Swarm Algorithm

Запропоновано чотирьох етапний алгоритм навчання класифікатора. На першому етапі навчання відбувається навчання з нормалізованою триплетною функцією втрат та регуляризуючою складовою для штрафування за помилку округлення вихідного сигналу мережі до двійкового коду. Наступний етап потрібний для визначення двійкового коду класу відповідно до принципів завадо захищеного кодування з самовиправленням помилок, але з урахуванням внутрікласових і між класових відношень.

Abstract. The purpose of the work is to develop a methodology for quantifying the degree of influence of the human factor on the safety and management of technical systems in the context of the genesis (origin) and topology (development) of the influence of this phenomenon, as an artifact, on management decision–making, identifying the stages and ways of its development and influence.
The article considers the transition adapter from determining the psychological properties of a person (level of confidence, passivity, etc.) under the influence of the human factor on decision-making in a state of mobilization readiness (based on the
ergatic system state function and Heaviside function) for the successful completion of a control action in a technical system (the very fact of making a decision); an approach to analyzing the temporal sweep of the influence of the human factor on the formation of decision-making based on the use of heuristic algorithms, swarm intelligence mechanisms to describe the genesis and topology of this phenomenon. An auxiliary method is proposed for assessing the impact of the human factor using
statistical distributions in the context of considering the safety culture of workers for industrial areas in the context of their participation in the functioning of ergatic systems.
The manifestation of the human factor is considered as a statistical result (manifestation) of a large number of relationships and events of an individual nature and group interaction within professional groups. The conditions and hypotheses for the application of statistical distributions are given. The general case of using a binomial distribution based on the hypothesis of a statistical distribution of cases of human influence is considered for the transition from a psychological interpretation of this phenomenon to a quantitative assessment of the impact in the context of a safety culture based on a one-factor statistical distribution. The cases and conditions of using the Poisson distribution to characterize the influence of the human factor on the safety of technical systems during managerial decision-making are indicated.
Keywords: ergatic system, human factor, safety culture, genesis, topology, safeometrics, swarm algorithm, Heaviside function.
Аннотация Цель работы-развитие методологии количественной оценки степени влияния человеческого фактора на безопасность и управление технических систем в контексте генезиса (зарождения) и топологии (развития) влияния этого явления как артефакта, на принятие управляющих решений, выявление стадий и путей его развития и влияния. Рассматриваются адаптер перехода от определения психологических свойств личности (уровня уверенности, пассивности) при влиянии человеческого фактора на принятие решения в состоянии мобилизационной готовности (на основе функции состояния эргатической системы и функции Хевисайда) для совершения успешного завершения управляющего действия в технической системе (самого факта принятия решения); подход анализа временнóй развёртки влияния человеческого фактора на формирование принятия решения (гипотеза) на основе использования эвристических алгоритмов, механизмов роевого интеллекта для описания генезиса и топологии этого явления. Предложен вспомогательный метод для оценки интегрального влияния человеческого фактора с использованием статистических распределений в разрезе рассмотрения культуры безопасности работников для производственных областей в контексте их участия в функционировании эргатических систем. Проявление человеческого фактора рассматривается как статистический результат (проявление) большого количества отношений и событий индивидуального характера и группового взаимодействия внутри профессиональных групп. Приведены условия и гипотезы для применения статистических распределений. Рассмотрен общий случай использования биномиального распределения на основании гипотезы о статистическом распределении случаев влияния человеческого фактора для перехода от психологической интерпретации этого явления к количественной оценке влияния в разрезе культуры безопасности на основе однофакторного статистического распределения. Указаны случаи и условия применения распределения Пуассона для характеристики влияния человеческого фактора на безопасность технических систем в ходе принятия управленческих решений. Ключевые слова: эргатическая система, человеческий фактор, культура безопасности, генезис, топология, сэйфеометрика, роевой алгоритм, функция Хевисайда.
Лобач Д.И. О НЕКОТОРЫХ АСПЕКТАХ ОЦЕНКИ ВЛИЯНИЯ ЧЕЛОВЕЧЕСКОГО ФАКТОРА В ЭРГАТИЧЕСКИХ СИСТЕМАХ / Д.И. Лобач // Cifra. Психология. – 2025. – №3 (8). – URL: https://psychology.cifra.science/archive/3-8-2025-july/10.60797/PSY.2025.8.3 (дата обращения: 29.07.2025). – DOI: 10.60797/PSY.2025.8.3

