504 research outputs found
Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions
Agile satellites with advanced attitude maneuvering capability are the new
generation of Earth observation satellites (EOSs). The continuous improvement
in satellite technology and decrease in launch cost have boosted the
development of agile EOSs (AEOSs). To efficiently employ the increasing
orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the
entire observation profit while satisfying all complex operational constraints,
has received much attention over the past 20 years. The objectives of this
paper are thus to summarize current research on AEOSSP, identify main
accomplishments and highlight potential future research directions. To this
end, general definitions of AEOSSP with operational constraints are described
initially, followed by its three typical variations including different
definitions of observation profit, multi-objective function and autonomous
model. A detailed literature review from 1997 up to 2019 is then presented in
line with four different solution methods, i.e., exact method, heuristic,
metaheuristic and machine learning. Finally, we discuss a number of topics
worth pursuing in the future
Multiple agile Earth observation satellites, oversubscribed targets scheduling using complex networks theory
The Earth observation satellites (EOSs) scheduling is of great importance to
achieve efficient observation missions. The agile EOSs (AEOS) with stronger
attitude maneuvering capacity can greatly improve observation efficiency while
increasing scheduling complexity. The multiple AEOSs, oversubscribed targets
scheduling problem with multiple observations are addressed, and the potential
observation missions are modeled as nodes in the complex networks. To solve the
problem, an improved feedback structured heuristic is designed by defining the
node and target importance factors. On the basis of a real world Chinese AEOS
constellation, simulation experiments are conducted to validate the heuristic
efficiency in comparison with a constructive algorithm and a structured genetic
algorithm
Task Scheduling of Multiple Agile Satellites with Transition Time and Stereo Imaging Constraints
This paper proposes a framework for scheduling the observation and download
tasks of multiple agile satellites with practical considerations such as
attitude transition time, onboard data capacity, and stereoscopic image
acquisition. A mixed integer linear programming (MILP) formulation for optimal
scheduling that can address these practical considerations is introduced. A
heuristic algorithm to obtain a near-optimal solution of the formulated MILP
based on the time windows pruning procedure is proposed. A comprehensive case
study demonstrating the validity of the proposed formulation and heuristic is
presented
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
An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy
Efficient scheduling is of great significance to rationally make use of
scarce satellite resources. Task clustering has been demonstrated to realize an
effective strategy to improve the efficiency of satellite scheduling. However,
the previous task clustering strategy is static. That is, it is integrated into
the scheduling in a two-phase manner rather than in a dynamic fashion, without
expressing its full potential in improving the satellite scheduling
performance. In this study, we present an adaptive Simulated Annealing based
scheduling algorithm aggregated with a dynamic task clustering strategy (or
ASA-DTC for short) for satellite observation scheduling problems (SOSPs).
First, we develop a formal model for the scheduling of Earth observing
satellites. Second, we analyze the related constraints involved in the
observation task clustering process. Thirdly, we detail an implementation of
the dynamic task clustering strategy and the adaptive Simulated Annealing
algorithm. The adaptive Simulated Annealing algorithm is efficient, with the
endowment of some sophisticated mechanisms, i.e. adaptive temperature control,
tabu-list based revisiting avoidance mechanism, and intelligent combination of
neighborhood structures. Finally, we report on experimental simulation studies
to demonstrate the competitive performance of ASA-DTC. Moreover, we show that
ASA-DTC is especially effective when SOSPs contain a large number of targets or
these targets are densely distributed in a certain area.Comment: 23 pages, 5 figures, 4 table
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
Earth observation resources are becoming increasingly indispensable in
disaster relief, damage assessment and related domains. Many unpredicted
factors, such as the change of observation task requirements, to the occurring
of bad weather and resource failures, may cause the scheduled observation
scheme to become infeasible. Therefore, it is crucial to be able to promptly
and maybe frequently develop high-quality replanned observation schemes that
minimize the effects on the scheduled tasks. A bottom-up distributed
coordinated framework together with an improved contract net are proposed to
facilitate the dynamic task replanning for heterogeneous Earth observation
resources. This hierarchical framework consists of three levels, namely,
neighboring resource coordination, single planning center coordination, and
multiple planning center coordination. Observation tasks affected by
unpredicted factors are assigned and treated along with a bottom-up route from
