Mission Scheduling Research Articles

Mission scheduling optimization of multi-optical satellites for multi-aerial targets staring surveillance

The paper investigates the emergency scheduling problem of multiple aerial targets staring surveillance with multiple optical satellites (MSMT). To fulfill the requirement of state estimation and trajectory prediction, a novel scheduling model including specified duration of cooperative observation and revisits has been proposed. The total observation profit, introducing the independent observation for detection efficiency and cooperative observation for positioning precision, is regarded as the optimization objective. The corresponding time-varying observation gain functions of the two kinds of observations are illustrated respectively. Combined with the constraints of visibility, optic axis (OA) adjustment ability, cooperation duration and immunity, the constraint satisfaction mission scheduling model is established. In addition, a cooperation-oriented ant colony optimization algorithm (CO-ACO) is designed to determine the observation sequence for each satellite. Five elements for heuristics, balancing the quality and quantity of observations, are introduced to the design which can fully exploit the intelligence and improve the algorithm effectiveness. Simulations are performed to verify the effectiveness and demonstrate the superiority to existing algorithms under different conditions in terms of optimization performance.

The NASA human system risk mitigation process for space exploration

This paper describes NASA's innovative approach to assess and mitigate astronaut health and performance risks for space exploration missions beyond Low-Earth Orbit, with Mars as the focal destination. As human space missions gradually become more autonomous and complex to reach the Mars goal, the original risk management methodology falls short of providing accurate risk evaluations for the development of the corresponding countermeasures. A more systematic and refined approach is required to manage all the risks related to in-mission astronaut health and productivity, as well as the long-term effects on terrestrial quality of life after landing. Another important aspect of this new method is the ability to stratify all the human system risks relative to each other so effective tradeoffs can be made resulting in an optimized set of medical and performance competencies for future space mission architectures. The management of these risks at the portfolio level is based on several factors, not just Likelihood versus Consequence (LxC) scoring and its corresponding color: Red, Yellow, or Green. First, the resultant LxC score for each mission should include the respective level of uncertainty in the scoring. This uncertainty is determined by the evidence source, which ranges from cellular terrestrial to human inflight, the evidence type ranging from case study to causation, and the number of studies yielding similar results. Second, the risk characterization or “risk state of knowledge” should include the effectiveness level of current countermeasures to determine the need for further mitigation strategies. Third, the cost versus benefit evaluation of additional mitigation should be stacked against specific mission schedules; this will determine if the pending product development timelines meet mission deadlines. There is no immediate benefit in investing in technology development if the products will not be ready for the intended mission. Lastly, the subject matter experts and risk decision makers should review all the components of the previous evidence and debate in a formal setting the most sensible path forward to manage these risks, as every decision is important and unique. The decision process, with all pertinent parties involved, should provide insight into the possible negative effects of implementing a particular countermeasure on other human system risks; thus, the implementation of a particular mitigation should not increase the overall risk posture of the entire portfolio. The following content describes this methodology and its impact to assessing NASA's missions.

Mission planning and scheduling for Earth observation space system

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 optimisation NP-hard problem which 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 manoeuvrability, placed in different orbits, and loaded with different payloads. The system's user can choose one of six optimisation 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 one month planning horizon.

RVD flight mission planning and scheduling method based on finite state machine

RVD flight mission planning and scheduling method based on finite state machine

Simulation and 3D Visualization of Mission Scheduling for Imaging Satellites

Simulation, 3D visualization and analyses of satellite imaging tasks are capable to display orbital maneuver and ground cover scheme, which are much helpful to ensure the feasibility and reliability of the mission planning and to improve the efficiency of the satellite utilization. A system of 3D visualization and simulation for satellites is proposed and presented in the current work. Multi-resolution mass terrain data, images and vector data are processed and seamless stitched, and dynamic roaming of large range scenes based on digital earth is built as well. Then, the satellite flight status, maneuver, onboard sensor states and ground coverage ranges are visualized by using data of the satellite mission scheduling. Moreover, the system provides the visualization analyses of multi-dimension, high fidelity, free observation and so on. The proposed system is evaluated by practical satellite data and the experiments show that the system is capable to offer an intuitive, convenient and effective way for the space exploration and engineering design of satellites.