Магістерська дисертація: 107 с., 47 рис., 39 табл., 42 джерела. Мета роботи – розробка системи класифікації сигналів кардіограм для діагностики захворювань серця на основі розширеного датасету з синтезованими сигналами ЕКГ. Об’єктом дослідження є набір даних кардіограм пацієнтів з захворюваннями серця. Предметом дослідження є засоби генерації часових медичних сигналів та методи класифікації на основі штучних нейронних мереж. В роботі досліджується проблема розпізнавання стану здоров’я людини, шляхом аналізу її електрокардіограми, а також програмні засоби для реалізації поставленої задачі. Виконано аналіз методів обробки часових сигналів, проведено дослідження засобів генерації штучних сигналів ЕКГ, аналіз математичних структур та сучасних методів класифікації. Результатом роботи є аналіз сучасних систем та підходів до вирішення задачі класифікації кардіограм, розробка модифікованої системи класифікації зі збільшенням кількості вхідних даних шляхом додавання синтетичних сигналів ЕКГ....

We consider the problem of coordinating a team of agents that have to collect disseminated resources in an unknown environment. We are interested in approaches in which agents collectively explore the environment and build paths between home and resources. The originality of our approach is to simultaneously build an artificial potential field (APF) around the agents' home while foraging. We propose a multi-agent model defining a distributed and asynchronous version of Barraquand et al.'s Wavefront algorithm. Agents need only to mark and read integers locally on a grid, that is, their environment. We prove that the construction converges to the optimal APF. This allows the definition of a complete parameterfree foraging algorithm, called c-marking agents. The algorithm is evaluated by simulation while varying the foraging settings. Then we compare our approach to a pheromone-based algorithm. Finally, we discuss requirements for implementation in robotics.

We developed the algorithm of learning of the multilayer feature extractor based on ideas and methods of neural gas and sparse encoding, for the problem of prediction of violation of agreement conditions on the service level in a cloud-based environment. Effectiveness of the proposed extractor and autoencoder was compared by the results of physical simulation. It is shown that the proposed extractor requires approximately 1.6 times as few learning samples as the autoencoder for construction of error-free decision rules for learning and test samples. This allows us previously put into effect prediction mechanisms of controlling appropriate cloud-based services.To build up decision rules, it is proposed to use transformation of the space of primary features using computationally efficient operations of comparison and "excluding OR" for construction in the radial basis of the binary space of secondary features of separate class containers. In this case, for binary feature enc...

The work presented focuses in the simulations and real experiments of perimeter surveillance under communication constraints, performed by teams of UAVs using a Bluetooth communication framework. When UAVs work in a colaborative manner, communication among them is essential to properly perform their task. Moreover, energy consumption and weight of the devices equipped in a UAV are important to be reduced at minimum possible, particularly in micro-UAVs. A coordination variables strategy is implemented to perform the perimeter division.

Health care and hospitals services could greatly benefit from technological innovations in many fields beyond the disease treatment itself. For instance the cleaning process deserves a significant role among the services that a hospital must deliver. In this study we explore the fully automated and the traditional (human-based) cleaning protocol, we define its main components and discuss steps and benefits in the introduction of a partially automated solution based on a new robotic system.

Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brainwaves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of such system through providing a better sub-set of electrodes or features, reducing the required training time of the classifiers, reducing the noise to signal ratio, and so on. This chapter provides a survey on some of the commonly used EA methods in EEG study.

Abstract: One of the popular methods for optimizing combinational problems such as portfolio selection problem is swarmbased methods. In this paper, we have proposed an approach based on Quantum-Behaved Particle Swarm Optimization (QPSO) for the portfolio selection problem. The particle swarm optimization (PSO) is a well-known population-based swarm intelligence algorithm. QPSO is also proposed by combining the classical PSO philosophy and quantum mechanics to improve performance of PSO. Generally, investors, in portfolio selection, simultaneously consider such contradictory objectives as the rate of return, risk and liquidity. We employed Quantum-Behaved Particle Swarm Optimization (QPSO) model to select the best portfolio in 50 supreme Tehran Stock Exchange companies in order to optimize the objectives of the rate of return, systematic and non-systematic risks, return skewness, liquidity and sharp ratio. Finally, the obtained results were compared with Markowitz`s classic and Gene...

