High-Impact Performance Processing Capabilities in Operational Mission Planning and Scheduling Systems

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.

This special issue features the selected papers from i-SAIRAS 2010 (The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space) at Sapporo, Japan on August 29 - September 1, 2010), which explores the technology of Artificial Intelligence (AI), Automation and Robotics, and its application in space. In the AI domain, in particular, i-SAIRAS focuses on the following issues: (1) spacecraft autonomy (e.g., inboard software for mission planning and execution, resource management, fault protection, science data analysis, guidance, navigation and control, smart sensors, testing and validation, architectures); (2) mission operations automation (e.g., decision support tools for mission planning and scheduling, anomaly detection and fault analysis, innovative operations concepts, data visualization, secure commanding and networking); (3) design tools and optimization methods, electronic documentation; and (4) AI methods (e.g., automated planning and scheduling, agents model-based reasoning, machine learning and data mining). In the selection process for JACIII (Journal of Advanced Computational Intelligence and Intelligent Informatics), 13 papers were firstly nominated from 133 oral presentation papers as outstanding AI-related papers by i-SAIRAS International Committee, and 6 papers were finally accepted through the two-stages of pear-reviews. All papers were reviewed by three reviewers. As the brief introduction of these papers, the paper by Mark Johnston and Mark Giuliano presents an architecture called MUSE (Multi-User Scheduling Environment) to integrate multi-objective evolutionary algorithms with existing domain planning and scheduling tools. The second paper by Amdeo Cesta et al. discusses general lessons learned from a series of deployed planning and scheduling systems. The third paper by Alessandro Donati et al. spotlights specific achievements and trends in the area of spacecraft diagnosis and mission planning and scheduling. The fourth paper by Cedric Cocaud and Takashi Kubota proposes the system that provides position and attitude information to a spacecraft during its approach descent and landing phase toward the surface of an asteroid. The firth paper by Tomohiro Harada et al. studies On-Board Computer which evolves computer programs through the bit inversion and analyzes its robustness to the bit inversion. Finally, the last paper by Masayuki Otani et al. explores the distributed control of the multiple robots which may be broken in the assembly of space solar power satellite. The editor hopes that these papers would help for readers to capture the state-of-art of AI technology in space.

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.

: 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.

Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

The emergence of uncertainty factors will exert a strong influence on the accuracy of workshop scheduling. Digital twin’ technology and planning and scheduling are integrated to form a planning and scheduling system based on digital twin, which can effectively use the concept of twin to carry out all-around management and control of the uncertain factors in production activities so that the planning and scheduling scheme in the production process can accurately guide the actual production. Firstly, the system establishes management and control mechanism of planning and scheduling system based on digital twin. Second, it establishes a digital twin model of the planning and scheduling system for the management and control mechanism. Finally, the key technology of the planning and scheduling system is expounded, which mainly includes the perception and collection of total production factor information and the planning and scheduling prediction. In order to verify the validity of the scheme, the planning and scheduling system suited for the enterprise is designed and developed in combination with the frame workshop.

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.

Hyperspectral Imager Suite (HISUI) is a Japanese future spaceborne hyperspectral instrument being developed by Ministry of Economy, Trade, and Industry (METI) and will be launched in 2016 or later. HISUI’s operation strategic study is described in this paper. In HISUI project, Operation Mission Planning (OMP) team will make long- and short-term observation strategy of the sensor. OMP is important for HISUI especially for hyperspectral sensor with narrow swath of 30 km. There are two major limitations on the operation of HISUI Hyperspectral Imager. The first one is the maximum observation time per orbit. This is due to the cooling systems of the instrument to keep the instruments temperature within the design requirements. The maximum observation time per orbit is set to 15 minutes as the current baseline. The second one is maximum data downlink amount per day. This is a limitation given by communication link of the satellite bus and heavily depends on the operation of the platform satellite. The current baseline is 150 GB per day for hyperspectral sensor and 550 GB for multispectral sensor. We have developed observation coverage simulation program and studied the relationship between the limitations of sensor operation and the planned observation scenarios. The achievements of global mapping or regional monitoring need to be simulated precisely before launch. We have prepared daily global high resolution (30 second in latitude and longitude) cloud coverage data. The results of the simulations shows that HISUI will be able to acquire cloud free image of about 70 % of the terrestrial surface in three years (at the condition of 150 GB/day downlink rate).

The purpose of this paper is to present a computerized integrated project management system and report results of a survey on the effectiveness of the system. The system consists of a scheduling system, material management system, labor/equipment system, and safety/quality control system. The backbone system is a scheduling system that adopts a production planning system and a project scheduling system. The lowest level in the scheduling system is a daily work management system, which is linked to each functional management system (i.e. material management system, labor/equipment system, and safety/quality control system). The paper focuses on the material management and scheduling systems to implement a material “pull” system to reduce material inventories on site. Details of material management and scheduling systems are discussed, and a sample application is presented to demonstrate the features of the proposed computer application system. The paper presents practitioners and researchers with a practical tool to integrate material management and scheduling systems for site personnel.

A simulation-based modular planning and scheduling system developed for semiconductor fabrication facilities (SFFs) is discussed. Firstly, the general structure model (GSM) for SFFs, composed of a configurable definition layer, a physical layer, a process information layer and a planning and scheduling layer, is proposed. Secondly, a data-based dynamic simulation modeling method is given. Thirdly, a simulation-based modular planning and scheduling system (SMPSS) for SFFs, including model modules, release control modules, scheduling modules and rescheduling modules, is designed and developed. Finally, a case study is used to demonstrate the effectiveness of

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 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.

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.

The Moon Trek portal (https://trek.nasa.gov/moon) aims to provide the scientific community as well as the general public access to lunar data collected from various lunar missions. The portal also offers a suite of tools with the goal of allowing users to analyze the data for the purposes of education, mission planning, and research. Such tools include elevation profilers, crater and rock detection, lighting analysis, and slope analysis to name a few. Moon Trek is further expanding its analytic capabilities by adding feature detection to its toolset.The feature detector, similar to the rock and crater detection tools, seeks to detect features on the lunar surface using orbital imagery. Unlike the detection tools currently available on the Moon Trek, the feature detector is built to be generic, trainable, and able to seek out any feature when provided a training set for the feature in question. The tool currently supports detection of craters, rocks, and lunar pits.The feature detector takes a deep learning approach in finding features from orbital imagery. The model used in the latest detection tool is a Faster Region Based Convolutional Neural Network (Faster-RCNN) with a finetuning approach. More succinctly, the finetuning approach uses a model which has been developed and trained on a different and larger training set. The classification layer is replaced to detect features of the chosen domain (rocks, pits, craters, etc.) The model is then trained with smaller training sets.Currently we use panchromatic Narrow Angle Camera (NAC) images from the Lunar Reconnaissance Orbiter Camera (LROC) as input. However, the model can be trained on orbital imagery from any mission. The tool’s output includes the NAC image with bounding boxes over detected and an ascii file showing pixel coordinates of each detected feature.

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.