Collective Search

This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of network optimization methods, and we expect this to be a fiuitful area for future research.

In this paper, we study a simple means for coordinating teams of simple agents. In particular, we study ant robots and how they can cover terrain once or repeatedly by leaving markings in the terrain, similar to what ants do. These markings can be sensed by all robots and allow them to cover terrain even if they do not communicate with each other except via the markings, do not have any kind of memory, do not know the terrain, cannot maintain maps of the terrain, nor plan complete paths. The robots do not even need to be localized, which completely eliminates solving difficult and time-consuming localization problems. In this paper, we use real-time heuristic search methods to implement ant robots and present a simulation study with several real-time heuristic search methods to study their properties for terrain coverage. Our experiments show that all of the realtime heuristic search methods robustly cover terrain even if the robots are moved without realizing this, some robots fail, and some markings get destroyed. These results demonstrate that terrain coverage with real-time heuristic search methods is an interesting alternative to more conventional terrain coverage methods.

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.

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.

We present an algorithm by which a swarm of unicycle robots can simultaneously fill multiple planar solid polygonal shapes and also morph between different shapes. By decomposing the desired shape into triangles and defining formation points that lie on each triangle, the robots fill the shape using a divide-and-conquer strategy. Each robot is equipped with limited range and bearing sensors that are used for localized communication and for collision avoidance. The proposed algorithm also allows the swarm to operate in and adapt to dynamic environments, for example, while navigating through narrow passages or avoiding dynamic obstacles. The algorithm is designed to prevent oscillatory behaviour and deadlocks while enabling collision avoidance. We demonstrate the effectiveness of the algorithm through simulations using the iRobot Create mobile robots.

We present a method for finding reduced time coverage paths of multiple UAVs (Unmanned Air Vehicles) monitoring a 3D terrain represented as height fields. A novel metric based on per time visibility is used that couples visibility gained at a terrain point with the time spent to reach the point. This coupled metric is utilized to form reduced time paths by maximizing the visibility gained per unit time at every step. We compare the results of this approach with an approach that covers the terrain based on a per distance visibility metric, which reduces the sum, over distances covered by each UAV path. The comparisons show that the current method gives substantially time reduced paths albeit with an expected increase in sum over distances of UAV paths. We also show that time taken to cover the terrain based on the current metric is far less than prevalent methods that try to decompose the terrain based on visibility followed by time or time followed by visibility in a decoupled fashion. The method is further extended to provide for fault tolerance on a hostile terrain. Each terrain point is guaranteed to be seen by at-least one UAV that has not been damaged due to any calamity, shot or otherwise.

An agent-based emergent intelligence methodology is utilized to assemble agents into teams and assign distributed tasks to them in a dynamic environment. The application concerns the management of military missions that utilize intelligent munitions that are capable of searching, detecting, identifying, and attacking targets in a battlefield. The simulator follows a reactive, behavior-based design philosophy. Agents are implemented as asynchronous threads that employ polymorphic signal-based communication. Results to date indicate that the simulation framework is effective in evaluating ways for emergent teams to quickly solve dynamic teaming and task allocation problems.

In the plane, the way to enclose the most area with a given perimeter and to use the shortest perimeter to enclose a given area, is always to use a circle. If we replace the plane by a regular tiling of it, and construct polyforms i.e. shapes as sets of tiles, things become more complicated. We need to redefine the area and perimeter measures, and study the consequences carefully. A spiral construction often provides, for every integer number of tiles (area), a shape that is most compact in terms of the perimeter or boundary measure; however it may not exhibit all optimal shapes. We characterize in this paper all shapes that have both shortest boundaries and maximal areas for three common planar discrete spaces.

