Mission Planning Verification System for KOMPSAT-2 Operations

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.

: 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.

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.

KOMPSAT (KOrea Multi-Purpose SATellite) series performing the mission in low earth orbit are operated by Korea Aerospace Research Institute (KARI) in Korea. To protect operational satellites from numerous space objects, the prediction of the conjunction is performed using in-house Automated Conjunction Analysis System (ACAS). For confirming the conjunction, the Two-Line Element (TLE) provided by US Strategic Command (USSTRATCOM) is used as the orbit information of space objects. This paper addresses the improved orbit prediction of the space objects taking TLE uncertainties in consideration. The TLE is used as pseudo-measurement and the orbit determination and the orbit prediction is performed through the flight dynamics system. In fitting the TLE orbit, the state vectors are assumed by successive TLEs in a certain period of time and the prediction error is confirmed according to the fitting period and the number of pseudo-observations. To find out the improvement of orbit prediction with the proposed method, the method is applied using KOMPSAT series having the precision orbit ephemerides in the meter-level range. Then the orbit result is applied to space objects under the same conditions. The orbit prediction is performed by adjusting the area to mass of the space objects and the results are compared with reference orbit. It is expected to obtain the more precise prediction results by performing daily proximity analysis using improved prediction of space objects and to utilize a detailed analysis with space objects at high risk.

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.

While there are numerous powerful tools to support Navy mission planning, the mission planning process still remains a hybrid planning activity across human operators and advanced tools. Advances in artificial intelligence (AI) have seen an increase in interest and use in the mission planning environment. Yet traditional approaches typically focus on optimizing the performance of the individual operator or the mission planning tool, not the joint problem solving that is needed for the human and AI team necessary in these envisioned mission planning environments. For example, while AI-based approaches can offload extensive processing by the human operator, outcomes of AI-based tools are seldom presented in ways that make it readily understand by a human or fit in with the overall process. This results in new and additional work for the operator as they must manually translate these outcomes into actionable information. There are also specific characteristics of AI-based approaches to consider, such as accounting for data limitations; many mission planning environments have data availability constraints or do not capture the right data. New methods are needed to specifically consider the benefits of AI-based approaches, ensure that AI derived insights are communicated effectively to operators, and optimally support operators in their own mission planning workflow.In this paper, we describe an overall approach for incorporating AI into the mission planning process. First, to consider how best to leverage the unique capabilities and constraints of a human operator and artificial intelligence technologies as a cooperative team throughout the mission planning process, we will frame the human operator and AI system as a single unit and take a joint cognitive system (JCS) analysis approach. This approach will identify the key information to jointly create the most effective mission plan most efficiently. Next, we describe a novel approach, neural policy programs (NPPs) to address some of the critical challenges of incorporating AI into the mission planning process. Finally, we describe our approach for extending to the StarCraft II game and creating a pre-game planning stage to prototype our approaches.KeywordsMission planningArtificial intelligenceJoint cognitive systems (JCS)Neural policy programsStarcraft II

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

Increased operational constraints on spacecraft and ground station operations have led to stricter ground software management and better performance throughput. For Mission Planning and Scheduling Systems, the ability to process numerous scheduling and planning input requests from multiple systems, applying high-level logistical rules, and providing export capabilities to all external entities, including the spacecraft itself, must be balanced against system performance. The constraint on the processing time of the schedule comes from a mission-defined need-by time, which is driven by execution or contact time. flexplan, an operational Mission Planning and Scheduling (MPS) system, has addressed the balance of performance and system capabilities. flexplan utilizes a combination of a relational database and soft-algorithm scheduling to maximize system performance. This paper demonstrates the method for the design of the internal database processing and maintenance. Performance methodologies and capabilities are presented to demonstrate software strength in two case studies: The Lunar Reconnaissance Orbiter (LRO) low lunar-orbit science mission currently operational and the Ground Network Schedule System (GNSS) multi-mission, multi-stations study of NASA’s infrastructure.

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.

1 A Single Orbital Revolution Planner for NASA’s EO-1 Spacecraft Mark Abramson , David Carter, Alex Kahn, Stephan Kolitz, Jason Riek, and Peter Scheidler Draper Laboratory, 555 Technology Square, Cambridge, MA, 02139 A new operational concept is being developed for NASA’s EO-1 spacecraft that will allow the public to request scenes to be imaged. When public imaging requests are accepted, the number of scene requests per revolution is expected to grow significantly. This paper describes a planner developed at Draper Laboratory that can be used by EO-1 operations staff to determine which image requests to accept on any given orbital revolution. Nomenclature € x = spacecraft state € t(x) = spacecraft state, associated UTC time Δt(x) = spacecraft state, imaging time remaining € φ (x) = spacecraft state, roll angle € φ = spacecraft roll rate € ts = settling time after a roll maneuver

The scale of Earth observation satellite system continues to expand and its application is becoming more and more extensive, which puts forward higher requirements for satellite mission planning system. This paper summarizes and analyzes some key issues in the mission planning of earth observation satellites. Firstly, the development status and the trend of earth observation satellites are introduced. Secondly, we summarize and analyze complex requirements of observation decomposition method, the priority of requirements determination method, the model construction and solving methods of single-satellite scheduling, multi-satellite integrated scheduling and multi-satellite dynamic scheduling in the process of requirements Integrated Preprocessing and mission planning. Finally, we discuss the problems that still need to be solved and the direction of research.

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.

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.

This paper describes an interactive computer-aiding system for tactical aircraft mission planning. A Multiple-Objective Decision-Making approach has been applied to the tactical mission planning domain. The planner specifies a set of potential flight routes and selects an optimum one as the result of a human-computer dialogue. In this dialogue, the planner iteratively specifies acceptable constraints and desired weights on several mission-related decision objectives. The system provides graphic feedback about the merit of the selected route vis a vis the specified objectives, and allows the planner to tailor the mission route plan to meet explicit decision objectives.

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations.