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Stigmergic Masa: a Stigmergy Based Algorithm 1479 Proceedings of the 2014 Federated Conference on DOI: 10.15439/2014F395 Computer Science and Information Systems pp. 1477–1485 ACSIS, Vol. 2 S-MASA: A Stigmergy Based Algorithm for Multi-Target Search Ouarda Zedadra and Hamid Seridi Nicolas Jouandeau Giancarlo Fortino LabSTIC Laboratory, 8 may 1945 University Advanced Computing laboratory DIMES, Universita’ P.O.Box 401, 24000 Guelma, Algeria of saint-Denis Paris 8 University della Calabria Department of computer science Badji Mokhtar Saint Denis 93526, France Via P. Bucci, cubo 41c - 87036 Annaba University P.O.Box 12, 23000 Annaba, Algeria Email: [email protected] - Rende (CS) - Italy Email: zedadra nawel1, [email protected] Email:[email protected] Abstract—We explore the on-line problem of coverage where 1) it is of very low computational complexity, in which multiple agents have to find a target whose position is unknown, agents have a very low-range of sensors; and without a prior global information about the environment. 2) it executes a search in nearby locations first by adopting In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based spiral turns around the starting cell and around agents on the principle of pheromone-based communication. Accord- each other; ing to this algorithm, called S-MASA (Stigmergic Multi Ant 3) agents use stigmergic communication via digital Search Area), the agents search nearby their base incrementally pheromone; using turns around their center and around each other, until 4) it can be executed on known or unknown static obstacle- the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via free environments or obstacle environments. depositing/detecting markers. This work improves the search The rest of this paper is organized as follows. Section 2 performance in comparison with random walk and S-random discusses some related work. Section 3 describes the problem walk (stigmergic random walk) strategies, we show the obtained statement and formulation. S-MASA algorithm is described in results using computer simulations. detail in Section 4. Performance evaluation is given in Section I. INTRODUCTION 5. A comparison with the random walk and S-random walk strategies are given in Section 6 and Section 7 concludes the HE PROBLEM of finding multiple targets whose posi- paper. T tions are unknown without a prior information about the environment is very important in many real world applica- II. RELATED WORK tions [1]. Those applications vary from mine detecting [2] [3], The problem of searching a target may be considered as a search in damaged buildings [4] [5], fire fighting [6], and ex- partial area coverage problem that constitutes a key element ploration of spaces [7] [8], where neither a map, nor a Global of the general exploration problem [17] where coverage can Positioning System (GPS) are available [9]. The random walk be done by a single or multiple robots, with on-line or off-line is the best option when there is some degree of uncertainty algorithms. In the on-line coverage algorithms, the area and in the environment and a reduced perceptual capabilities [10] target positions are unknown, and are discovered step by step because it is simple, needs no memory and self-stabilizes. while the robot explores the environment, whereas, in the off- However, it is inefficient in a two-dimensional infinite grid, line algorithms, the robot has a prior information about the where it results in an infinite searching time, even if the environment, target and obstacles positions, so it can plan the target is nearby [11], it results also in energy consumption path to go through. Different approaches have been developed and malfunction risks. To deal with these limits, some effective in the literature to solve area coverage using single or multiple ways to coordinate multiple agents in their searching task need robots. In this section, a brief overview of techniques that to take place. Recently many researchers have investigated are used to solve the coverage problem using both single bio-inspired coordination methods [12] [13], in which agents and multiple robots is presented. The single robot covering coordinate on the basis of indirect communication principle problem was explored by Gabriely and Rimon [18]. One of known as stigmergy. the most popular algorithms is the Spanning Tree Coverage Complexity of multi-target search solutions depends on (STC). In an STC algorithm, the robot operates in a 2D grid simplifications considered over idealized assumptions, such as: of large square cells. It aims to find a spanning tree for such perfect sensors [14], stationary environments [15], unlimited grid, and allow the robot to circumnavigate it. This algorithm direct communication [16]. Even if these assumptions are far covers every cell that is accessible