Agile Earth Observation Satellite Research Articles

Optimal Agile Satellite Target Scheduling with Learned Dynamics

Target sequencing is an important aspect of agile Earth-observing satellite scheduling. The objective of the problem is to find a feasible imaging sequence that maximizes the cumulative value of unique heterogeneously valued requests; no expendable resource constraints (power and data storage) are considered. Two challenges are encountered: transition times between targets are dictated by dynamics of an arbitrary controller, and the solution space is combinatorial with respect to request count. To find dynamically accurate time-dependent attitude maneuver transition times, a neural network is trained on simulated slew data; this allows for the generation of more physically accurate, and thus better performing, schedules than when using the traditional approach of lower-order models for slew feasibility. Next, slews between requests are represented by a sparsified graph, and a mixed-integer linear program is formulated to solve them; the sparse formulation keeps the problem tractable over longer planning horizons and larger numbers of requests when compared to the standard approach. The solutions are optimal up to the quality of the transition time estimator and a time discretization. Finally, the solutions are verified in a high-fidelity simulation, demonstrating validity when deployed on a lifelike system. Time-dependent request values and multiple satellites are also considered.

Three multi-objective memetic algorithms for observation scheduling problem of active-imaging agile earth observation satellites

Three multi-objective memetic algorithms for observation scheduling problem of active-imaging agile earth observation satellites

Knowledge-Guided Parallel Hybrid Local Search Algorithm for Solving Time-Dependent Agile Satellite Scheduling Problems

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes.

Agile earth observation satellite constellations scheduling for large area target imaging using heuristic search

Demand for imaging of large area targets by space-based assets such as constellations of Earth Observation Satellites (EOS) is on the rise for different applications. The area targets coverage cannot be achieved by a single satellite in a single observation opportunity, as the region for imaging spreads wider than the coverage swaths of imaging sensors. Hence, the imaging satellites are scheduled in a cooperative way to achieve maximum coverage of the area in a given planning time horizon. The scheduling algorithms also aim to maximize or minimize some specific objectives of interest while simultaneously complying with all resource constraints for an efficient use of precious space resources. As the current generation satellites are highly agile, there are many ways of choosing an imaging location in the target area for each single observation opportunity of a satellite. Even though the possible imaging region in continuous space is discretized into a finite number of strips for each observation, the huge combinatorial search space formed with all satellites’ imaging opportunities makes the problem intractable. Thus, the area target imaging scheduling is a complex combinatorial NP-hard optimization problem. Many exact and heuristic methods were evolved to provide a solution to this scheduling problem. Even though the exact methods solve the problem to optimality, they are limited to smaller problem instances because of the high computational complexities involved in those methods. More focus is seen in the literature on heuristic approaches as they guarantee scheduling performance with feasible solutions within an acceptable computational complexity. This paper presents our efficient heuristic approach proposed for the scheduling problem for imaging a large area target using a constellation of multiple satellites. Our approach is an extension of a widely used greedy approach in combination with insights from dynamic programming. In this paper, first we addressed the scheduling problem by describing it in detail and formulating it into a non-linear integer programming problem, considering the multi-objectives of achieving maximum coverage and minimum imaging resolution. The solution to the problem comprises two phases, namely the area decomposition phase and the scheduling phase. In the area decomposition phase, the area target is divided into strips dynamically for each observation opportunity of a satellite. Then exact observation period of each strip is computed using a simplified semi-analytical computational method. In the scheduling phase, we applied our heuristic search strategy, namely Forward and Backward Heuristic Search (FBHS) to obtain a near optimal solution within an acceptable computational time. Through the extensive simulations conducted under various scenarios, the effectiveness of the proposed method is verified in comparison with the baseline greedy approach.

Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests.

Optimal Mission Planning for Multiple Agile Satellites Using Modified Dynamic Programming

This paper aims to optimize imaging mission planning for multiple agile Earth observation satellites using a modified dynamic programming (MDP) algorithm. The mission planning problem is mathematically modeled using mixed-integer linear programming, which defines the objective function and constraints. An MDP algorithm is proposed to address the challenge of extended computation time inherent in typical deterministic dynamic programming, aiming to optimize the imaging scheduling problem. Numerical simulations are conducted under various target numbers and satellites in a relatively narrow mission area with a high target density. The simulation results illustrate the stage configuration and target observation sequence over time, and this paper investigates the achievement rate, measured as the ratio of obtained profit and target observations to the total. The trend of computation time is also analyzed across all mission scenarios. Ultimately, the validity of the MDP algorithm is confirmed, as it demonstrates superior performance compared to the genetic algorithm, the branch-and-bound method, and the greedy algorithm.

Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem

The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention network-based decision neural network (GDNN) to solve the AEOSSP. Specifically, we first represent the task and time-dependent attitude transition constraints by a graph. We then describe the problem as a Markov decision process and perform feature engineering. On this basis, we design a GDNN to guide the construction of the solution sequence and train it with proximal policy optimization (PPO). Experimental results show that the proposed method outperforms construction heuristics at scheduling profit by at least 45%. The proposed method can also calculate the approximate profits of the state-of-the-art method with an error of less than 7% and reduce scheduling time markedly. Finally, we demonstrate the scalability of the proposed method.

Simulated Annealing-Based Heuristic for Multiple Agile Satellites Scheduling Under Cloud Coverage Uncertainty

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments cannot see through the cloud, the cloud coverage brings a significant influence on the satellite observation missions. The scheduling of multiple agile EOSs (AEOSs) is already complicated due to strong satellite maneuverability and onboard satellite energy constraints. Moreover, introducing cloud coverage uncertainty further increases scheduling complexity. Motivated by these challenges, we address the multiple AEOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. A chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via a sample approximation method. To overcome the solving difficulty, an improved simulated annealing (ISA)-based heuristic including a fast insertion strategy is proposed for large-scale observation missions. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method. Experimental results show that the ISA-based heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which validates the proposed algorithm.

Frequent pattern-based parallel search approach for time-dependent agile earth observation satellite scheduling

To address the time-dependent agile earth observation satellite (AEOS) scheduling problem more effectively, the frequent pattern-based parallel search (FPBPS) algorithm is proposed, which is composed of a parallel local search procedure, a competition-based algorithm and operator adaptive selection procedure and the new solutions construction method based on frequent pattern. First, the algorithm and the operator are selected from the algorithm pool and operator pool and run in parallel. As a result, some high-quality and diverse solutions are obtained in a short time. Second, we update the probability of each algorithm and operator to strengthen the self-adaptive ability of the algorithm. Third, frequent pattern mining method is used to extract knowledge with respect to AEOS scheduling to construct new solutions, and parallel local search is applied to further improve these solutions. Finally, extensive experiments prove that the FPBPS algorithm has a better performance than other comparison algorithms in the quality of solution, computation time, and robustness.

A Distributed Approach for Time-Dependent Observation Scheduling Problem in the Agile Earth Observation Satellite Constellation

The increasing number of agile earth observation satellites (AEOSs) in orbit have advanced maneuverable capabilities, enabling the AEOS constellation to provide richer observation services. Therefore, observation scheduling in the AEOS constellation is crucial for improving the performance of satellite remote sensing systems. This paper focuses on the problem of distributed observation scheduling in the AEOS constellation, where a period of transition time is required between two consecutive observations, and this constraint depends on the start time of observations. We define a new fitness function that not only maximizes the profit sum but also considers system load balancing. Based on the fundamental idea of a distributed performance impact (PI) algorithm, we develop a PI-based distributed scheduling method (PIDSM) that runs concurrently on all AEOSs via local inter-satellite link (ISL)-based communications. The PIDSM iterates between two phases: target inclusion and consensus and target removal. The first phase aims to select the optimal task for each AEOS, while the second phase reaches a consensus over all AEOSs and removes targets that may decrease overall fitness. Experimental results demonstrate that the PIDSM can schedule more targets, reduce communication overhead, and achieve higher fitness values than existing algorithms. Sensitivity analyses further validate the effectiveness of the PIDSM.

Reasoning-Based Scheduling Method for Agile Earth Observation Satellite with Multi-Subsystem Coupling

With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints. First, we defined the AEOS mission scheduling model with multi-subsystem constraints. Second, we put forward a state variable prediction method that reflects the different coupling states of a satellite after analyzing the coupling relationships between various subsystems and identifying the primary limiting coupling states for each subsystem. Third, we established the reasoning rules corresponding to the planning strategies of different coupling states of the satellite by adding two planning strategies based on the planning strategies of existing planning methods. By comparing the proposed method to three heuristic scheduling methods and a meta-heuristic scheduling method, the results show that our method has better performance in terms of scheduling results and efficiency.

