A Conflict-Resilient Lock-Free Calendar Queue for Scalable Share-Everything PDES Platforms

Emerging share-everything Parallel Discrete Event Simulation (PDES) platforms rely on worker threads fully sharing the workload of events to be processed. These platforms require efficient event pool data structures enabling high concurrency of extraction/insertion operations. Non-blocking event pool algorithms are raising as promising solutions for this problem. However, the classical non-blocking paradigm leads concurrent conflicting operations, acting on a same portion of the event pool data structure, to abort and then retry. In this article we present a conflict-resilient non-blocking calendar queue that enables conflicting dequeue operations, concurrently attempting to extract the minimum element, to survive, thus improving the level of scalability of accesses to the hot portion of the data structure---namely the bucket to which the current locality of the events to be processed is bound. We have integrated our solution within an open source share-everything PDES platform and report the results of an experimental analysis of the proposed concurrent data structure compared to some literature solutions

Position discovery for a system of bouncing robots

International audienceA collection of n anonymous mobile robots is deployed on a unit-perimeter ring or a unit-length line segment. Every robot starts moving at constant speed, and bounces each time it meets any other robot or segment endpoint, changing its walk direction. We study the problem of position discovery, in which the task of each robot is to detect the presence and the initial positions of all other robots. The robots cannot communicate or perceive information about the environment in any way other than by bouncing nor they have control over their walks which are determined by their initial positions and their starting directions. Each robot has a clock allowing it to observe the times of its bounces. We give complete characterizations of all initial configurations for both the ring and the segment in which no position detection algorithm exists and we design optimal position detection algorithms for all feasible configurations

Visual attention and swarm cognition for off-road robots

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2011Esta tese aborda o problema da modelação de atenção visual no contexto de robôs autónomos todo-o-terreno. O objectivo de utilizar mecanismos de atenção visual é o de focar a percepção nos aspectos do ambiente mais relevantes à tarefa do robô. Esta tese mostra que, na detecção de obstáculos e de trilhos, esta capacidade promove robustez e parcimónia computacional. Estas são características chave para a rapidez e eficiência dos robôs todo-o-terreno. Um dos maiores desafios na modelação de atenção visual advém da necessidade de gerir o compromisso velocidade-precisão na presença de variações de contexto ou de tarefa. Esta tese mostra que este compromisso é resolvido se o processo de atenção visual for modelado como um processo auto-organizado, cuja operação é modulada pelo módulo de selecção de acção, responsável pelo controlo do robô. Ao fechar a malha entre o processo de selecção de acção e o de percepção, o último é capaz de operar apenas onde é necessário, antecipando as acções do robô. Para fornecer atenção visual com propriedades auto-organizadas, este trabalho obtém inspiração da Natureza. Concretamente, os mecanismos responsáveis pela capacidade que as formigas guerreiras têm de procurar alimento de forma auto-organizada, são usados como metáfora na resolução da tarefa de procurar, também de forma auto-organizada, obstáculos e trilhos no campo visual do robô. A solução proposta nesta tese é a de colocar vários focos de atenção encoberta a operar como um enxame, através de interacções baseadas em feromona. Este trabalho representa a primeira realização corporizada de cognição de enxame. Este é um novo campo de investigação que procura descobrir os princípios básicos da cognição, inspeccionando as propriedades auto-organizadas da inteligência colectiva exibida pelos insectos sociais. Logo, esta tese contribui para a robótica como disciplina de engenharia e para a robótica como disciplina de modelação, capaz de suportar o estudo do comportamento adaptável.Esta tese aborda o problema da modelação de atenção visual no contexto de robôs autónomos todo-o-terreno. O objectivo de utilizar mecanismos de atenção visual é o de focar a percepção nos aspectos do ambiente mais relevantes à tarefa do robô. Esta tese mostra que, na detecção de obstáculos e de trilhos, esta capacidade promove robustez e parcimónia computacional. Estas são características chave para a rapidez e eficiência dos robôs todo-o-terreno. Um dos maiores desafios na modelação de atenção visual advém da necessidade de gerir o compromisso velocidade-precisão na presença de variações de contexto ou de tarefa. Esta tese mostra que este compromisso é resolvido se o processo de atenção visual for modelado como um processo auto-organizado, cuja operação é modulada pelo módulo de selecção de acção, responsável pelo controlo do robô. Ao fechar a malha entre o processo de selecção de acção e o de percepção, o último é capaz de operar apenas onde é necessário, antecipando as acções do robô. Para fornecer atenção visual com propriedades auto-organizadas, este trabalho obtém inspi- ração da Natureza. Concretamente, os mecanismos responsáveis pela capacidade que as formi- gas guerreiras têm de procurar alimento de forma auto-organizada, são usados como metáfora na resolução da tarefa de procurar, também de forma auto-organizada, obstáculos e trilhos no campo visual do robô. A solução proposta nesta tese é a de colocar vários focos de atenção encoberta a operar como um enxame, através de interacções baseadas em feromona. Este trabalho representa a primeira realização corporizada de cognição de enxame. Este é um novo campo de investigação que procura descobrir os princípios básicos da cognição, ins- peccionando as propriedades auto-organizadas da inteligência colectiva exibida pelos insectos sociais. Logo, esta tese contribui para a robótica como disciplina de engenharia e para a robótica como disciplina de modelação, capaz de suportar o estudo do comportamento adaptável.Fundação para a Ciência e a Tecnologia (FCT,SFRH/BD/27305/2006); Laboratory of Agent Modelling (LabMag

