Mission Planning Systems for Commercial Small-Sat Earth Observation Constellations (original) (raw)
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10th International Workshop on Planning and Scheduling for Space (IWPSS), 2017
Over the next decade, hundreds of Earth Observation satellites are expected to be launched as large constellations, aiming at providing data for hundreds of applications. In order to support these applications and achieve the maximum possible value from EO constellations, Surrey Satellite Technology Ltd (SSTL), leading provider of small satellite missions for EO applications, is looking at new, efficient and user friendly solutions to constellation operations and tasking. This paper firstly presents the experience gained with our most recent and complex missions: DMC3 and NovaSAR. Both missions have introduced requirements that are becoming fundamental for future constellation missions. DMC3 is a high-resolution optical constellation whilst NovaSAR is our first Synthetic Aperture Radar (SAR) mission which is managed using a capacity share scheme amongst its various customers. The last part of the paper describes how SSTL is tackling these new challenges by developing a next generation constellation mission planning system.
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Mission planning and scheduling for Earth observation space system, 2020
Planning and scheduling systems are needed to manage Earth-observing satellites for satisfying the optimum usage of the constellation's resources. This is a combinatorial optimization NP-hard problem that is solved in this paper using the constraint programming technique. The proposed system can deal with a heterogeneous constellation that consists of satellites with different maneuverability, placed in different orbits, and loaded with different payloads. The system's user can choose one of six optimization objectives, three of them were not used before, for constructing the satellites' mission plan. Searching within the system is performed using one of five different search algorithms. The system produces plans with different planning horizons ranging from one track to more than one month. The obtained results depict that the proposed system behaves, comparatively, in a perfect manner even when dealing with a complicated case study consisting of three satellites, 2,500 targets, and a one-month planning horizon.
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2007 IEEE Aerospace Conference, 2007
The roles and interactions of activity planning and scheduling for Earth Observing Satellites are based on factors such as mission objective, system assets and resources, system and spacecraft constraints, planning criteria, scheduling strategies, timelines, and desired level of automation and operator interaction. Activities are generalized into four categories: accomplish the mission objective, support the mission objective, manage the system resources,
Mission Planning for a Low Cost Multi-Customer Imaging Service
The 15th International Conference on Space Operations - SpaceOps, 2018
In the coming decade, a 70% growth in Earth observation (EO) satellites is expected compared to the last decade [1]. This growth is expected in the form of multi-satellite constellations, which present unique operational challenges. Surrey Satellite Technology Ltd (SSTL), leading provider of small satellite missions for EO applications, is looking at new operational paradigms to manage these constellations and to offer their benefits to a broader audience. The core of the paper is a new operational approach called capacity share. Under this schema an SSTL owned mission can be operated directly by multiple customers. Each customer would own a share of the mission capacity and would have its own Mission Planning System to submit their imaging tasks. The paper presents the SSTL capacity share solution in terms of service and Mission Planning System architecture and illustrates how these are applied to the existing CARBONITE-2 mission. The last part of the paper describes SSTL's intentions in applying this schema to the future constellation missions and the challenges associated with it when designing the next generation of constellation planning system.
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SHAI is developing a software architecture for automated, distributed planning and coordination of constellations of satellites. This architecture allows large satellite constellations to manage themselves with minimal human oversight. SHAI is utilizing an integrated approach drawing upon a broad range of AI and non-AI techniques. Advanced planning and scheduling algorithms permit the system to quickly create complex plans satisfying intricate time and other constraints. A reactive planning component deals with unexpected, time-critical local events such as new critical tasks. In addition, a knowledge base stores information about the satellites' capabilities and commitments that is used during the distributed planning process to properly allocate tasks to the satellites best suited to perform them. The resulting architecture provides the capacity for robust, scalable management of satellite constellations. The potential benefits are reduced costs, increased operational efficiency, and improved robustness. A prototype utilizing a subset of the architecture has been built and verified. Sub-components Satellite D-SpaCPlanS Component Other On-board Component Ground Station (c) 2001 American Association for Artificial Intelligence (AAAI). www.aaai.org. Reproduced with permission from AAAI.
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Proceedings of the 6th …, 2002
We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the EOS scheduling problem, and proposes a stochastic heuristic search method for solving it.
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smc-it.org
The space industry has evolved in the past decades with the result that the main satellite operators are in charge of fleets of spacecraft manufactured by different companies with different characteristics. Additionally, many new scientific missions have come up with the quest to reach Mars and the Moon as well as a better knowledge of the Earth, with ever improving instruments and increased data recordings. The operation of multiple platform fleets or highly specialized missions have one thing in common: they require a tool that can plan and schedule while adapting to the ever changing requirements and constraints driven by the mission. flexplan is a COTS Mission Planning and Scheduling (MPS) product that has matured in the past years while integrated in such missions. Its primary goal of offering a flexible solution that can adapt with minor operational impact has been achieved and superseded by an increased focus on performance when multiple platforms and complex operations are present. This paper describes the latest upgrades as well as a sample application of flexplan.
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This article concerns the problem of managing the new generation of Agile Earth Observing Satellites (AEOS). This kind of satellites is presently studied by the French Centre National d'Études Spatiales (PLEIADES project). The mission of an Earth Observing Satellite is to acquire images of specified areas on the Earth surface, in response to observation requests from customers. Whereas non-agile satellites such as SPOT5 have only one degree of freedom for acquiring images, the new generation satellites have three, giving opportunities for a more efficient use of the satellite imaging capabilities. Counterwise to this advantage, the selection and scheduling of observations becomes significantly more difficult, due to the larger search space for potential solutions. Hence, selecting and scheduling observations of agile satellites is a highly combinatorial problem. This article sets out the overall problem and analyses its difficulties. Then it presents different methods which have been investigated in order to solve a simplified version of the complete problem: a greedy algorithm, a dynamic programming algorithm, a constraint programming approach and a local search method.
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This paper describes the ASPEN system for automation of planning and scheduling for space mission operations. ASPEN contains a number of innovations including: an expressive but easy to use modeling language, multiple search (inference) engines, iterative repair suited for mixed-initiative human in loop operations, real-time replanning and response (in the CASPER system), and plan optimization. ASPEN is being used for the Citizen Explorer (CX-1) (August 2000 launch) and the 2 nd Antarctic Mapping Missions (AMM-2) (September 2000). ASPEN has also been used to automate ground communications stations -automating generation of tracking plans for the Deep Space Terminal (DS-T). ASPEN has been used to demonstrate automated command generation and onboard planning for rovers and is currently being evaluated for operational use for the Mars-01 Marie Curie rover mission. CASPER, the soft real-time versions of ASPEN, has been demonstrated with the Jet Propulsion Laboratory (JPL) Mission Data Systems (MDS) Control Architecture prototypes. .
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The next generation of communications satellites may be designed as a fast packet-switched constellation of spacecraft able to withstand substantial bandwidth capacity uctuation ranging from unstable weather phenomena to intentional jamming of communication.