In this paper we discuss the task of efficiently using ant-like robotic agents for covering a connected region on the Z 2 grid, whose shape and size are unknown in advance, and which expands at a given rate. This is done using myopic robots, with no ability to directly communicate with each other, where each robot is equipped with only O(1) memory. We show that regardless of the algorithm used, and the robots' hardware and software specifications, the minimal number of robots required in order to enable such coverage is Ω(√ n) (where n is the initial size of the connected region). In addition, we show that when the region expands at a sufficiently slow rate, a team of Θ(√ n) robots could cover it in at most O(n 2 ln n) time. Regarding the coverage of nonexpanding regions in the grid, we improve the current best known result of O(n 2) by demonstrating an algorithm of worse case completion time of O(1 k n 1.5 + n), and faster for shapes of perimeter length which is shorter than O(n).

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

In the plane, the way to enclose the most area with a given perimeter and to use the shortest perimeter to enclose a given area, is always to use a circle. If we replace the plane by a regular tiling of it, and construct polyforms i.e. shapes as sets of tiles, things become more complicated. We need to redefine the area and perimeter measures, and study the consequences carefully. A spiral construction often provides, for every integer number of tiles (area), a shape that is most compact in terms of the perimeter or boundary measure; however it may not exhibit all optimal shapes. We characterize in this paper all shapes that have both shortest boundaries and maximal areas for three common planar discrete spaces.

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.

We propose a new `Mark-Ant-Walk' algorithm for robust and efficient covering of continuous domains by ant-like robots with very limited capabilities. The robots can mark places visited with pheromone marks and sense the level of the pheromone in their local neighborhood. In the case of multiple robots, these pheromone marks can be sensed by all robots and provide the only way of (indirect) communication between the robots. The robots are assumed to be memoryless, and to have no information besides that mentioned above. Despite the robots' simplicity we show that they are able, by running a very simple rule of behavior, to ensure efficient covering of arbitrary connected domains, including non-planar and multidimensional domains. The novelty of our algorithm lies in the fact that, unlike previously proposed methods, our algorithm works on continuous domains without relying on some induced underlying graph effectively reducing the problem to a discrete case of graph covering. ...

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.

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.

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 ...

We describe the Centibots system, a very large scale distributed robotic system, consisting of more than 100 robots, that has been successfully deployed in large, unknown indoor environments, over extended periods of time (i.e., durations corresponding to several power cycles). Unlike most multiagent systems, the set of tasks about which teams must collaborate is not given a priori. We first describe a task inference algorithm that identifies potential team commitments that collectively balance constraints such as reachability, sensor coverage, and communication access. We then describe a dispatch algorithm for task distribution and management that assigns resources depending on either task density or replacement requirements stemming from failures or power shortages. The targeted deployment environments are expected to lack a supporting communication infrastructure; robots manage their own network and reason about the concomitant localization constraints necessary to maintain team communication. Finally, we present quantitative results in terms of a "search and rescue problem" and discuss the team-oriented aspects of the system in the context of prevailing theories of multiagent collaboration.

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.

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

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.

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 ...

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 ...

This paper is a survey of research on autonomous search strategies which originate in engineering and biology. Our motivation is to identify methods of search in an essentially two-dimensional Euclidean space, which can be applied to the area of demining. Such search strategies are based on spatio-temporal distributions. These distributions may be known in advance, because of prior intelligence or through the use of remote sensing, or they may be the result of on-line gathering of information as the search progresses, or of both. We first review the literature on search and coordination which emanates from the field of robotics, we then summarize significant research in the field of animal search, and also discuss relevant results in robotics which are inspired by animal behavior.

This paper is a survey of research on autonomous search strategies which originate in engineering and biology. Our motivation is to identify methods of search in an essentially two-dimensional Euclidean space, which can be applied to the area of demining. Such search strategies are based on spatio-temporal distributions. These distributions may be known in advance, because of prior intelligence or through the use of remote sensing, or they may be the result of on-line gathering of information as the search progresses, or of both. We first review the literature on search and coordination which emanates from the field of robotics, we then summarize significant research in the field of animal search, and also discuss relevant results in robotics which are inspired by animal behavior.

