Research Progress on Requirements Integrated Preprocessing and Mission Planning for Earth Observation Satellites

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

The KOMPSAT-2 (KOrea Multi-Purpose SATellite-2) was launched in July 28, 2006 and has been normally operated. Mission planning system provides the capability to make conflict-free satellite mission schedule by predicting and analyzing satellite orbital events and users’ imaging requests. The amount of satellite mission planning has been increased due to the growing number of image requests. In order to meet the demands of lots of satellite mission planning activities, all mission plans have to be automatically verified without human intervention. Automation concept is also needed because these planning activities have to be done by the pre-defined timeline. To resolve these problems, MPVS(Mission Planning Verification System) has been developed by mission planning team. The MPVS automatically verifies mission plan with satellite contact schedules, resources and constraints. This paper presents the operational concept of the MPVS which verifies mission plan of the KOMPSAT-2. Also design architecture of the MPVS which is composed of the mission planning verification module, command planning verification module, contact schedule generation module, and database including resources and constraints is presented. This paper shows operation results of the MPVS and validity of the KOMPSAT-2 operation.

Autonomous mission planning of Earth observation satellite based on onboard cloud detection

Agile Earth observation satellite (AEOS) mission planning (AEOSMP) problem aims to optimize observation efficiency by selecting and scheduling tasks from the Earth’s surface, subject to complex resource constraints. Increased flexibility of AEOS presents challenges for autonomous mission planning and scheduling. Deep reinforcement learning (DRL) and clustering tasks are two approaches to enhance the autonomy and observation efficiency of AEOSMP. This letter introduces two innovative algorithms to tackle the AEOSMP problem: the Sequential Clique Clustering and PPO Planning algorithm (SCC-PPO) and the Simultaneous Clustering and Planning PPO Algorithm (SCP-PPO). SCC-PPO initially partitions the mission tasks into cliques, followed by planning. In contrast, SCP-PPO combines clustering and planning into a single, concurrent process. Numerical simulations reveal that SCP-PPO enhances the observation reward by 1.01% to 11.43% compared to SCC-PPO.

: Current mission preparation and analysis methods place an undue burden of effort on conventional and special operations forces to effectively synchronize and execute their increasingly complex operational responsibilities in a rapidly changing global environment. This thesis developed a tool for the United States Special Operations Command (USSOCOM) in support of their Mission Planning, Analysis, Rehearsal, and Execution (MPARE) initiative to allow special operations forces commanders and staffs to conduct mission planning and analysis in a distributed environment, and rapidly produce dynamic synchronization matrices and scheduling products. Operations research methods provide the foundation for the analysis. The system developed in this thesis is called the Special Operations Mission Planning and Analysis Support System (SOMPASS). SOMPASS is simple to learn and operate, provides dynamic changes with little effort, and is universal in application. This system has the capability to execute on any hardware platform, operate across any network connection, and expand easily to support additional users and requirements. This thesis provides not only a demonstration of capabilities through a special operations oriented illustrative scenario, but also a working product that can be adapted for use in mission planning and analysis by all units under USSOCOM.

A multi-platform active debris removal mission planning method based on DCOP with chain topology

The European Space Operations Centre (ESOC) is responsible for operating spacecraft covering a large range of mission types. These range from planetary probes/deep space missions through observatories and science missions to earth observation. All of these different types of missions have the need to plan operations on the spacecraft and, increasingly, of the ground segment. Typically the planning systems used to carry out this process have been implemented on a mission specific basis, at best utilizing some elements from a similar mission type. Reuse across different mission types has typically been almost non-existent. In order to increase synergy in the development of mission planning systems ESOC has started work to define a mission planning framework that can be used to build planning systems for any of the mission types operated. To do this it has been necessary to obtain input from the flight control teams of missions in operation and development. This has proved to be an interesting exercise as one of the main issues has been defining the terminology that was required. Whilst the terminology used amongst missions of the same type is similar it varies significantly between the mission types. This obviously adds considerably to the difficulty in trying to define a common approach to mission planning. To address this issue it was first necessary to define a set of terminology that was clear to all participants and could be used as the basis for defining a common concept. Once this has been done it then proved possible to identify a workflow that was largely applicable to most (although not all) missions. This workflow is as seen by the “planning team” part of a flight control team (FCT); no assumptions have been made as to whether the planning team is solely dedicated to planning activities, only that the planning is carried out as an FCT function.

<p>Aiming at the problems of high time-consuming and insufficient guarantee of high-priority task completion in the large-scale multi-satellite multi-observation mission planning application, a multi-satellite multi-mission planning model based on preemptive priority model is proposed. Combined with the whole-neighborhood greedy search strategy, an improved tabu search algorithm is designed, taking the combination of satellite observation windows as the decision variables. The designed algorithm realizes an efficient solver to the proposed mission planning model. The simulation results show that the average solving time of the algorithm is 144s under the problem scale of 100 satellites and 3000 tasks. Compared with the existing algorithm, the calculation time is shortened by at least 75% under the premise that the total number of planning tasks is comparable, which is more in line with the high time efficiency requirements for satellite mission planning in engineering applications.</p> <p> </p>

Satellite mission planning is the basis and top-level work of space missions and the beginning of each space mission. Therefore, the scientific research of satellite mission planning is very important. By analyzing the existing research results, we can know that the research on task planning mainly focuses on three aspects: research objects, established model, and solution algorithm. Starting from these three aspects vertically and then horizontally, this paper comprehensively discusses the theoretical basis, application, and advantages and disadvantages of related technologies in the research literature in recent years. Finally, based on the research on satellite mission planning, this paper puts forward its own views on the future development direction and research focus.