We present a model consisting of an emergent procedure for n unpiloted, autonomous flying munitions to cooperatively conduct a parallel sweeping search of a geographic area for specific targets. The control of the unmanned air vehicles (UAVs) is decentralized and follows the reactive, behavior-based design philosophy. Global communication is not available to the UAVs; rather, they rely on passive sensors and minimal amounts of short-range, inter-UAV communication. The model is implemented and successfully demonstrated in an agent-based, simulated battlefield environment. The results of the simulation indicate that the emergent, behavior-based search procedure for UAVs is effective, robust, and scalable. It is especially well suited for numerous, small, inexpensive, and expendable UAVs. Key-Words: Adaptive Flying Munitions, Autonomous Behaviors, Emergent Intelligence, Cooperative Search, Unpiloted Air Vehicles.

This article presents a quadtree data structured method for complete area coverage (CAC) path-planning in known environment with arbitrary shape obstacles by applying the concepts of contour map and spanning tree. Contour map can reduce the number of turns effectively. The results of this proposed method can achieve no overlapping with complete area coverage. The robot will start and end at the exact same point. 

In this paper, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to Forgive & Forget (F&F), otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically F&F to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane.

We present a model consisting of an emergent procedure for n unpiloted, autonomous flying munitions to cooperatively conduct a parallel sweeping search of a geographic area for specific targets. The control of the unmanned air vehicles (UAVs) is decentralized and follows the reactive, behavior-based design philosophy. Global communication is not available to the UAVs; rather, they rely on passive sensors and minimal amounts of short-range, inter-UAV communication. The model is implemented and successfully demonstrated in an agent-based, simulated battlefield environment. The results of the simulation indicate that the emergent, behavior-based search procedure for UAVs is effective, robust, and scalable. It is especially well suited for numerous, small, inexpensive, and expendable UAVs.

Over the past few years, many research projects have begun to focus on swarms of mobile unmanned systems (e.g., drones, ground robots) globally referred as UMS. These systems, because of the many sensors and actuators they can embed, are suitable for autonomous missions in 3D (Dull, Dirty and Dangerous) environments for instance. However, embedding a large number of capabilities in all of members of a swarm is expensive in terms of cost, weight and energy consumption. Thus, it is usually more efficient to embed only a single or a few capabilities within each UMS. It then becomes necessary to provide a discovery mechanism built into the swarm in order to allow its members to share their capabilities and to collaborate for achieving a global mission. These shared capabilities are called services. In this paper, we propose a new service discovery system called AMiRALE for Asynchronous Missions Relay for Autonomous and Lively Entities dedicated to highly volatile, autonomous and mobile swarms of UMS. Our solution is independent of both nodes' mobility and connectivity patterns. Moreover, it supports heterogeneous swarms and degraded conditions of operation (i.e., message loss, UMS loss and disconnected network). It is also totally decentralized and enables both discovery and service usage. We provide a description of the theoretical model of our AMiRALE system as well as several simulation results obtained from a park cleaning scenario.

Several recent works considered multi agents robotics in static environments (e.g. [2], [4], [5] and others). In this work we examine ways of operating in dynamic environments, in which changes may take place regardless of the agents' activity. The work focuses on a dynamic variant of the known Cooperative Cleaners problem (described and analyzed in [2]). This problem assumes a grid, part of which is "dirty", when the "dirty" part is a connected region of the grid. On this dirty region several agents move, each having the ability to "clean" the place it is located in. The dynamic variant of the problem involves a deterministic evolution of the environment, simulating a spreading contamination, or fire. A cleaning protocol for the problem is presented, as well as several analytic bounds for it. In addition, the work contains simulative results for the proposed protocol.

An ant-inspired method is described for exploring a continuous unknown planar region by a group of robots having limited sensors and no explicit communication. Such a method has applications in robotics where a robot with limited sensing capabilities but with the ability to leave marks on the ground has to cover a closed region for purposes of cleaning a dirty floor, painting a wall, or demining a mine-field. We formalize the problem and suggest the mark and cover (MAC) rule of motion to solve it using temporary markers ("pheromones") as means of navigation and indirect communication. The convergence of the algorithm is proved, and its cover time is shown to be the asymptotically optimal O(A=a), A being the total area and a - the area covered by the robot in a single step. 1 Introduction Exploring unknown terrain is an important issue in robotics. The problem has already been investigated, and several methods have been suggested and implemented. Most of those methods, howe...

