Multi-satellite Scheduling Research Articles

Scheduling of Earth observing satellites using distributed constraint optimization

Earth observation satellites (EOS) are satellites equipped with optical sensors that orbit the Earth to take photographs of particular areas at the request of users. With the development of space technology, the number of satellites has increased continuously. Yet still, the number of satellites cannot meet the explosive growth of applications. Thus, scheduling solutions are required to satisfy requests and obtain high observation efficiency. While the literature on multi-satellite scheduling is rich, most solutions are centralized algorithms. However, due to their cost, EOS systems are often co-funded by several agents (e.g., countries, companies, or research institutes). Central solutions require that these agents share their requests for observations with others. To date, there has yet to be a solution for EOS scheduling that protects the private information of the interested parties. In this study, we model the EOS scheduling problem as a distributed constraint optimization problem (DCOP). This modeling enables the generation of timetables for the satellites in a distributed manner without a priori sharing users’ private information with some central authority. For solving the resulting DCOP, we use and compare the results of two different local search algorithms—Distributed Stochastic Algorithm and Maximum Gain Message—which are known to produce efficient solutions in a timely manner. The modeling and solving of the resulting DCOP constitute our new solution method, which we term Distributed Satellite Timetable Solver (DSTS). Experimental evaluation reveals that the DSTS method provides solutions of higher quality than a commonly used centralized greedy algorithm and is comparable to an additional centralized algorithm that we propose.

Multisatellite Scheduling for Moving Targets Using the Enhanced Hybrid Genetic Simulated Annealing Algorithm and Observation Strip Selection

Multisatellite Scheduling for Moving Targets Using the Enhanced Hybrid Genetic Simulated Annealing Algorithm and Observation Strip Selection

A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

Successive observation oriented scheduling and task planning of heterogeneous constellation

This paper put forward a method to address the problem, of how to select multiple satellites from a given heterogeneous constellation and plan their tasks to implement a temporary mission for ground or aerial targets successive observation, while hardly affecting the constellation’s original mission as much as possible. First, considering the observation geometric constraints of the satellite on the target, an observability analysis model for effective observation of the satellite on the target is established to characterize the observable spatio-temporal scope. Considering the time-length requirement of sustained observation on the target, a multi-satellite-to-target sustained observation model is constructed for the scheduling of satellites. Second, to address the problem of selecting task satellites in a given heterogeneous constellation, a mathematical model for task planning is established to maximize observation benefits and minimize task burdens. Third, a hybrid method of multi-satellite scheduling and task planning is designed with fault tolerance, based on dynamic programming and an improved contract network protocol algorithm. Next, a joint simulation software system is established through the secondary development of STK and Python, which includes satellite-target observability analysis, task satellite selection, task allocation and planning, and full process visible demonstration functions. Finally, simulation cases verified the effectiveness and superiority of the proposed technique.

Multi-satellite cooperative scheduling method for large-scale tasks based on hybrid graph neural network and metaheuristic algorithm

Traditional heuristic optimization algorithms are no longer applicable to the multiple satellites scheduling with large-scale tasks, as they are unable to provide satisfactory performance in terms of convergence and speed when addressing complex constraint conflicts. Therefore, we propose a multi-satellite cooperative scheduling method for large-scale tasks based on a hybrid graph neural network (GNN) and metaheuristic algorithm. By using the representation and extraction capability of GNN for relations in graph, the features of large-scale tasks and their constraint relations were expressed, and the generalized knowledge was extracted. And combined with metaheuristic algorithm, the optimization framework for large-scale satellite task scheduling based knowledge and data was implemented. The method consists of a GNN pre-solver module for static constraint conflicts and a metaheuristic optimization module for dynamic constraint conflicts of satellite missions. In the GNN pre-solver module, the graph sample and aggregate network was used to learn and extract the common knowledge in the static task-conflict graph, and provided effective prior information for the metaheuristic optimization module. The quadratic unconstrained binary optimization loss function was designed with multiple influencing factors, to convert the discrete optimization function into a continuous function; a greedy threshold was also added to improve the task completion rate. Finally, numerical experimental results showed that the proposed method can achieve an efficient solution to the multi-satellite scheduling problem with tens of thousands tasks. Compared with the commonly used multi-satellite scheduling algorithms (the genetic algorithm (GA) and greedy-GA), the proposed method can obtain higher-quality solutions under the same conditions and greatly improve the computational efficiency of large-scale mission planning.

Learning multi-satellite scheduling policy with heterogeneous graph neural network

In this paper, we investigate the multi-satellite scheduling (MSS) problem, a combinatorial optimization problem (COP) that aims to enhance satellite utilization. Numerous scholars have conducted in-depth studies on this matter. Handcrafted policies are commonly employed in various algorithms. Despite the use of graphical representations, designing policies manually becomes increasingly difficult, particularly as the number of satellites and tasks grows. Consequently, we propose a novel approach for automatically summarizing graph structures and learning policies for MSS problem. The proposed approach is developed based on graph neural network, Transformer, and sequence to sequence framework. First, MSS problem is modeled as a heterogeneous graph following various constraints. Next, the encoder, which is based on GNN and Transformer, is designed to summarize crucial information from the aforementioned graph. Finally, a two-stage decoder is proposed taking into account dynamic information in stepwise solution generation. Since obtaining optimal solutions in practical applications is challenging, we train the model using reinforcement learning. The results of the experiment demonstrate that our proposed method performs effectively in various scenarios. The quality of the solutions generated through this method transcends that of conventional construction methods. Additionally, the method generates solutions quickly, which are comparable in quality to those produced by meta-heuristic algorithms.

Deep Reinforcement Learning-Based Periodic Earth Observation Scheduling for Agile Satellite Constellation

The development of remote sensing satellite constellations has created an increasing need for periodic observations of ground targets. At present, the research on periodic observation is not sufficient, and the multisatellite scheduling algorithm based on deep reinforcement learning (DRL) is still relatively few and is trained inefficiently. In this paper, a periodic observation mission planning (POMP) algorithm is proposed for the periodic Earth observation scheduling problem. The POMP is based on the encoder/decoder architecture. First, static and dynamic attributes of the observation task and satellite are encoded using three convolutional networks in the encoder and decoder. Second, an additive attention mechanism is employed to calculate the probability of each observation task being selected. Third, the model is trained with the REINFORCE with rollout baseline algorithm. Experimental results in various scenarios show that both training with the REINFORCE with rollout baseline algorithm and encoding dynamic attributes of the satellite as part of the step context vector are effective. The proposed POMP can maintain the gap with the ant colony optimization (ACO) algorithm in weight degree of timeout (WDT) and total timeout within 0.7% and 1.5 h, respectively, while having a tens of seconds advantage over ACO in terms of computational time.

Multi-satellite scheduling problem with marginal decreasing imaging duration: An improved adaptive ant colony algorithm

With the development of satellite technology, agile Earth Observation Satellites (AEOS) can achieve variable duration imaging. The correlation of decreasing marginal gain during observation between the observation profit and the observation duration has been found. For the first time, we apply the diminishing marginal return function between the observation time and the observation profit obtained by observation tasks, and we study the scheduling problem of multiple observations with variable duration. We propose an improved adaptive ant colony algorithm (IAACO) to solve the problem. Finally, we prove the problems can also obtain greater profits, and the algorithm is better than ACO and A-ALNS in terms of convergent profits by computational experiments regarding the parameters of the actual AEOS.

A Real-Coding Population-Based Incremental Learning Evolutionary Algorithm for Multi-Satellite Scheduling

With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and efficiency of information acquisition. In this paper, focusing on the problem of centralized multi-satellite scheduling, we establish a mathematical model of satellite scheduling with complex constraints of load and platform operation. We also propose a real-coding Population Incremental Based Learning (PBIL) algorithm to solve the multi-satellite scheduling problem. The real-coding format can greatly shorten the coding length compared to the traditional PBIL algorithm with binary coding so that the computational efficiency is improved. Additionally, we design a value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. Finally, we take some numerical examples to verify the real-coding PBIL algorithm for multi-satellite scheduling. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is also analyzed.