from the starting point, and from real world applications, they provide first basic solutions. it is optimal because the robot passes through each cell at least The algorithm presented in this paper avoids such type of once [19]. Spiral STC is an online sensor based algorithm assumptions. It makes the following contributions: for covering planar areas by a square shaped tool attached 978-83-60810-58-3/$25.00 c 2014, IEEE 1477 1478 PROCEEDINGS OF THE FEDCSIS. WARSAW, 2014 to a mobile robot. The algorithm incrementally subdivides individually based on its local information to which subarea it the planar work into disjoint D size cells, while following should move. A direct communication via WIFI model is used a spanning tree of the resulting grid. The spiral STC covers between robots and their neighbors. The paper [11], introduce every subcell accessible from the starting point, and covers the ANTS (Ants Nearby Treasure Search) problem, in which these subcells in O(n) time using O(n) memory [20]. In this k identical agents, initially placed at some central location, new version of STC, the spanning tree is stored in the onboard collectively search for a treasure in a two-dimensional plane, memory, which results in a dependency of the search area on without any communication between them. A survey of online memory size. With the aim of resolving the memory problem, algorithms for searching and exploration in the plane is given Gabriely and Rimon propose in [21] the ant-like STC which in [34]. S-MASA is a simple search algorithm that uses forms the third version of the basic STC algorithm, that uses pheromones to guide the search process, agents are reactive markers on visited cells. D-STC is introduced in [21] to solve and do not need any memory. It can locate nearby targets as the problem of uncovered partially occupied 2D-size cells, fast as possible and at a rate that scales well with the number by visiting the previously uncovered cells, which results in of agents, so it operates as some animal species that search for worst-case scenarios, a twice coverage of the environment food around a central location, known as central place foraging area. A generalization of STC to multi-robots is given in [22], theory [35]. Table I gives a comparison between our algorithm the MSTC, in which a spanning tree is computed, and then and some of the related works according to the search process it is circumnavigated by each robot. Another spanning tree used. construction using multiple robots based on approximate cel- Even if chemical substances [36], electrical devices such lular decomposition is proposed in [23]. Another approach as Radio Frequency Identification Devices (RFIDs) [37] developed in [1], where the environment is subdivided into [38] [39] [40] are examples of real implementation of stig- n concentric discs, each disc is covered by one robot, when mergic communication in real world experiments, it is still the entire disc is completely covered, the robot move to the important to understand and improve pheromone-based algo- next disc not yet covered; an extension of this algorithm that rithms in simulations. By understanding the optimal conditions uses heterogeneous robots is given in [17]. Instead of focusing required for pheromone-based coordination, the real world on the on-board resources, some part of robotics literature use implementations can also be better directed [31]. a single ant or a group of robots to cover an area robustly, even if they do not have any memory, do not know the III. PROBLEM STATEMENT AND FORMULATION terrain, cannot maintain maps of the terrain, nor plan complete paths. They use environmental markers such as pebbles [24], In a collective multi-target search task, there are a lot of [25], [26] or pheromone like traces [27] or greedy navigation targets randomly distributed in an area. The agents (robots) strategies [28]. should find as fast as possible the targets and, after that, Whether we deal with coverage, multi-target search as remove them, if we deal with a cleanup task, or transport them foraging task, we need at the first stage to search the cor- to a nest, if we deal with a foraging task. In this paper, a new responding area. A search is defined as the action to look search algorithm is proposed that enables a group of agents, into the area carefully and thoroughly in an effort to find each with limited perception capabilities to search quickly or discover something [29]. In most search strategies based the targets. The algorithm presented here uses the principle on random walk, the agent tends to return to the same point of pheromone-based coordination where each agent deposits many times before finally wandering away, because it has no pheromone on its environment to inform the others about historical information about visited regions.
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