Analytical field-of-regard representation for rapid and accurate prediction of agile satellite imaging opportunities

Agile Earth observation satellites (AEOSs) possess high-attitude maneuverability and achieve better efficiency than traditional platforms in imaging missions. Onboard mission scheduling of agile satellites requires rapid and accurate prediction of imaging opportunities. This problem has not yet been fully investigated in the previous literature. Our study presents a precise analytical model for field-of-regard (FoR) representation of agile satellites and applies a self-adaptive Hermite interpolation technique for rapid determination of imaging opportunities. The FoR of an agile satellite is regarded as a large virtual field-of-view (FoV) synthesized from infinite instantaneous FoVs of onboard camera corresponding to all possible orientations of agile satellite within limits of maximum maneuverability. Analytical equations of the bounding envelope of the FoR are formulated through quadratic curve fitting. Visibility criteria of the analytical FoR model are derived and piecewise cubic polynomials featured with autonomous searching for interpolation points are used to approximate the visibility functions. Numerical simulations show that the analytical FoR model approximates the truth model with an accuracy of better than 99%. In addition, the self-adaptive algorithm significantly reduces the computational cost for imaging opportunity determination compared to traditional trajectory checking method.

Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response.

Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.

Mission Scheduling in Space Network With Antenna Dynamic Setup Times

Due to the constantly increasing mission demands for the space network (SN), the technical issue of high-efficient scheduling has attracted continuous attention in recent years. Most of the previous scheduling algorithms have generally ignored two critical factors, i.e., the dynamic property of single access (SA) antenna's setup times and the time-space distribution characteristics of users’ requested missions. In this paper, we build a mathematical model for the heterogeneous intersatellite link antenna (ILA) pointing route problem with the consideration of constraints on visibility windows and dynamic setup times. Specifically, the ILA's pointing mechanics cost and the missions’ time-space distribution are first taken into account into the scheduling model. Meanwhile, a new two-stage heuristic algorithm with hierarchical scheduling strategies is proposed to solve this optimization model. Finally, we employ the SN dataset to verify our proposed model by comparing three algorithms, the existing greedy randomized adaptive search procedure (GRASP) algorithm with static setup times, our improved GRASP algorithm with dynamic setup times, and our proposed two-stage heuristic algorithm. Experimental results show that our improved GRASP can schedule 3.72%, 10.36%, 12.51% more missions and can consume 53.18%, 49.95%, and 49.32% less setup times of SA antennas than the original Rojanasoonthon's GRASP for the mission scale of 200, 400, and 600, respectively. Moreover, compared with the improved GRASP algorithm, our proposed two-stage heuristic algorithm is more effective, which can further enhance the total number of scheduled missions by 2.41%, 4.43%, and 6.02% and reduce the setup times of SA antennas by 11.84%, 10.38%, and 9.54%.

Optimal Energy Management of UAV-Based Cellular Networks Powered by Solar Panels and Batteries: Formulation and Solutions

We focus on the problem of managing the energy consumption of a cellular network tailored to cover rural and low-income areas. The considered architecture exploits Unmanned Aerial Vehicles (UAVs) to ensure wireless coverage, as well as Solar Panels (SPs) and batteries installed in a set of ground sites, which provides the energy required to recharge the UAVs. We then target the maximization of the energy stored in the UAVs and in the ground sites, by ensuring the coverage of the territory through the scheduling of the UAV missions over space and time. After providing the problem formulation, we face its complexity by proposing a decomposition-based approach and by designing a brand-new genetic algorithm. The results, obtained over a set of representative case studies, reveal that there exists a trade-off between the UAVs battery level, the ground sites battery level, and the level of coverage. In addition, both the decomposed version and the genetic algorithm perform sufficiently close to the integrated model, with a strong improvement in the computation times.

High-Efficient Resource Allocation in Data Relay Satellite Systems With Users Behavior Coordination

In data relay satellite (DRS) systems, users compete for limited intersatellite link antenna beam (ILAB) resources of data relay satellites by submitting excessive service requests to the mission scheduling center (MSC), which leads to serious scheduling conflicts and considerably degrades the QoS (quality of service). Two key challenges, the effective mission scheduling, and the coordination of multiple users’ behaviors in submitting service requests, are involved in this issue. Most of the previous works have only focused on the mission scheduling issue, while neglecting the impact of users’ selfish behavior on the QoS. In this paper, we investigate the resource allocation problem in DRS systems from the perspective of users’ behavior analysis, i.e., joint coordination of users’ selfish behavior in submitting service requests and mission scheduling. Since those selfish users have no incentive to cooperate in the one-shot interaction, we construct a repeated game framework with a proper punishment and forgiveness strategy to maximize users’ payoffs and meanwhile to reduce the resource conflicts through a cooperative way. Moreover, an efficient cheat-proof mechanism is proposed to make users self-enforced to submit his/her actual resource requirement. Simulation results confirm that the proposed scheme can significantly improve the system operational performance. Compared with the payoffs under the Nash equilibrium in the one-shot game which is the main method of the current coordination scheme for DRS systems, our proposed joint method based on the repeated game framework can acquire 1.36 to 3.46 times gain in respect to 2 to 10 users, and also effectively decrease the resource conflicts.