In this paper we study the strengths and limitations of collaborative teams of simple agents. In particular, we discuss the efficient use of "ant robots" for covering a connected region on the Z 2 grid, whose area is unknown in advance, and which expands at a given rate, where n is the initial size of the

We consider a problem of decentralized exploration of a faulty network by several simple, memoryless agents. The model we adopt for a network is a directed graph. We design an asynchronous algorithm that can cope with failures of network edges and nodes. The algorithm is self-stabilizing in the sense that it can be started with arbitrary initializations and scalablenew agents can be added while other agents are already running.

Indeed, ants are most fascinating social insects. These tiny creatures live in societies as complex as ours, and are the most abundant and resilient creatures on the earth. The communities of these myopic creatures capable of short-range interactions achieve amazing feats of global problem solving and pattern formation. It should come as no surprise that ant-behavior became a source of algorithmic ideas for distributed systems where a computer, or a robot, is the "individual" and the network, or a swarm of robots, plays the role of the "colony". The flow of ideas from insects to machines is becoming increasingly popular, see, e.g., [2]. Basic lessons learned from the ants are achieving reliability through redundancy, relying on decentralization via some "individual utility" optimization and transforming the environment into a grand shared database by active marking. Research into ant-robotic or distributed a(ge)nt systems can be classified into analysis and synthesis topics. In analysis, a rule of behavior is postulated and one attempts to predict or, more often, simulate the global behavior, while in synthesis a global goal is given and one must find a proper local rule the achieves it. Naturally, most of the body of research existing today is concerned with the considerably simpler analysis issues. Systematic and mathematically proved methods of analysis are still rare, and synthesis examples are almost nonexistent. Recently ideas about simple a(ge)nts that cooperate to achieve global goals were pursued by many, among them [3,4,10]. Only in a few cases mathematical results exhibiting explicit benefits of cooperation have been found. Experimental research is still the universal tool of analysis and discovery, and it should be so. However, the last years have seen many attempts at formalizing and proving results on multi-agent interaction. The mathematics needed is always interesting, and interdisciplinary in nature, and we are very excited to see these new developments. Mathematical approaches to ant-robotics

This work discusses the problem of exploration of an unknown environment using a collaborative group of simple agents. While this problem was known to be of a non-polynomial time complexity, it was speculated in the past that in grid domains the completion time of this problem is much lower (although analytic proofs were not available hitherto). In this work we present a preliminary result concerning a constructive analytic constraint for guaranteeing that the time complexity of this problem in grid domains is indeed polynomial.

SUMMARYThis work examines the Cooperative Hunters problem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more “evading targets,” which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

Some insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper we follow an ancient advice 1 and investigate the ability of a group of robots that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown network. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multia(ge)nt adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of cooperatively covering a graph, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g. DFS), our algorithms are adaptive: they will complete the traversal of the graph even if some of the agents die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time. A similar smell-oriented mechanism can be used to keep a spanning tree of a dynamic network.

In the world of living creatures, simple-minded animals often cooperate to achieve common goals with amazing performance. One can consider this idea in the context of robotics, and suggest models for programming goal-oriented behavior into the members of a group of simple robots lacking global supervision. This can be done by controlling the local interactions between the robot agents, to have them jointly carry out a given mission. As a test case we analyze the problem of many simple robots cooperating to clean the dirty floor of a non-convex region inZ2 , using the dirt on the floor as the main means of inter-robot communication.

Several recent works considered multi-a(ge)nt robotics in static environments. In this work we examine ways of operating in dynamic environments, where changes take place independently of the agents' activity. The work focuses on a dynamic variant of the cooperative cleaners problem, a problem that requires several simple agents to clean a connected region of "dirty" pixels in Z 2. A number of simple agents move in this dirty region, each having the ability to "clean" the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a deterministic expansion of dirt in the environment, simulating spreading of contamination or fire. Theoretical lower bounds for the problem are presented, as well as various impossibility results. A cleaning protocol for the problem is presented, and a wealth of experimental results testing its performance in comparison to the lower bounds. Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

Abstract. This work discusses an impossibility result for the Dynamic Cooperative Cleaners problem, and the relation of a specific geometric feature of the problem, known as the shape factor, to the efficiency of the operating swarm. The dynamic cooperative cleaners problem assumes a grid, having “contamination” points or tiles that form a connected region of the grid. Several agents move in this contaminated region, each having the ability to “clean” the place it is located in. The” contaminated” tiles expand deterministically, simulating a ...

We consider a problem of decentralized exploration of a faulty network by several simple, memoryless agents. The model we adopt for a network is a directed graph. We design an asynchronous algorithm that can cope with failures of network edges and nodes. The algorithm is self-stabilizing in the sense that it can be started with arbitrary initializations and scalable–new agents can be added while other agents are already running.