This paper is a survey of research on autonomous search strategies which originate in engineering and biology. Our motivation is to identify methods of search in an essentially two-dimensional Euclidean space, which can be applied to the area of demining. Such search strategies are based on spatio-temporal distributions. These distributions may be known in advance, because of prior intelligence or through the use of remote sensing, or they may be the result of on-line gathering of information as the search progresses, or of both. We first review the literature on search and coordination which emanates from the field of robotics, we then summarize significant research in the field of animal search, and also discuss relevant results in robotics which are inspired by animal behavior.

We describe the Centibots system, a very large scale distributed robotic system, consisting of more than 100 robots, that has been successfully deployed in large, unknown indoor environments, over extended periods of time (i.e., durations corresponding to several power cycles). Unlike most multiagent systems, the set of tasks about which teams must collaborate is not given a priori. We first describe a task inference algorithm that identifies potential team commitments that collectively balance constraints such as reachability, sensor coverage, and communication access. We then describe a dispatch algorithm for task distribution and management that assigns resources depending on either task density or replacement requirements stemming from failures or power shortages. The targeted deployment environments are expected to lack a supporting communication infrastructure; robots manage their own network and reason about the concomitant localization constraints necessary to maintain team communication. Finally, we present quantitative results in terms of a "search and rescue problem" and discuss the team-oriented aspects of the system in the context of prevailing theories of multiagent collaboration.

We describe the Centibots system, a very large scale distributed robotic system, consisting of more than 100 robots, that has been successfully deployed in large, unknown indoor environments, over extended periods of time (i.e., durations corresponding to several power cycles). Unlike most multiagent systems, the set of tasks about which teams must collaborate is not given a priori. We first describe a task inference algorithm that identifies potential team commitments that collectively balance constraints such as reachability, sensor coverage, and communication access. We then describe a dispatch algorithm for task distribution and management that assigns resources depending on either task density or replacement requirements stemming from failures or power shortages. The targeted deployment environments are expected to lack a supporting communication infrastructure; robots manage their own network and reason about the concomitant localization constraints necessary to maintain team communication. Finally, we present quantitative results in terms of a "search and rescue problem" and discuss the team-oriented aspects of the system in the context of prevailing theories of multiagent collaboration.

We describe the Centibots system, a very large scale distributed robotic system, consisting of more than 100 robots, that has been successfully deployed in large, unknown indoor environments, over extended periods of time (i.e., durations corresponding to several power cycles). Unlike most multiagent systems, the set of tasks about which teams must collaborate is not given a priori. We first describe a task inference algorithm that identifies potential team commitments that collectively balance constraints such as reachability, sensor coverage, and communication access. We then describe a dispatch algorithm for task distribution and management that assigns resources depending on either task density or replacement requirements stemming from failures or power shortages. The targeted deployment environments are expected to lack a supporting communication infrastructure; robots manage their own network and reason about the concomitant localization constraints necessary to maintain team communication. Finally, we present quantitative results in terms of a "search and rescue problem" and discuss the team-oriented aspects of the system in the context of prevailing theories of multiagent collaboration.

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.

E cient exploration of large networks is a central issue in data mining and network maintenance applications. In most existing work there is a distinction between the active \searcher" which both executes the algorithm and holds the memory and the passive \searched graph" over which the searcher has no control at all. Large dynamic networks like the Internet, where the nodes are powerful computers and the links have narrow bandwidth and are heavily-loaded, call for a di erent paradigm, in which a non-centralized group of one or more lightweight autonomous agents traverse the network in a completely distributed and parallelizable way. Potential advantages of such a paradigm would be fault tolerance against network and agent failures, and reduced load on the busy nodes due to the small amount of memory and computing resources required by the agent in each node. Algorithms for network covering based on this paradigm could be used in today's Internet as a support for data mining and network control algorithms. Recently, a Vertex Ant Walk (VAW) method has been suggested WLB98] for searching an undirected, connected graph by an a(ge)nt that walks along the edges of the graph, occasionally leaving \pheromone" traces at nodes, and using those traces to guide its exploration. It was shown there that the ant can cover a static graph within time nd where n is the number of vertices and d the diameter of the graph. In this work we further investigate the performance of the VAW method on dynamic graphs, where edges may appear or disappear during the search process.

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 ...