Channel-Aware Mission Scheduling in Broadband Data Relay Satellite Networks

Mission scheduling algorithms are envisioned as critical to satisfy the increasing mission requirements in broadband data relay satellite networks, which is severely influenced by time-varying inter-satellite contacts (i.e., potential available communication links) and differentiated satellite downlink contacts. Nevertheless, the intertwined effect of such two types of contacts on mission schedules poses daunting challenges for the efficient mission scheduling design. In this paper, to achieve fair performance among user satellites, we maximize the minimum number of successfully scheduled missions over all user satellites by jointly optimizing contact plan design, power allocation (PA) in relay satellites, and mission schedules based on the time-expanded graph. The formulated problem is a mixed-integer nonlinear program optimization problem that is challenging to solve. For tractability purpose, we equivalently decompose the problem into a PA problem and an optimal PA-based mission scheduling (OPA_MS) problem, which is still a mixed-integer linear program. We further devise a new two-stage scheme to efficiently solve the OPA_MS problem. Simulation results validate the significant gains of the proposed algorithm in mission completion number and necessitate the consideration of the time-varying and differentiated inter-satellite and downlink contacts.

IMARS Phase 2A Draft Mission Architecture and Science Management Plan for the Return of Samples from Mars Phase 2 Report of the International Mars Architecture for the Return of Samples (iMARS) Working Group.

iMARS Phase 2A Draft Mission Architecture and Science Management Plan for the Return of Samples from Mars Phase 2 Report of the International Mars Architecture for the Return of Samples (iMARS) Working Group.

Joint dynamic scheduling of missions and maintenance for a commercial heavy vehicle: value of on-line information

Jointly optimizing maintenance and missions schedule for a vehicle has become a priority to improve the profitability for commercial heavy vehicles. Many research works are interested in optimizing either the preventive maintenance planning or the production planning but few researches are focused on scheduling both activities for a system which deteriorates over time. A static approach has previously been developed to jointly schedule missions and maintenance operations while considering the vehicle deterioration to minimize the maintenance costs. However, once the schedule is determined, no update is considered during its completion. This contribution presents a dynamic method to answer the joint scheduling problem based on the failure opportunities and the collected deterioration information. A comparison between the static and the dynamic approaches is drawn to evaluate the possible maintenance cost gain brought by the on-line information availability.

Deflection of fictitious asteroid 2017 PDC: Ion beam vs. kinetic impactor

Mission scenarios for the deflection of fictitious asteroid 2017 PDC are investigated. Two deflection options, kinetic impactor (KI) and ion beam shepherd (IBS), are studied and compared on the basis of deflection performance, safety, as well as mission schedule and political aspects. Firstly, we propose the launch of a medium-size rendevous spacecraft equipped with at least two ionic thrusters that can serve as propulsion means for the interplanetary trajectory up to rendezvous with the asteroid and as contactless actuators for a possible follow-up deflection mission. The asteroid, whose uncertainty ellipsoid is initially too large to establish whether (and how) it should be deflected, is reached by the rendezvous spacecraft after a low-thrust interplanetary trajectory of reasonable duration. Following rendezvous the spacecraft is placed in the vicinity of the asteroid to estimate its mass, study its structure and composition and, crucially, reduce its uncertainty ellipsoid by ground tracking to confirm or rule out an impact. Assuming that an impact is confirmed two main deflection scenarios are considered based on the actual asteroid size. Ion beam deflection is considered with the possibility of full deflection (the asteroid misses the Earth by a safe margin) or impact location adjustment (the impact footprint is diplaced to the nearest unpopulated region) depending on the asteroid size and the predicted impact location. The launch of a kinetic impactor mission is also considered with the employment of the rendezvous spacecraft to measure the deflection outcome and possibly to refine the deflection in case it is needed. The deflection performance of the two methods is compared.

Research on detection mission scheduling strategy for the LEO constellation to multiple targets

The space-based infrared LEO constellation has the ability of surveillance and tracking of ballistic missiles during entire phases, which has played an important role in the ballistic missile defense system (BMDS). This paper focuses on the scheduling strategy for the LEO constellation to multiple targets. The concept of average contribution of the satellite is firstly introduced, transforming the dynamic scheduling process to continuous intervals. The switching frequency and relaxation degree are also considered as decision variables. Then the multilayer coding genetic algorithm is improved in order to solve the scheduling problem. Finally, a scheduling demonstration validates the correctness and effectiveness of the presented method. The theory analysis and simulation results of this paper can provide powerful support for future design of the LEO constellation and research on the BMDS.