Abstract. The field of multi agents and multi robotics has become increasingly popular during the last two decades. The motivation behind multi agents based systems is that many tasks can be rather efficiently completed by using multi-ple simple autonomous agents (robots, ...

In this paper a task allocation problem for multi-UAV systems was considered. The possibility of applying a consensus approach to organize a distribution of tasks in an autonomous group of small Unmanned Aerial Vehicles (UAVs) is studied. The use of several small UAVs with autonomous distribution of tasks gives a significant advantage over the use of a single UAV. The well-known consensus protocol-local voting protocol is suggested to solve this problem. To provide the communication in the network multi-agent technologies are used.

Several recent works considered multi agents robotics in static environments (e.g. [2], [4], [5] and others). In this work we examine ways of operating in dynamic environments, in which changes may take place regardless of the agents' activity. The work focuses on a dynamic variant of the known Cooperative Cleaners problem (described and analyzed in [2]). This problem assumes a grid, part of which is "dirty", when the "dirty" part is a connected region of the grid. On this dirty region several agents move, each having the ability to "clean" the place it is located in. The dynamic variant of the problem involves a deterministic evolution of the environment, simulating a spreading contamination, or fire. A cleaning protocol for the problem is presented, as well as several analytic bounds for it. In addition, the work contains simulative results for the proposed protocol.

Indeed, ants are most fascinating social insects. These tiny creatures live in societies as complex as ours, and are the most abundant and resilient creatures on the earth. The communities of these myopic creatures capable of short-range interactions achieve amazing feats of global problem solving and pattern formation. It should come as no surprise that ant-behavior became a source of algorithmic ideas for distributed systems where a computer, or a robot, is the "individual" and the network, or a swarm of robots, plays the role of the "colony". The flow of ideas from insects to machines is becoming increasingly popular, see, e.g., [2]. Basic lessons learned from the ants are achieving reliability through redundancy, relying on decentralization via some "individual utility" optimization and transforming the environment into a grand shared database by active marking. Research into ant-robotic or distributed a(ge)nt systems can be classified into analysis and synthesis topics. In analysis, a rule of behavior is postulated and one attempts to predict or, more often, simulate the global behavior, while in synthesis a global goal is given and one must find a proper local rule the achieves it. Naturally, most of the body of research existing today is concerned with the considerably simpler analysis issues. Systematic and mathematically proved methods of analysis are still rare, and synthesis examples are almost nonexistent. Recently ideas about simple a(ge)nts that cooperate to achieve global goals were pursued by many, among them [3,4,10]. Only in a few cases mathematical results exhibiting explicit benefits of cooperation have been found. Experimental research is still the universal tool of analysis and discovery, and it should be so. However, the last years have seen many attempts at formalizing and proving results on multi-agent interaction. The mathematics needed is always interesting, and interdisciplinary in nature, and we are very excited to see these new developments. Mathematical approaches to ant-robotics

This work discusses the problem of exploration of an unknown environment using a collaborative group of simple agents. While this problem was known to be of a non-polynomial time complexity, it was speculated in the past that in grid domains the completion time of this problem is much lower (although analytic proofs were not available hitherto). In this work we present a preliminary result concerning a constructive analytic constraint for guaranteeing that the time complexity of this problem in grid domains is indeed polynomial.

SUMMARYThis work examines the Cooperative Hunters problem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more “evading targets,” which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

Some insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper we follow an ancient advice 1 and investigate the ability of a group of robots that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown network. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multia(ge)nt adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of cooperatively covering a graph, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g. DFS), our algorithms are adaptive: they will complete the traversal of the graph even if some of the agents die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time. A similar smell-oriented mechanism can be used to keep a spanning tree of a dynamic network.

In the world of living creatures, "simple minded" animals often cooperate to achieve common goals with amazing performance. One can consider this idea in the context of robotics, and suggest models for programming goaloriented behavior into the members of a group of simple robots lacking global supervision. This can be done by controlling the local interactions between the robot agents, to have them jointly carry out a given mission. As a test case we analyze the problem of many simple robots cooperating to clean the dirty floor of a non-convex region in Z 2 , using the dirt on the floor as the main means of inter-robot communication.