resources to planning centers. This bottom-up distributed coordinated framework
transfers part of the computing load to various nodes of the observation
systems to allocate tasks more efficiently and robustly. To support the prompt
assignment of large-scale tasks to proper Earth observation resources in
dynamic environments, we propose a multiround combinatorial allocation (MCA)
method. Moreover, a new float interval-based local search algorithm is proposed
to obtain the promising planning scheme more quickly. The experiments
demonstrate that the MCA method can achieve a better task completion rate for
large-scale tasks with satisfactory time efficiency. It also demonstrates that
this method can help to efficiently obtain replanning schemes based on original
scheme in dynamic environments.Comment: 14 pages, 11 figures.This work has been submitted to the IEEE for
possible publicatio
Scheduling of space to ground quantum key distribution
Satellite-based platforms are currently the only feasible way of achieving intercontinental range for quantum communication, enabling thus the future global quantum internet. Recent demonstrations by the Chinese spacecraft Micius have spurred an international space race and enormous interest in the development of both scientific and commercial systems. Research efforts so far have concentrated upon in-orbit demonstrations involving a single satellite and one or two ground stations. Ultimately satellite quantum key distribution should enable secure network communication between multiple nodes, which requires efficient scheduling of communication with the set of ground stations. Here we present a study of how satellite quantum key distribution can service many ground stations taking into account realistic constraints such as geography, operational hours, and most importantly, weather conditions. The objective is to maximise the number of keys a set of ground stations located in the United Kingdom could share while simultaneously reflecting the communication needs of each node and its relevance in the network. The problem is formulated as a mixed-integer linear optimisation program and solved to a desired optimality gap using a state of the art solver. The approach is presented using a simulation run throughout six years to investigate the total number of keys that can be sent to ground stations
Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft
This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) small-spacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationally-tractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit
Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft
This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) small-spacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationally-tractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit
Routing and scheduling optimisation under uncertainty for engineering applications
The thesis aims to develop a viable computational approach suitable for solving large vehicle routing and scheduling optimisation problems affected by uncertainty. The modelling framework is built upon recent advances in Stochastic Optimisation, Robust Optimisation and Distributionally Robust Optimization. The utility of the methodology is presented on two classes of discrete optimisation problems: scheduling satellite communication, which is a variant of Machine Scheduling, and the Vehicle Routing Problem with Time Windows and Synchronised Visits. For each problem class, a practical engineering application is formulated using data coming from the real world. The significant size of the problem instances reinforced the need to apply a different computational approach for each problem class. Satellite communication is scheduled using a Mixed-Integer Programming solver. In contrast, the vehicle routing problem with synchronised visits is solved using a hybrid method that combines Iterated Local Search, Constraint Programming and the Guided Local Search metaheuristic.
The featured application of scheduling satellite communication is the Satellite Quantum Key Distribution for a system that consists of one spacecraft placed in the Lower Earth Orbit and a network of optical ground stations located in the United Kingdom. The satellite generates cryptographic keys and transmits them to individual ground stations. Each ground station should receive the number of keys in proportion to the importance of the ground station in the network. As clouds containing water attenuate the signal, reliable scheduling needs to account for cloud cover predictions, which are naturally affected by uncertainty. A new uncertainty sets tailored for modelling uncertainty in predictions of atmospheric phenomena is the main contribution to the methodology. The uncertainty set models the evolution of uncertain parameters using a Multivariate Vector Auto-Regressive Time Series, which preserves correlations over time and space. The problem formulation employing the new uncertainty set compares favourably to a suite of alternative models adapted from the literature considering both the computational time and the cost-effectiveness of the schedule evaluated in the cloud cover conditions observed in the real world. The other contribution of the thesis in the satellite scheduling domain is the formulation of the Satellite Quantum Key Distribution problem. The proof of computational complexity and thorough performance analysis of an example Satellite Quantum Key Distribution system accompany the formulation.