We design and analyze the performance of cooperative search strategies for unmanned aerial vehicles (UAVs) searching for moving, possibly evading, targets in a hazardous environment. Rather than engaging in independent sensing missions, the sensing agents (UAVs with sensors) " work together " by arranging themselves into a flight configuration that optimizes their integrated sensing capability. If a UAV is shot down by enemy fire, the team adapts by reconfiguring its topology to optimally continue the mission with the surviving assets. We present a cooperative search methodology that integrates the multiple agents into an advantageous formation that distinctively enhances the sensing and detection operations of the system while minimizing the transmission of excessive control information for adaptation of the team's topology. After analyzing our strategy to determine the performance tradeoff between search time and number of UAVs employed, we present an algorithm that selects the minimum number of UAVs to deploy in order to meet a targeted search time within probabilistic guarantees.

Constructing architecture and generating optimal paths for autonomous robots are some of the heavily studied subjects in mobile agents applications. The aim of this paper is to find and analyze a optimal path algorithm for a group of autonomous robots using agent-based architecture in a virtual reality environment. The optimal path is determined by a heuristic approach, A-Star algorithm. The master-slave architecture control the communication between robots (slave agents) and the mobile agent (master). The robots communicate with the mobile agents to generate an optimal path (time and collision-free optimal path) by presenting all the subsequent trajectories while navigating among various obstacles. The results are conducted to show the effectiveness of the proposed architecture. 2000 Mathematics Subject Classification. Primary 68T99; Secondary 65D17.

Generally, swarm models and algorithms consider synchronous agents, i.e., they act simultaneously. This hypothesis does not fit multi-agent simulators nor robotic systems. In this paper, we consider such issues on a patrolling ant algorithm. We examine how different execution hypotheses influence self-organization capabilities and patrolling performances of this algorithm. We consider the mono and multiagent cases, the synchronism and determinism hypotheses, and the execution of the model with real robots.

This paper explores two enhancements that can be made to single and multiple robot exploration in graph-like worlds. One enhancement considers the order in which potential places are explored and another considers the exploitation of local neighbor information to help disambiguate possible locations. Empirical evaluations show that both enhancements can produce a significant reduction in exploration effort in terms of the number of mechanical steps required over the original exploration algorithms and that for some environments up to 60% reduction in mechanical steps can be achieved.

Complete area coverage navigation (CAC) requires a special type of robot path planning, where the robots should visit every point of the state workspace. CAC is an essential issue for cleaning robots and many other robotic applications. Real-time complete area coverage path planning is desirable for efficient performance in many applications. In this paper, a novel vertical cell-decomposition (VCD) with convex hull (VCD-CH) approach is proposed for real-time CAC navigation of autonomous mobile robots. In this model, a vertical cell-decomposition (VCD) methodology and a spanningtree based approach with convex hull are effectively integrated to plan a complete area coverage motion for autonomous mobile robot navigation. The computational complexity of this method with minimum trajectory length planned by a cleaning robot in the complete area coverage navigation with rectangle obstacles in the Euclidean space is O(n log n). The performance analysis, computational validation and comparison studies demonstrate that the proposal model is computational efficient, complete and robust.

Dispersing swarms of robots to cover an unknown, potentially hostile area is useful to setup a sensor network for surveillance. Previous research assumes relative locations (distance and bearing) of neighboring robots are available to each robot through sensors. Many robots are too small to carry sensors capable of providing this information. We use wireless signal intensity as a rough approximation of distance to assist a large swarm of small robots in dispersion. Simulation experiments indicate that a swarm can effectively disperse through the use of wireless signal intensities without knowing the relative locations of neighboring robots.

Generally, swarm models and algorithms consider synchronous agents, i.e., they act simultaneously. This hypothesis does not fit multi-agent simulators nor robotic systems. In this paper, we consider such issues on a patrolling ant algorithm. We examine how different execution hypotheses influence self-organization capabilities and patrolling performances of this algorithm. We consider the mono and multiagent cases, the synchronism and determinism hypotheses, and the execution of the model with real robots.