Biologically Inspired Intelligence with Applications on Robot Navigation

Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies

Multi-Robot Coalition Formation for Distributed Area Coverage

The problem of distributed area coverage using multiple mobile robots is an important problem in distributed multi-robot sytems. Multi-robot coverage is encountered in many real world applications, including unmanned search & rescue, aerial reconnaissance, robotic demining, inspection of engineering structures, and automatic lawn mowing. To achieve optimal coverage, robots should move in an efficient manner and reduce repeated coverage of the same region that optimizes a certain performance metric such as the amount of time or energy expended by the robots. This dissertation especially focuses on using mini-robots with limited capabilities, such as low speed of the CPU and limited storage of the memory, to fulfill the efficient area coverage task. Previous research on distributed area coverage use offline or online path planning algorithms to address this problem. Some of the existing approaches use behavior-based algorithms where each robot implements simple rules and the interaction between robots manifests in the global objective of overall coverage of the environment. Our work extends this line of research using an emergent, swarming based technique where robots use partial coverage histories from themselves as well as other robots in their vicinity to make local decisions that attempt to ensure overall efficient area coverage. We have then extended this technique in two directions. First, we have integreated the individual-robot, swarming-based technique for area coverage to teams of robots that move in formation to perform area coverage more efficiently than robots that move individually. Then we have used a team formation technique from coalition game theory, called Weighted Voting Game (WVG) to handle situations where a team moving in formation while performing area coverage has to dynamically reconfigure into sub-teams or merge with other teams, to continue the area coverage efficiently. We have validated our techniques by testing them on accurate models of e-puck robots in the Webots robot simulation platform, as well as on physical e-puck robots

Robot-assisted discovery of evacuation routes in emergency scenarios

Abstract — When an emergency occurs within a building, it is crucial to guide victims towards emergency exits or human responders towards the locations of victims and hazards. The objective of this work is thus to devise distributed algorithms that allow agents to dynamically discover and maintain short evacuation routes connecting emergency exits to critical cells in the area. We propose two Evacuation Route Discovery mechanisms, Agent2Tag-ERD and Tag2Tag-ERD, and show how they can be seamlessly integrated with existing exploration algorithms, like Ants, MDFS and Brick&Mortar. We then examine the interplay between the tasks of area exploration and evacuation route discovery; our goal is to assess whether the exploration algorithm influences the length of evacuation paths and the time that they are first discovered. Finally, we perform an extensive simulation to assess the impact of the area topology on the quality of discovered evacuation paths. I

The multi-agent flood algorithm as an autonomous system for search and rescue applications

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