Multi-objective optimization for multi-satellite scheduling task

The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is superior to its peers from other methods.

HiPGen: An approach for fast generation of multi-satellite observation plans via a hierarchical multi-channel transformer network

Effective and fast Earth Observation Satellite (EOS) scheduling plays an essential role in new generation space-based information services, especially in emergency scenarios. Most existing research regards the EOS scheduling as a combinatorial optimization problem and adopts metaheuristic local search-based methods to solve it in a batch-wise fashion. To the best of our knowledge, no practical scheduling approach has been able to generate an optimized multi-EOS observation plan in an immediate response style. This paper proposes a novel real-time multi-satellite scheduling method consisting of a machine learning-based hierarchical prediction model and a heuristic local search algorithm. Firstly, the hierarchical prediction model based on a stacked multi-channel transformer network can learn from existing historical multi-EOS observation plans and generate a high-quality initial solution for the current scheduling scenario. Then, the local search algorithm based on Random Hill Climbing does further improvement and heuristic constraint handling on the initial solution and generates the final solution. To verify the effectiveness of the proposed method, simulation experiments are carried out. The results show that the method proposed in this paper consumes a short calculation time and has high planning profits compared with the state-of-the-art approaches.

Multi-satellite scheduling problem for regional targets with uneven income

Multi-satellite scheduling problem for regional targets with uneven income

Multi-satellite scheduling framework and algorithm for very large area observation

This paper presents a multi-satellite scheduling problem for very large area observation with given specific constraints, derived from satellite capacity and customer requests. It is assumed that the profit was proportional to the coverage of acquired area, and the objective is therefore to maximize the total profits of generated observation schedule. To address the satellite scheduling problem, we first demonstrate a detailed problem description and then transform the problem into set covering problem within several criteria and constraints. Based on that, a mathematical model is established. In order to solve the multi-satellite scheduling problem for large area observation, a new solving framework is proposed. The framework is composed of three phases. In the discretizing phase, an area discretization method is adopted to establish the evaluation system. In the target decomposing phase, area target is decomposed into strips and corresponding visible time windows are calculated. In the scheduling phase, with crossover, mutation and feasibility operators, a genetic algorithm is introduced to generate an optimal observation schedule. Through extensive computational experiments on realistically generated problem with Chinese satellite platform, the effectiveness and reliability of the proposed solving framework are verified.

QUEST – A new quadratic decision model for the multi-satellite scheduling problem

Demand for earth observation satellite imagery is pervasive and increasing rapidly across multiple domains, highlighting the key problem to optimally schedule satellite image acquisition/collection, subject to ground and on-board constraints. Despite the variety of reported approaches to solving the NP-hard multi-satellite scheduling problem (m-SatSP), serious limitations still prevail, largely overlooking problem structure exploitation and/or domain knowledge. Assuming a few constraints, problem modelling—or scope—is often confined to a simple trailing satellite constellation composition perspective and tends to conveniently oversimplify footprint coverage intricacies. As a result, adequate problem task decomposition properly reflecting complex kinematic behaviour for an ad hoc satellite constellation remains elusive. Known approaches also fail to provide valuable solution optimality gap estimations to objectively qualify best computed solution and/or to control run-time execution in solving hard problem instances. In this paper, a novel approach to solving the single objective static m-SatSP is proposed. It is based on network flow optimization using mathematical programming. Unlike previous methods, QUEST (QUadratically constrainEd program Solver Technology) relies on a sound alternative approximate objective function and, the exploitation of exact problem-solving techniques. Derived from domain knowledge and problem structure considerations, QUEST generalizes problem modelling to successfully handle virtual constellation, avoiding unsuitable utilization of traditional area coverage decomposition scheme. The proposed decision model concurrently captures coverage approximation, imaging success uncertainty and quality for a variety of tasks. It also includes new and optional constraints while embracing an acceptable upper bound on collection value. A QUEST variant alternatively relying on “delayed reward” to bridge promising search regions on move selection, further shows optimality gap reduction and provides additional speedup. Computational results prove QUEST to be cost-effective and to outperform some recent baseline methods derived from best-known m-SatSP procedures. It comparatively demonstrates measurable collection and run-time gains, and provides a tight upper bound on the optimal solution of hard problems.

