MRSAM: a quadratically competitive multi-robot online navigation algorithm

Sarid, Shahar; Shapiro, Amir; Gabriely, Y.

doi:10.1109/robot.2006.1642109

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Another spanning tree construction using multiple robots based on approximate cellular decomposition is proposed in [23]. Another approach developed in [1], where the environment is subdivided into n concentric discs, each disc is covered by one robot, when the entire disc is completely covered, the robot move to the next disc not yet covered; an extension of this algorithm that uses heterogeneous robots is given in [17]. Instead of focusing on the on-board resources, some part of robotics literature use 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 terrain, cannot maintain maps of the terrain, nor plan complete paths.…”

Section: Related Workmentioning

confidence: 99%

“…T HE PROBLEM of finding multiple targets whose positions are unknown without a prior information about the environment is very important in many real world applications [1]. Those applications vary from mine detecting [2] [3], search in damaged buildings [4] [5], fire fighting [6], and exploration of spaces [7] [8], where neither a map, nor a Global Positioning System (GPS) are available [9].…”

Section: Introductionmentioning

confidence: 99%

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Stigmergic MASA: A Stigmergy Based Algorithm for Multi-Target Search

Zedadra¹,

Séridi²,

Jouandeau³

et al. 2014

Annals of Computer Science and Information Systems

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Abstract-We explore the on-line problem of coverage where multiple agents have to find a target whose position is unknown, and without a prior global information about the environment. In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based on the principle of pheromone-based communication. According to this algorithm, called S-MASA (Stigmergic Multi Ant Search Area), the agents search nearby their base incrementally using turns around their center and around each other, until the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via depositing/detecting markers. This work improves the search performance in comparison with random walk and S-random walk (stigmergic random walk) strategies, we show the obtained results using computer simulations.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stigmergic MASA: A Stigmergy Based Algorithm for Multi-Target Search

Zedadra¹,

Séridi²,

Jouandeau³

et al. 2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…The solution presented in this paper is complete and robust and can be decentralized or centralized, depending on the setup communication. 2 MRSAM algorithm appeared first in [26], and here its detailed performance analysis and the simulation results are introduced for the first time.…”

Section: Introductionmentioning

confidence: 99%

Classifying the multi robot path finding problem into a quadratic competitive complexity class

Sarid

Shapiro

2008

Ann Math Artif Intell

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We explore two motion planning problems where a group of mobile robots has to reach a target located in an a priori unknown environment while on-line planning the next step. In the first problem the target position is unknown and should be found by the robots, while in the second problem the target position is known and only a path to it should be found. We focus on optimizing the cost of the task in terms of motion time, which, under the assumption of uniform velocity of all the robots, correlates to the path length passed by the robot which reaches the target. The performance of an on-line algorithm is usually expressed in terms of Competitiveness, the constant ratio between the on-line and the optimal off-line solutions. Specifically, the ratio between the lengths of the actual path made by the robot which reached the target to the shortest path to the target. We use generalized competitiveness, i.e., the ratio is not necessarily constant, but could be any function. Classification of a motion planning task in the sense of performance is done by finding an upper and a lower bounds on the competitiveness of all algorithms solving that task. If the two bounds belong to the same functional class this is the Competitive Complexity Class of the task. We find the two bounds for the aforementioned common on-line motion planning problems, and classify them into competitive classes. It is shown that in general any on-line motion planning algorithm that tries to solve these problems must have at least a quadratic competitive performance. This is a lower bound of 170 S. Sarid, A. Shapiro the problems. This paper describes two new on-line navigation algorithm which solve the problems under discussion. The first is called MRSAM, short for MultiRobot Search Area Multiplication, and the second is called MRBUG, short for MultiRobot BUG which extends Lumelsky famous BUG algorithm. Both algorithms have quadratic upper bounds, which prove that the problems they solve have quadratic upper bounds. Thus it is shown that navigation in an unknown environment by a group of robots belongs to a quadratic competitive class. MRSAM and MRBUG have a quadratic competitive performance and thus have optimal competitiveness. The algorithms' performance is simulated in office-like environments.

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