Multi-strip observation scheduling problem for active-imaging agile earth observation satellites

Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active imaging, AI-AEOS improves upon the observation capabilities of AEOS and provides additional ways to observe ground targets. This however makes the observation scheduling problem for these agile earth observation satellite more complex, especially when considering multi-strip ground targets. In this paper, we investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite. A bi-objective optimization model is presented for MSOP along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a non-dominated sorting genetic algorithm II (NSGA-II). The results of extensive computational experiments are presented which disclose that ALNS and NSGA-II when worked in unison produced superior outcomes. Our model is more versatile than existing models and provides enhanced capabilities in applied problem solving.

Reinforcement Learning for the Agile Earth-Observing Satellite Scheduling Problem

This work explores reinforcement learning (RL) for on-board planning and scheduling of an agile Earth-observing satellite (AEOS). In this formulation of the AEOS scheduling problem, a spacecraft in low-Earth orbit attempts to maximize the weighted sum of targets collected and downlinked. Reinforcement learning is both a class of problems and solution methods that involves learning how to map situations to actions to maximize a reward function through repeated interactions with an environment. Reinforcement learning problems are formulated as Markov decision processes (MDPs), which are formalizations of sequential decision making problems. In this work, the agile EOS Scheduling problem is formulated as a Markov decision process (MDP) where the number of upcoming imaging targets included in the action space is an adjustable parameter to account for clusters of imaging targets with varying priorities. Unlike prior Earth-observing satellite scheduling MDP formulations, this work explores how the size of the action space can be reduced to produce generalized policies that may be executed on board the spacecraft in seconds without sacrificing performance. Monte Carlo tree search (MCTS) and supervised learning are used to train a set of agents with varying numbers of targets in the action space. Monte Carlo tree search is an online search algorithm that was originally developed to solve two player games but has since been applied to solve reinforcement learning problems. Monte Carlo tree search solves reinforcement learning problems by simulating interactions with the environment, building an estimate of the state-action value function to select the next best action. In this work, Monte Carlo tree search is used to generate training data, and supervised learning is applied over the state-action value estimates generated by MCTS to solve for a generalized policy, which is used on-board the spacecraft to map states to actions. Two backup strategies are explored for MCTS - an incremental averaging operator and a maximization operator. For all backup operators, performance asymptotically increases as the number of targets in the action space approaches the maximum number of available targets. A benchmark is computed with Monte Carlo tree search to determine an upper bound on performance. Furthermore, MCTS is compared to solutions generated by a genetic algorithm. For all numbers of imaging targets in the action space, MCTS demonstrates a 2-5% increase in average reward at 10-20% of the single core wall clock time of the genetic algorithm. A search of various neural network hyperparameters is presented, and the trained neural networks are shown to approximate the MCTS policy with three orders of magnitude less execution time. Finally, the trained agents and the genetic algorithm are deployed on varying target densities for comparison purposes and to demonstrate robustness to mission profiles outside of the training distribution.

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.

Joint Observation and Transmission Scheduling in Agile Satellite Networks

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data are offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.

A data-driven improved genetic algorithm for agile earth observation satellite scheduling with time-dependent transition time

The agile earth observation satellite (AEOS) task scheduling problem has been proven to be NP-hard. The traditional meta-heuristics is easy to converge too early or too late, and difficult to ensure the quality of the final solution. To address the AEOS task scheduling problem more effectively, a data-driven improved genetic algorithm (DDIGA) is proposed, which is composed of a traditional genetic algorithm, an artificial neural network(ANN), a frequent pattern-based new solutions construction procedure, and competition-based adaptive local adjustment strategy. In DDIGA, the data from the real-world or the history of the search is used to train the ANN model, and then the initial population is built by the trained ANN model. Next, some high-quality solutions created by selection, crossover, mutation operator are gathered to mine the frequent patterns, and some new solutions are constructed based on the chosen patterns. Finally, the new solutions are further improved by an optimization procedure, and competition-based adaptive local adjustment strategy is worked on these solutions with high similarity. Some scenarios are designed to verify the validity of the proposed approach. Extensive experiments on the satellite instances demonstrate that the DDIGA algorithm outperforms the state-of-the-art algorithms in solution quality and computation time.