Most distributed simulations are applications composed of numerous mobile communicating entities that continuously evolve. Such entities are organized or organize themselves in groups or societies of cooperating agent or processes. To keep these simulations efficient, it may be necessary to migrate entities of highly communicating groups so that they remain close, ideally on the same processing unit, while at the same time, we need to maintain a reasonable load for each processor.

In order to organize effective rescue missions a
prompt intervention is essential, and to this extent UAV drones
are a promising technology to the end of exploring an area
and gathering information before first responders come into
play. In this article we investigate the development of team
of drones (multicopters) implementing autonomous coverage
strategies for Search&Rescue after earthquakes. In particular
we survey, analyse and compare the performance of a subset
of real-time multi-robot coverage algorithms in the literature,
in order to classify their performance in terms of the required
coverage time and, indirectly, the energy required to perform a
mission. The algorithms were implemented using a distributed
software framework and tested in a 3D simulation environment.
We support our analysis presenting a wide set of experimental
results tested in different scenarios. Finally we present some
preliminary test using real robots to perform the coverage.

Swarm spreading has been gaining more focus since the more reliance on artificial intelligence in solving complex problems. Such importance comes from the various possible applications of the swarm robotics physically and virtually. This work has more focus on proposing an enhanced approach for spreading virtual autonomous swarms over an unknown environment. The main considerations included having less direct communication among the agents, covering the whole environment with as few steps as possible, keeping the agents on the move with less waiting time and the possibility of applying the simulation environment to various problems. The preliminary results are promising as they show interesting outcomes over the setup environment.

In this paper, we propose Clustering method and Ant Colony Optimization (ACO) for mobile robot. This paper describes the analysis and design of a new class of mobile robots. These small robots are intended to be simple and inexpensive, and will all be physically identical, thus constituting a homogeneous team of robots. They derive their usefulness from their group actions, performing physical tasks and making cooperative decisions as a Coordinated

A multiple odor sources search strategy based on a modified ant algorithm is proposed to enable the multi-robot system to search for the odor sources that exist in the indoor environment by imitating the forage behavior of ant colony. The search strategy includes two parts, which are odor tracking strategy and odor sources localization strategy. The tracking strategy consists of a modified ant colony algorithm. The localization strategy is an odor source verification procedure introduced into the tracking process to localize multiple odor sources. Simulation results show that the proposed strategy can effectively enable the robot system to search and determine the odor sources quickly and accurately.

An agent-based emergent intelligence methodology is utilized to assemble agents into teams and assign distributed tasks to them in a dynamic environment. The application concerns the management of military missions that utilize intelligent munitions that are capable of searching, detecting, identifying, and attacking targets in a battlefield. The simulator follows a reactive, behavior-based design philosophy. Agents are implemented as asynchronous threads that employ polymorphic signal-based communication. Results to date indicate that the simulation framework is effective in evaluating ways for emergent teams to quickly solve dynamic teaming and task allocation problems.

Abstract Purpose–A simultaneous solution to the localization and mapping problem of a graph-like environment by a swarm of robots requires solutions to task coordination and map merging. The purpose of this paper is to examine the performance of two different map-merging strategies. Design/methodology/approach–Building a representation of the environment is a key problem in robotics where the problem is known as simultaneous localization and mapping (SLAM).

Abstract This paper explores two enhancements that can be made to single and multiple robot exploration in graph-like worlds. One enhancement considers the order in which potential places are explored and another considers the exploitation of local neighbor information to help disambiguate possible locations.

Abstract Ant robots are simple creatures with limited sensing and computational capabilities. They have the advantage that they are easy to program and cheap to build. This makes it feasible to deploy groups of ant robots and take advantage of the resulting fault tolerance and parallelism. We study, both theoretically and in simulation, the behavior of ant robots for one-time or repeated coverage of terrain, as required for lawn mowing, mine sweeping, and surveillance.

Abstract: 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 stochastically. Specifically, we discuss the problem where an initial connected region of $ S_0 $ boundary tiles expand outward with probability $ p $ at every time step.