Autonomous satellite constellation for enhanced Earth coverage using coupled selection equations

This paper presents a novel solution to the problem of autonomous task allocation for a self-organising constellation of small satellites in Earth orbit. The method allows the constellation members to plan manoeuvres to cluster themselves above particular target longitudes on the Earth’s surface. This is enabled through the use of Coupled Selection Equations, which represent a dynamical systems approach to combinatorial optimisation problems, and whose solution tends towards a Boolean matrix which describes pairings of the satellites and targets which solves the relevant assignment problems. Satellite manoeuvres are actuated using a simple control law which incorporates the results of the Coupled Selection Equations. Three demonstrations of the efficacy of the method are given in order of increasing complexity - first with an equal number of satellites and targets, then with a surplus of satellites, including agent failure events, and finally with a constellation of two different satellite types. The method is shown to provide efficient solutions, whilst being computationally non-intensive, quick to converge and robust to satellite failures. Proposals to extend the method for on-board processing on a distributed architecture are discussed

Dynamic Resource Allocation

Computer systems are subject to continuously increasing performance demands. However, energy consumption has become a critical issue, both for high-end large-scale parallel systems [12], as well as for portable devices [34]. In other words, more work needs to be done in less time, preferably with the same or smaller energy budget. Future performance and efficiency goals of computer systems can only be reached with large-scale, heterogeneous architectures [6]. Due to their distributed nature, control software is required to coordinate the parallel execution of applications on such platforms. Abstraction, arbitration and multi-objective optimization are only a subset of the tasks this software has to fulfill [6, 31]. The essential problem in all this is the allocation of platform resources to satisfy the needs of an application.\ud \ud This work considers the dynamic resource allocation problem, also known as the run-time mapping problem. This problem consists of task assignment to (processing) elements and communication routing through the interconnect between the elements. In mathematical terms, the combined problem is defined as the multi-resource quadratic assignment and routing problem (MRQARP). An integer linear programming formulation is provided, as well as complexity proofs on the N P-hardness of the problem.\ud \ud This work builds upon state-of-the-art work of Yagiura et al. [39, 40, 42] on metaheuristics for various generalizations of the generalized assignment problem. Specifically, we focus on the guided local search (GLS) approach for the multi-resource quadratic assignment problem (MRQAP). The quadratic assignment problem defines a cost relation between tasks and between elements. We generalize the multi-resource quadratic assignment problem with the addition of a capacitated interconnect and a communication topology between tasks. Numerical experiments show that the performance of the approach is comparable with commercial solvers. The footprint, the time versus quality trade-off and available metadata make guided local search a suitable candidate for run-time mapping

A Rapid Heuristic for Scheduling Non-Preemptive Dependent Periodic Tasks onto Multiprocessor

International audienceWe address distributed real-time applications represented by systems of non-preemptive dependent periodic tasks. This system is described by an acyclic directed graph. Because the distribution and the scheduling of these tasks onto a multiprocessor is an NP-hard problem we propose a greedy heuristic to solve it. Our heuristic sequences three algorithms: assignment, unrolling, and scheduling. The tasks of the same, or multiple, periods are assigned to the same processor according to a mixed sort. Then, the initial graph of tasks is unrolled, i.e. each task is repeated according to the ratio between its period and the least common multiple of all periods of tasks. Finally, the tasks of the unrolled graph are distributed and scheduled onto the processors where they have been assigned. Then, we give the complexity of this heuristic, and we illustrate it with an example. A performance analysis comparing our heuristic with an optimal Branch and Cut algorithm concludes that our heuristic is effective in terms of scheduling success ratio and speed

Random Neural Networks and Optimisation

In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain