Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots

Towards Mitigating Co-incident Peak Power Consumption and Managing Energy Utilization in Heterogeneous Clusters

As data centers continue to grow in scale, the resource management software needs to work closely with the hardware infrastructure to provide high utilization, performance, fault tolerance, and high availability. Apache Mesos has emerged as a leader in this space, providing an abstraction over the entire cluster, data center, or cloud to present a uniform view of all the resources. In addition, frameworks built on Mesos such as Apache Aurora, developed within Twitter and later contributed to the Apache Software Foundation, allow massive job submissions with heterogeneous resource requirements. The availability of such tools in the Open Source space, with proven record of large-scale production use, make them suitable for research on how they can be adapted for use in campus-clusters and emerging cloud infrastructures for different workloads in both academia and industry. As data centers run these workloads and strive to maintain high utilization of their components, they suffer a significant cost in terms of energy and power consumption. To address this cost we have developed our own framework, Electron, for use with Mesos. Electron is designed to be configurable with heuristic-driven power capping policies along with different scheduling policies such as Bin Packing and First Fit. We characterize the performance of Electron, in comparison with the widely used Aurora framework. On average, our experiments show that Electron can reduce the 95th percentile of CPU and DRAM power usage by 27.89%, total energy consumption by 19.15%, average power consumption by 27.90%, and max peak power usage by 16.91%, while maintaining a similar makespan when compared to Aurora using the proper combination of power capping and scheduling policies

Proceedings of the 8th Cologne-Twente Workshop on Graphs and Combinatorial Optimization

International audienceThe Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a series of workshops organized bi-annually by either Köln University or Twente University. As its importance grew over time, it re-centered its geographical focus by including northern Italy (CTW04 in Menaggio, on the lake Como and CTW08 in Gargnano, on the Garda lake). This year, CTW (in its eighth edition) will be staged in France for the first time: more precisely in the heart of Paris, at the Conservatoire National d’Arts et Métiers (CNAM), between 2nd and 4th June 2009, by a mixed organizing committee with members from LIX, Ecole Polytechnique and CEDRIC, CNAM

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

Power system performance improvement in the presence of renewable sources

Electromechanical oscillations is a phenomenon in which a generator oscillates against other generators in the power system, the damping of these oscillations has therefore become a priority objective, The objective of our work is to ensure maximum damping of low frequency oscillations and to guarantee the overall stability of the system for different operating points by the use of power stabilizers (PSSs). To achieve this goal, we developed an improved metaheuristic optimization method based on the crows search algorithm (CSA) applied on an objective function extracted from the eigenvalue analysis of the power system. A comparative study was made, with a classic stabilizer, genetic algorithm-based PSS (GA-PSS), a particle-swarm-based PSS (PSO-PSS) and other stabilizers based on recent algorithms. The performances of these optimization methods were evaluated on a single machine connected to an infinite bus (SMIB) via a linear model time domain simulation. On the other hand, the effect of integrating a photovoltaic PV generator on the stability of the power system is presented, as well as solutions to increase the amount of integration of the PV generator without losing the stability of the system

Scalable and Distributed Resource Management for Many-Core Systems

Many-core systems provide researchers with important new challenges, including the handling of very dynamic and hardly predictable computational loads. The large number of applications and cores causes scalability issues for centrally acting heuristics, which always must retain a global view of the entire system. Resource management itself can become a bottleneck which limits the achievable performance of the system. The focus of this work is to achieve scalability of resource management

Potential uptake and impact of new crop spraying technology on U.K. arable farms

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Train Scheduling in Public Rail Transport

This thesis deals with train scheduling problems with an emphasis on public rail transport. In particular, we assume a periodic schedule and a fixed railroad track network, which is common for public rail transport. A train schedule consists of arrival and departure times for the lines at certain points of the traffic network, e.g. railroad stations. The minimization of operational cost for the realization of a schedule forms a central part of this thesis. We introduce a mixed integer linear programming model for this objective. A direct solution of instances of real-world size is not possible with today's hard- and software. With the help of a decomposition idea, we are able to find solutions of acceptable quality for those instances in a reasonable amount of time. Therefore, we split the instance into an optimization component and a feasibility component. Both subproblems are integrated into a branch-and-bound algorithm. With these methods, we can produce solutions of practically sufficient quality in a few minutes. We present computational results for networks of the Netherlands and Germany.Die vorliegende Arbeit befasst sich mit Fahrplanoptimierung unter Beruecksichtigung der Verhaeltnisse beim spurgefuehrten, oeffentlichen Personenverkehr. Insbesondere wird davon ausgegangen, dass der Fahrplan sich nach einer bestimmten Zeitperiode (z.B. eine Stunde) wiederholen soll. Ein Fahrplan besteht aus den Ankunfts- und Abfahrtzeiten der einzelnen Verkehrslinien an bestimmten Punkten im Verkehrsnetz, etwa den Bahnhoefen beim Eisenbahn-Fernverkehr. Im Mittelpunkt dieser Arbeit steht die Minimierung der durch einen Fahrplan entstehenden Betriebskosten fuer die Fahrzeuge. Hierzu wird ein Modell entwickelt, das auf einem gemischt-ganzzahligen linearen Programm basiert. Eine direkte Loesung von Instanzen fuer praktisch relevante Problemgroessen ist mit der zur Zeit zur Verfuegung stehenden Hard- und Software nicht moeglich. In der Arbeit wird ein Dekompositionsansatz vorgestellt, mit dem auch aus Praxissicht interessante Problemgroessen bearbeitet werden koennen. Hierzu wird das gemischt-ganzzahlige Programm in eine Optimierungs- und eine Zulaessigkeitskomponente zerlegt. Beide Teilprobleme werden in ein Branch-and-Bound-Verfahren integriert, das innerhalb weniger Minuten auch fuer die oben genannten Problemgroessen gute Ergebnisse liefert. Es werden Rechenergebnisse fuer Netzwerke aus den Niederlanden und aus Deutschland vorgestellt