A new hybrid genetic algorithm for the collection scheduling problem for a satellite constellation

Many heuristics and meta-heuristics problem-solving methods have been proposed so far to solve the NP-hard multi-satellite collection scheduling problem (m-SatCSP). In particular, genetic algorithms (GAs), well-suited for large scale problems, its simplicity and low cost implementation have been pervasive. However, most contributions largely emphasise simple variant or basic GA principles promotion, overlooking prior problem structure exploitation or potential problem-solving benefits that may be conveyed from similar combinatorial optimisation problems such as the vehicle routing problem with time windows (VRPTW). In fact, despite some recognised similarity with VRPTW and early investigation on limited exact methods, few efforts have been successfully reported to adapt efficient advanced special-purpose problem-solving techniques to m-SatCSP. In this paper, a VRPTW-based hybrid genetic algorithm is proposed to tackle the single objective static m-SatCSP. The advocated approach combines and adapts well-known routing heuristics knowledge with standard genetic operator principles to efficiently explore promising search regions, manage constraint handling and improve solution quality. The hybrid strategy co-evolves two populations of solution plan individuals, maximising expected collection value while concurrently densifying collection paths to minimise orbit demand. Computational results show the approach to be cost-effective and competitive in comparison to some recent methods inspired from the best reported m-SatCSP heuristics.

A mixed integer linear programming model for multi-satellite scheduling

We address the multi-satellite scheduling problem with limited observation capacities that arises from the need to observe a set of targets on the Earth’s surface using imaging resources installed on a set of satellites. We define and analyze the conflict indicators of all available visible time windows of missions, as well as the feasible time intervals of resources. The problem is then formulated as a mixed integer linear programming model, in which constraints are derived from a careful analysis of the interdependency between feasible time intervals that are eligible for observations. We apply the proposed model to several different problem instances that reflect real-world situations. The computational results verify that our approach is effective for obtaining optimum solutions or solutions with a very good quality.

Priority-based and conflict-avoidance heuristics for multi-satellite scheduling

In this paper we address the problem of multi-satellite scheduling with limited observing ability. As with other computationally hard combinatorial optimization problems, a two-stage heuristic method is developed to obtain high quality solutions in a reasonable amount of computation time. The first stage involves the determination of an observing sequence and the generation of a feasible scheduling scheme. We propose several priority-based and conflict-avoidance heuristic strategies and develop the time-based greedy approaches, the weight-based greedy approaches, and an improved differential evolution (DE) algorithm. The second stage consists of further improvement strategies under different resource contentions, thus improving the scheduling results further. Finally, we design different classes of instances to test the efficiency and applicability of the methods. Computational results reveal that the new proposed methods routinely delivered very close to optimal solutions.

Mission Track Coordination Based Multi-Objective Optimization for Multi-Satellite Imaging Scheduling

Satellite imaging scheduling is a complicated task been developed to ideally satisfy all requests under various constraints. A considerable amount of theoretical work has been carried out on single- or multi-satellite scheduling problems based on single-objective optimization. On the other hand, concerning multi-objective optimization in a multi-satellite scheduling scenario, few theoretical analyses have been performed. In this paper, we propose a novel imaging scheduling algorithm of multi-objective optimization for multi-satellite (MOO-MS). The concept of orbit coordination is introduced and the geometry analysis is incorporated to obtain the Pareto front as a set of trade-off solutions. Moreover, a truncated Cauchy probability distribution function (TCPDF) search algorithm is developed to efficiently obtain a sequence of tasking areas with imaging time. Simulation results show that the proposed algorithm avoids large amount of calculation and provides high quality solution within a short period of time by comparing with the single- or multi-satellite optimization scheme.