The Home Care Scheduling and Routing Problem, which instances are solved for the largest provider of such services in Scotland, is the application of the Vehicle Routing Problem with Time Windows and Synchronised Visits. The problem instances contain over 500 visits. Around 20% of them require two carers simultaneously. Such problem instances are well beyond the scalability limitations of the exact method and considerably larger than instances of similar problems considered in the literature. The optimisation approach proposed in the thesis found effective solutions in attractive computational time (i.e., less than 30 minutes) and the solutions reduced the total travel time threefold compared to alternative schedules computed by human planners. The Essential Riskiness Index Optimisation was incorporated into the Constraint Programming model to address uncertainty in visits' duration. Besides solving large problem instances from the real world, the solution method reproduced the majority of the best results reported in the literature and strictly improved the solutions for several instances of a well-known benchmark for the Vehicle Routing Problem with Time Windows and Synchronised Visits.The thesis aims to develop a viable computational approach suitable for solving large vehicle routing and scheduling optimisation problems affected by uncertainty. The modelling framework is built upon recent advances in Stochastic Optimisation, Robust Optimisation and Distributionally Robust Optimization. The utility of the methodology is presented on two classes of discrete optimisation problems: scheduling satellite communication, which is a variant of Machine Scheduling, and the Vehicle Routing Problem with Time Windows and Synchronised Visits. For each problem class, a practical engineering application is formulated using data coming from the real world. The significant size of the problem instances reinforced the need to apply a different computational approach for each problem class. Satellite communication is scheduled using a Mixed-Integer Programming solver. In contrast, the vehicle routing problem with synchronised visits is solved using a hybrid method that combines Iterated Local Search, Constraint Programming and the Guided Local Search metaheuristic.
The featured application of scheduling satellite communication is the Satellite Quantum Key Distribution for a system that consists of one spacecraft placed in the Lower Earth Orbit and a network of optical ground stations located in the United Kingdom. The satellite generates cryptographic keys and transmits them to individual ground stations. Each ground station should receive the number of keys in proportion to the importance of the ground station in the network. As clouds containing water attenuate the signal, reliable scheduling needs to account for cloud cover predictions, which are naturally affected by uncertainty. A new uncertainty sets tailored for modelling uncertainty in predictions of atmospheric phenomena is the main contribution to the methodology. The uncertainty set models the evolution of uncertain parameters using a Multivariate Vector Auto-Regressive Time Series, which preserves correlations over time and space. The problem formulation employing the new uncertainty set compares favourably to a suite of alternative models adapted from the literature considering both the computational time and the cost-effectiveness of the schedule evaluated in the cloud cover conditions observed in the real world. The other contribution of the thesis in the satellite scheduling domain is the formulation of the Satellite Quantum Key Distribution problem. The proof of computational complexity and thorough performance analysis of an example Satellite Quantum Key Distribution system accompany the formulation.
The Home Care Scheduling and Routing Problem, which instances are solved for the largest provider of such services in Scotland, is the application of the Vehicle Routing Problem with Time Windows and Synchronised Visits. The problem instances contain over 500 visits. Around 20% of them require two carers simultaneously. Such problem instances are well beyond the scalability limitations of the exact method and considerably larger than instances of similar problems considered in the literature. The optimisation approach proposed in the thesis found effective solutions in attractive computational time (i.e., less than 30 minutes) and the solutions reduced the total travel time threefold compared to alternative schedules computed by human planners. The Essential Riskiness Index Optimisation was incorporated into the Constraint Programming model to address uncertainty in visits' duration. Besides solving large problem instances from the real world, the solution method reproduced the majority of the best results reported in the literature and strictly improved the solutions for several instances of a well-known benchmark for the Vehicle Routing Problem with Time Windows and Synchronised Visits
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