A Heuristic Construction Neural Network Method for the Time-Dependent Agile Earth Observation Satellite Scheduling Problem

The agile earth observation satellite scheduling problem (AEOSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficiently utilize the historical scheduling information and task characteristics, this paper introduces a new neural network model based on the deep reinforcement learning and heuristic algorithm (DRL-HA) to the AEOSSP and proposes an innovative non-iterative heuristic algorithm. The DRL-HA is composed of a heuristic construction neural network (HCNN) model and a task arrangement algorithm (TAA), where the HCNN aims to generate the task planning sequence and the TAA generates the final feasible scheduling order of tasks. In this study, the DRL-HA is examined with other heuristic algorithms by a series of experiments. The results demonstrate that the DRL-HA outperforms competitors and HCNN possesses outstanding generalization ability for different scenario sizes and task distributions. Furthermore, HCNN, when used for generating initial solutions of meta-heuristic algorithms, can achieve improved profits and accelerate interactions. Therefore, the DRL-HA algorithm is verified to be an effective method for solving AEOSSP. In this way, the high-profit and high-timeliness of agile satellite scheduling can be guaranteed, and the solution of AEOSSP is further explored and improved.

Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites

Concerning the onboard autonomous mission planning problem of high and low orbiting agile Earth observation satellites (AEOSs), which requires high algorithm timeliness, a deep reinforcement learning (DRL) based algorithm, namely, the multi-satellite mission planning (MSMP) algorithm, is proposed. The algorithm uses neural networks with an ‘encoder-decoder’ structure and designs a mechanism that enables each satellite to select requests in turn. These two designs allow the algorithm to achieve simultaneous scheduling of multiple satellites. After that, the MSMP uses a REINFORCE with Critic Baseline algorithm to optimize its selection strategy. Computational experiments show that the proposed algorithm can reduce the reasoning time by a factor of tens compared to the adaptive task assignment large neighborhood search (A-ALNS) algorithm, while being able to keep the revenue rate difference to A-ALNS below 5%.

Reward Factor-Based Multiple Agile Satellites Scheduling With Energy and Memory Constraints

Earth observing satellites (EOS) orbit around the earth to perform observation tasks specified by users. The additional maneuverability resulting from higher degrees of freedom than nonagile EOS (N-AEOS) provides agile EOS (AEOS) a significantly larger visible time window to complete the tasks. As a consequence, the task scheduling for AEOS is much more computationally complex than N-AEOS. In this article, a mixed-integer nonlinear optimization problem is formulated to find a near-optimal task allocation for a realistic AEOS scheduling problem. The satellite resources, such as energy and memory constraints, are considered in this problem. A reward factor is used to address the requirement of multiple scans in order to complete a task. A probability factor is also taken into consideration to incorporate the uncertainty of successful scans due to external factors, such as cloud coverage. An elitist mixed coded genetic algorithm-based satellite scheduling (EMCGA-SS) algorithm is proposed to solve the formulated problem. EMCGA-SS is extended to elitist mixed coded hybrid genetic algorithm-based satellite scheduling by combining a hill-climber mechanism in order to have better initialization. Experimental results to illustrate the performance of the algorithms and a comparison with some widely used methodologies are also presented.

A competitive Markov decision process model and a recursive reinforcement-learning algorithm for fairness scheduling of agile satellites

With the development of space science and technology, the requirements of stereo imaging tasks are becoming ever more urgent in many space applications, such as 3-D building changes, natural disaster assessment, and target positioning. As the new generation of three degrees freedom(roll, pitch, and yaw) satellites, agile earth observation satellites have a wider variable-roll visible scope and longer variable-pitch visible time for ground targets, thus they are suitable for stereo tasks. However, current task scheduling models and algorithms ignore the imaging quality and fairness of resource sharing by pitch and roll. This paper investigated a new scheduling model and algorithm to realize the satellite fairness scheduling for stereo tasks. First, a novel image quality representation method is proposed to measure the fairness of task observation. Second, a finite Markov decision process model that considers flexible pitch and roll capabilities is designed. Finally, an algorithm based on the reinforcement learning technique with block coding is proposed to solve fairness scheduling problems. We apply the proposed scheduling technique to several different problem instances, the computation results verify that our approach is effective for obtaining optimum solutions and relative fairness solutions.