Several recent works considered multi-a(ge)nt robotics in static environments. In this work we examine ways of operating in dynamic environments, where changes take place independently of the agents' activity. The work focuses on a dynamic variant of the cooperative cleaners problem, a problem that requires several simple agents to clean a connected region of "dirty" pixels in Z 2. A number of simple agents move in this dirty region, each having the ability to "clean" the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a deterministic expansion of dirt in the environment, simulating spreading of contamination or fire. Theoretical lower bounds for the problem are presented, as well as various impossibility results. A cleaning protocol for the problem is presented, and a wealth of experimental results testing its performance in comparison to the lower bounds. Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

Abstract. Consider a swarm of weak, anonymous and homogeneous robots lacking memory, orientation, and communication capabilities, and having myopic sensors that tell them the directions to nearby robots, but not the distance from them. We present a simple randomized algorithm which, when performed by all members of the swarm, gathers them in a small region. We explore the interesting global phenomena that occur during the process, evident from our analysis and simulations.

Abstract. This work discusses an impossibility result for the Dynamic Cooperative Cleaners problem, and the relation of a specific geometric feature of the problem, known as the shape factor, to the efficiency of the operating swarm. The dynamic cooperative cleaners problem assumes a grid, having “contamination” points or tiles that form a connected region of the grid. Several agents move in this contaminated region, each having the ability to “clean” the place it is located in. The” contaminated” tiles expand deterministically, simulating a ...

Abstract. The field of multi agents and multi robotics has become increasingly popular during the last two decades. The motivation behind multi agents based systems is that many tasks can be rather efficiently completed by using multi-ple simple autonomous agents (robots, ...

With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.

An ant-inspired method is described for exploring a continuous unknown planar region by a group of robots having limited sensors and no explicit communication. Such a method has applications in robotics where a robot with limited sensing capabilities but with the ability to leave marks on the ground has to cover a closed region for purposes of cleaning a dirty oor, painting a wall, or demining a mine-eld. We formalize the problem and suggest the mark and cover (MAC) rule of motion to solve it using temporary markers (\pheromones") as means of navigation and indirect communication. The convergence of the algorithm is proved, and its cover time is shown to be the asymptotically optimal O(A=a), A being the total area and athe area covered by the robot in a single step.

Some insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper we follow an ancient advice 1 and investigate the ability of a group of robots that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown network. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multia(ge)nt adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of cooperatively covering a graph, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g. DFS), our algorithms are adaptive: they will complete the traversal of the graph even if some of the agents die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time. A similar smell-oriented mechanism can be used to keep a spanning tree of a dynamic network.

Ants and other insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper, we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multiagent adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. As opposed to existing traversal methods (e.g., depth first search), our algorithms are adaptive: they will complete the traversal of the graph even if some of the a(ge)nts die or the graph changes (edges/vertices added or deleted) during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time.

Some insects are known to use chemicals called pheromones for various communication and coordination tasks. In this paper we investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the oor of an un-mapped building, or any task that requires the traversal of an unknown network. More speci cally, we consider robots which leave chemical odor traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement ...

Traditional control approaches fall well short of the necessary flexibility and efficiency needed to meet the commercial and military demands placed upon UAV swarms. Effective coordination of these swarms requires development of control strategies based on emergent behavior. We have developed a rule-based, decentralized control algorithm that relies on constrained randomized behavior and respects UAV restrictions on sensors, computation, and flight envelope. To demonstrate and evaluate the effectiveness of our approach, we have created a simulation of an air vehicle swarm searching for and mapping a chemical cloud within a patrolled region. We then consider several different detection and mapping strategies based on emergent behavior. We then establish an inverse linear relation between the size of the swarm and the time to detect the cloud, regardless of the size of the cloud. Further, we also show the size of the swarm has a linear relation with the successful detection of the cloud.

Traditional control approaches fall well short of the necessary flexibility and efficiency needed to meet the commercial and military demands placed upon UAV swarms. Effective coordination of these swarms requires development of control strategies based on emergent behavior. We have developed a rule-based, decentralized control algorithm that relies on constrained randomized behavior and respects UAV restrictions on sensors, computation, and flight envelope. To demonstrate and evaluate the effectiveness of our approach, we have created a simulation of an air vehicle swarm searching for and mapping a chemical cloud within a patrolled region. We then consider several different detection and mapping strategies based on emergent behavior. We then establish an inverse linear relation between the size of the swarm and the time to detect the cloud, regardless of the size of the cloud. Further, we also show the size of the swarm has a linear relation with the successful detection of the cloud.