Performance of digital pheromones for swarming vehicle control

Sauter, John; Matthews, Robert; Parunak, H. Van Dyke; Brueckner, Sven

doi:10.1145/1082473.1082610

Cited by 126 publications

(69 citation statements)

References 8 publications

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“…in infrastructures for military air operations. The digital pheremone infrastructure [23] provides services for injecting and perceiving digital pheromones in different sites of the physical/logical distributed environment, with the inner ability of aggregating, maintaining and diffusing information according to spatial and temporal criteria-this idea is inspired by stigmergy [17], similarly to the work on TOTA co-fields. In the military context these features can be exploited for several purposes such as surveillance and patrol, target acquisition, and target tracking.…”

Section: Digital Pheromone Infrastructurementioning

confidence: 99%

Infrastructures for the environment of multiagent systems

Viroli

Holvoet

Ricci

et al. 2006

Auton Agent Multi-Agent Syst

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The notion of environment is receiving an increasing attention in the development of multiagent applications. This is witnessed by the emergence of a number of infrastructures providing agent designers with useful means to develop the agent environment, and thus to structure an effective multiagent application. In this paper we analyse the role and features of such infrastructures, and survey some relevant examples. We endorse a general viewpoint where the environment of a multiagent system is seen as a set of basic bricks we call environment abstractions, which (i) provide agents with services useful for achieving individual and social goals, and (ii) are supported by some underlying software infrastructure managing their creation and exploitation. Accordingly, we focus the survey on the opportunities that environment infrastructures provide to system designers when developing multiagent applications.

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Section: Digital Pheromone Infrastructurementioning

confidence: 99%

Infrastructures for the environment of multiagent systems

Viroli

Holvoet

Ricci

et al. 2006

Auton Agent Multi-Agent Syst

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“…Existing aerial robotic swarms use relative or global positioning information to navigate in their environment using either map-based strategies (Kuiper and NadjmTehrani, 2006;Parunak et al, 2005;Sauter et al, 2005;Elston and Frew, 2008;Flint et al, 2002;Lawrence et al, 2004;Pack and York, 2005;Yang et al, 2005), Reynolds' Flocking (Reynolds, 1987) or Artificial Physics (Spears et al, 2005) approaches (Basu et al, 2004;De Nardi and Holland, 2007;Holland et al, 2005;Kadrovach and Lamont, 2001;Merino et al, 2006), or predefined swarm formations (Vincent and Rubin, 2004). Other researchers have explored the use of artificial evolution to automatically determine position-aware swarm controllers (Gaudiano et al, 2005;Lin et al, 2004;Richards et al, 2005;Soto and Lin, 2005;Wu et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Evolved swarming without positioning information: an application in aerial communication relay

2008

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In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy.This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.

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“…In the recent past, several computational systems using swarming have been developed for different applications including vehicle routing [16], self-repairing formation control for mobile agents [23], adaptive control in overlay networks [3], and for military applications [5,19,21]. Most of these approaches do not address the task selection problem in swarming as a independent issue and use straightforward approaches such as greedy algorithms within a centralized shared memory setting [9,19] that facilitates rapid information exchange between the swarm units. In contrast, in this paper we describe different heuristic-based strategies in a distributed task selection model for swarming.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we focus on the task selection mechanism used by the individual swarm units to process the tasks in the environment. Previous researchers [9,19] have addressed this problem using centralized task allocation algorithms where information about tasks are shared between the swarm units using shared memory-based techniques. In contrast, ensuring efficient task selection by the swarm units becomes a challenging problem in a distributed setting because of the dynamic nature of the environment, differences in characteristics of the swarm units and possible inconsistencies in information between the different swarm units.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Task Selection in Multi-agent Based Swarms Using Heuristic Strategies

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Abstract. Swarm-based systems have emerged as an attractive paradigm for implementing distributed autonomous systems for various applications in commercial, military and business domains. One of the major operations in a swarm-based system is to ensure that the individual swarm units process the tasks in the environment in an efficient manner. This can be achieved using a suitable task selection mechanism that allocates the desired number of swarm units to each task while reducing inter-task latencies and communication overhead, and, ensuring adequate commitment of resources to tasks. In this paper, we describe a multi-agent based distributed task selection mechanism for swarm-based systems. We show that the distributed task selection problem is NPcomplete and propose polynomial-time heuristic-based algorithms. Our simulation results show that heuristics in which each swarm unit considers both the effects of other swarm units on tasks and its own relative position to other swarm units achieve better task processing efficiency and improved distribution of swarm units over tasks.

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Performance of digital pheromones for swarming vehicle control

Cited by 126 publications

References 8 publications

Infrastructures for the environment of multiagent systems

Infrastructures for the environment of multiagent systems

Evolved swarming without positioning information: an application in aerial communication relay

Distributed Task Selection in Multi-agent Based Swarms Using Heuristic Strategies

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