Determining the amplitude of Mercury's long period librations with the BepiColombo radio science experiment⋆

The Mercury Orbiter Radio science Experiment (MORE) is one of the experiments on-board the ESA/JAXA BepiColombo mission to Mercury. A crucial goal of MORE is to determine the gravity field and rotational state of Mercury in order to enable a better understanding of the planet's geophysics. The authors have recently reported on the results of a set of simulations of the MORE gravimetry and rotation experiments, carried out with the dedicated ORBIT14 software. Since that time, the launch date has been postponed twice, leading to a shift of more than one year in the orbital phase of the mission. Actually, the updated schedule results in a more suitable planetary configuration to determine the amplitude of the forced librations in longitude induced by Jupiter. In fact, the amplitude can be considerably enhanced due to a near-resonance with the free librations period, a key parameter to constrain the interior structure of Mercury. We show that the newest launch date allows the measurement of the long period librations amplitude forced by Jupiter with an accuracy of some tenth of arcseconds, a significant improvement with respect to the results with the previous mission schedule.

Swarm satellite mission scheduling & planning using Hybrid Dynamic Mutation Genetic Algorithm

Space missions have traditionally been controlled by operators from a mission control center. Given the increasing number of satellites for some space missions, generating a command list for multiple satellites can be time-consuming and inefficient. Developing multi-satellite, onboard mission scheduling & planning techniques is, therefore, a key research field for future space mission operations. In this paper, an improved Genetic Algorithm (GA) using a new mutation strategy is proposed as a mission scheduling algorithm. This new mutation strategy, called Hybrid Dynamic Mutation (HDM), combines the advantages of both dynamic mutation strategy and adaptive mutation strategy, overcoming weaknesses such as early convergence and long computing time, which helps standard GA to be more efficient and accurate in dealing with complex missions. HDM-GA shows excellent performance in solving both unconstrained and constrained test functions. The experiments of using HDM-GA to simulate a multi-satellite, mission scheduling problem demonstrates that both the computation time and success rate mission requirements can be met. The results of a comparative test between HDM-GA and three other mutation strategies also show that HDM has outstanding performance in terms of speed and reliability.

Research on Mission Scheduling of Electronic Reconnaissance Satellites Based on Multi-area

Regarding to the issue of how electronic reconnaissance satellites (ERS) execute the regional survey mission task. Time, area and frequency are discretization handled in this paper. Based on the analysis of different occasions and task requirements on ERS, multi-Constrained Optimization Model is established to solve relative problems. Through modifying the structure of code, initializing the method and introducing the penalty function, genetic algorithm could apply to the model. Conclusion of the emulation indicates that the ERS area census task could be effectively satisfied by the model and algorithm in this paper.

Hybrid Interpreting Framework of the Unified Scheduling Language

The unified scheduling language is a Chinese domain-specific language with both programming and natural language features for space mission scheduling. To create a functional and yet simple language for all space-mission-related staff, a hybrid framework for language defining and interpreting is proposed. This framework combines script programming languages and natural language processing technologies, and it creates a language that is easy to learn yet still powerful to fulfill the automating and extending requirements of mission scheduling. A coordinated natural language processing approach is designed to parse human-oriented languages, and a general-purpose script engine is integrated for processing the script aspect of the unified scheduling language. The translating mechanism maintains logic consistency between the two parsers. Related logic models and algorithms are introduced to illustrate the parsing mechanism. This framework achieves sound practical results in the unified scheduling language, considering a valid domain-specific language system is built and parsed correctly. The framework can be generalized to other Chinese domain-specific languages in various application fields.

Antenna Slewing Time Aware Mission Scheduling in Space Networks

The slow antenna slewing rate on data relay satellites is a main performance bottleneck for space networks. This letter investigates the antenna slewing time aware schedule problem aiming at maximizing the number of successful delivered missions. Particularly, we extend the traditional time-expanded graph into a new model, with which the impact of antenna slewing time on the serving sequence of antennas is well depicted. Then, we formulate the schedule problem into a mixed integer non-linear programming and propose a polynomial time algorithm to solve it. Simulation results show that our scheme can improve the number of successfully scheduled missions significantly while guaranteeing good fairness.