensitive to the control, which a small control cost mitigates. The best perfor-</p> <p>mance of the solver requires proper sizing of the smoothing parameters used in regularization</p> <p>methods. An asymptotic increase in the magnitude of adjoint variables indicate approaching</p> <p>a feasibility boundary of the solution space.</p> <p>These techniques for indirect methods greatly facilitate their use and enable the gen-</p> <p>eration of large libraries of high-quality optimal trajectories for complex problems. In the</p> <p>future, these libraries can give a detailed account of vehicle performance throughout its flight</p> <p>envelope, feed higher-level system analyses, or inform real-time control applications.</p>

In-orbit calibrations of space instruments are often necessary to ensure the accuracy of the measurements. The Moon provides a target with very predictable characteristics. In this report, the opportunities to perform in-orbit lunar calibrations of the MicroCarb instrument are evaluated and a procedure for conducting the Mission Planning for these calibrations is developed. Through modeling the spacecraft in its orbit, simulations show that continuous observation sequences of up to 48 minutes can be expected each lunation. The variability of the optical properties of the Moon during an opportunity is related to the orientation of the plane of the orbit of the spacecraft with respect to the cone with axis the Moon-Sun direction and apex the center of the Moon that contains the spacecraft. Choosing a value of the phase angle (Sun-Moon-Spacecraft angle) around −20 degrees to plan the lunar calibrations allows to minimize the variations of apparent radiance of the Moon during the observation. The results make it possible to refine the choice of the best moments to plan the lunar calibrations. This also allows the satellite operations team to anticipate the planning of lunar calibrations on the scale of several months. / Kalibreringar i omloppsbana för rymdinstrument är ofta nödvändiga för att säkerställa mätningarnas noggrannhet. Månen utgör ett kalibreringsmål med mycket förutsägbara egenskaper. I denna rapport utvärderas möjligheterna att utföra månkalibreringar i omloppsbana för MicroCarb-instrumentet och ett förfarande för genomförande av uppdragsplanering för dessa kalibreringar har utvecklats. Genom att modellera rymdfarkosten i sin bana visar simuleringar att kontinuerliga observationssekvenser på upp till 48 minuter kan förväntas varje månvarv. Variationen hos de optiska egenskaperna för månen under ett tillfälle är relaterad till orienteringen av rymdfarkostens plan i förhållande till konen med axeln för månen-solens riktning. Att välja ett värde för fasvinkeln (Sun-Moon-Spacecraft-vinkel) på runt −20 grader vid planering av månkalibreringarna gör det möjligt att minimera variationerna i månens strålning under observationen. Resultaten gör det möjligt att förfina valet av de bästa tidpunkterna för månkalibreringarna. Detta gör det också möjligt för satellitoperationsteamet att förutse planeringen av månkalibreringar flera månader framåt.