Influenced by complex terrain conditions of combat environments and constrained by the level of communication technology, communication among unmanned aerial vehicles (UAV) is greatly restricted. In light of this situation, mission planning for UAV swarms under limited communication has become a difficult problem. This paper introduces motifs as the basic unit of configuration and proposes a motif-based mission planning method considering dynamic reconfiguration. This method uses multidimensional dynamic list scheduling algorithm to generate a mission planning scheme based on the motif-based swarm configuration solution. Then it incorporates order preserved operators with NSGA-III algorithm to find Pareto front solutions of all possible mission planning schemes. The feasibility of this mission planning method is validated through a case study.

In this paper, a mission planning system is presented that generates mission plans for a group of unmanned aerial vehicles (UAVs) to provide continuous surveillance over an urban area. Given the information of terrain and buildings in the target area, a two-stage approach is employed to solve the problem. In the first stage, a set of camera locations called the vantage set is generated that provides complete coverage of the target area. In the second stage, one or several UAVs are determined to collectively share the vantage set and their individual paths are generated to carry out the continuous surveillance duty. In both stages, evolutionary algorithms (genetic algorithm for vantage set generation and ant colony system for UAV/path planning) are used to search for the optimal solution. During the search, constraints such as the flying capabilities of UAVs and collision avoidance are imposed to guarantee the feasibility of the final result.

The United States and its partners have begun a program to extend human presence in the solar system, beginning with a return to the Moon. The Lunar Reconnaissance Orbiter (LRO) is the first step in this endeavor, an unmanned mission to create the comprehensive atlas of the Moon's features and resources necessary to design and build a lunar outpost. LRO focuses on the selection of safe landing sites, identification of lunar resources, and the study of how the lunar radiation environment will affect humans. In order to achieve its objectives LRO carries a payload comprised of six instruments and one technology demonstration. In addition to its scientific mission LRO will use new technologies, systems and flight operations concepts to reduce risk and increase productivity of future missions. As part of the effort to achieve robust and efficient operations the LRO Mission Operations Team (MOT) will use its Mission Planning System (MPS) to manage the operational activities of the mission during the Lunar Orbit Insertion (LOI) and operational phases of the mission. The MPS, based on GMV’s flexplan tool, will receive activity and slew maneuver requests from multiple science operations centers (SOC), as well as from the spacecraft engineers. flexplan will apply scheduling rules to all the requests received and will generate conflict free command schedules in the form of daily stored command loads for the orbiter and a set of daily pass scripts that help automate nominal real-time operations. The MPS will also track the status of onboard flight software load files in the various onboard memory locations. The LRO mission is using a new Flight Software architecture, the Core Flight Executive (cFE), in which all onboard memory is managed as a file system. The MPS will interact with the ITOS real-time system to receive messages whenever changes are made to the memory content. The MPS will further interact with the real-time system in a closed loop mode to monitor the progress of planned activities. The LRO mission will use this feedback to provide continuous visibility of the status of daily operations to all of its team members via flexplan web-based interface, which includes a timeline of upcoming operations. By using a single MPS system to schedule multiple mission elements and generate automation products, the MOT will be able to reduce the risk of operator errors, while maintaining a high level of mission operations awareness among all mission elements. This in turn, achieves safer and more efficient flight operations at low cost.

In this paper, we proposed a new method for solving the problem of mission planning for large area target scanning by multiple Earth Observation Satellites. Firstly, based on a variety of physical constraints and specific mission constraints, the mission planning model is established. Then a simple and efficient area decomposition method is designed, by using this method and combining the physical constraints, the area target visibility extraction can be completed. Based on this method, the mission planning model can be simplified to an integer programming model. Then the adaptive genetic algorithm is designed to solve this model. Finally, the effectiveness and practicability of the proposed method are verified by simulation experiments.

The Dawn mission has successfully completed operations at its first target, the main belt asteroid Vesta. The quantity and quality of the returned science data exceeded expectations, thus validating the science and mission operations methodologies developed by the project. Preparations are now underway for Dawn’s second orbital mission, this one with the dwarf planet Ceres. Ceres mission operations will benefit from the lessons learned from Vesta operations, although mission planning and execution will be complicated by the absence of two of the spacecraft reaction wheels, the second of which was lost near the end of the Vesta mission. Fortunately, the spacecraft can adequately perform all required attitude control functions using only hydrazine-based thrusters in combination with its ion propulsion system. In addition, the project has developed an alternative attitude control capability that uses only two reaction wheels supplemented by thrusters. The latter capability consumes significantly less hydrazine than the thrusters alone, but the lifetimes of the remaining reaction wheels are unknown and their reliability is suspect. Therefore we have developed a plan that will meet all science requirements even without any further use of the reaction wheels. The guiding principles for developing the Ceres science and mission plans are as follows: 1) Conserve hydrazine during cruise to Ceres, 2) curtail further use of the two remaining reaction wheels until the lowest altitude orbit phase at Ceres, where the use of the wheels is most effective at saving hydrazine, 3) plan Ceres science data acquisition in a manner that achieves the primary science objectives while minimizing hydrazine consumption.

Advanced countries around the world are spurring the development of Unmanned Surface Vehicles (USVs) that can operate autonomously at marine environment. The key enabling technology for such USVs is the mission planning system (MPS) that can autonomously navigate through the harsh waters. The MPS not only has the functions for the navigation, but also has the capabilities, such as obstacle avoidance, malfunction corrections, dealing with unexpected events, return home functions, and many other eventualities that cannot be programmed in advance. The autonomy levels are increasingly moving higher and it is foreseeable that the trend will continue in the future. The main purpose of this paper is the analysis of the MPS onboard the USVs, in terms of the categories, functions, and technological details. Also, we analyze the case study of autonomous mission planning control systems in various fields and introduce the features that constitute the critical functionalities of the mission planning systems.