Dependency by Concentration of Pheromone Trail for Multiple Robots

Fujisawa, Ryusuke; Dobata, Shigeto; Kubota, Daisuke; Imamura, Hiroki; Matsuno, Fumitoshi

doi:10.1007/978-3-540-87527-7_28

Cited by 33 publications

(22 citation statements)

References 14 publications

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“…Other authors experiment with chemical pheromone traces, e.g. using alcohol (Fujisawa et al 2008;Sharpe and Webb 1999). In our system, one swarm of robots functions as pheromone for another swarm, in the form of mobile stigmergic markers.…”

Section: Related Workmentioning

confidence: 98%

“…In terms of the task to be solved, our work is related to research on self-organized foraging in swarm robotics, where robots need to optimize a path to follow back and forth between a source and a target (Fujisawa et al 2008;Garnier et al 2007;Panait and Luke 2004;Sharpe and Webb 1999;Sugawara et al 2004;Vaughan et al 2000;Wodrich and Bilchev 1997). All this work is inspired by pheromone guided foraging as observed in ant colonies.…”

Section: Related Workmentioning

confidence: 99%

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Self-organized cooperation between robotic swarms

et al. 2011

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We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eyebots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Self-organized cooperation between robotic swarms

et al. 2011

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“…Chemical trail-following strategies have been implemented with real robots. For example, ethanol trails were deposited and followed by the robots in Fujisawa et al [13]], but the use of decaying chemical trails by real robots can be problematic. Other robotic implementations of insectstyle pheromone trail following have instead used nonchemical substitutes for the trail chemicals.…”

Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

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“…The authors do not consider the possibility that a whole swarm of mobile robots guide each other's navigation, where each robot may be involved in a task of its own and is not dedicated to support the navigation of the others. Within the context of swarm robotics, most work on cooperative navigation is based on indirect stigmergic communication [5], [6], [7], [60], [61], [8], [62], [9], [10], [63], [11], [64], rather than on direct communication as in our algorithm. This approach is typically inspired by the behavior of certain types of ants, where individual ants mark their paths using a chemical substance, called pheromone, and follow these pheromone trails to find their way between the nest and a food source [13].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the system is vulnerable to failures of robots in the chain, making it less robust. Other approaches include the use of alcohol [60], [10], phosphorescent paint [63], or light encoding of pheromone using an overhead projector [62], [61], which are interesting, but are rather hard to detect and follow reliably or to implement in a general context. A general disadvantage of all these pheromone-inspired swarm navigation algorithms is that they crucially assume that all robots move between two targets.…”

Section: Related Workmentioning

confidence: 99%

Cooperative navigation in robotic swarms

Ducatelle

Förster

et al. 2013

Swarm Intell

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Abstract-We present a cooperative navigation algorithm for robotic swarms. Its purpose is to let a robot find a given target robot, while being guided by the other robots of the swarm. The system is based on wireless communication: the robots forward messages containing navigation information over the ad hoc network among them, and the searching robot uses this information to find its target. We study the algorithm in two different scenarios. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. We show that the communication based navigation system allows the robots of the swarm to guide the searching robot without the need to adapt their own movements. In the second scenario, we study collective navigation: all robots of the swarm need to navigate back and forth between two targets. We show that in this case, the proposed navigation algorithm gives rise to synergies in robot navigation, and lets the swarm self-organize into a robust dynamic structure. This selforganization improves navigation efficiency, and lets the swarm find shortest paths in cluttered environments. We test our system both in simulation and on real robots.

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Dependency by Concentration of Pheromone Trail for Multiple Robots

Cited by 33 publications

References 14 publications

Self-organized cooperation between robotic swarms

Self-organized cooperation between robotic swarms

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Cooperative navigation in robotic swarms

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