Information flow principles for plasticity in foraging robot swarms

Pitonakova, Lenka; Crowder, Richard; Bullock, Seth

doi:10.1007/s11721-016-0118-1

Cited by 42 publications

(49 citation statements)

References 66 publications

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“…We evaluate both the amount of energy needed to collect items and the number of items collected when robot energy is limited. We conclude with a discussion of how our results relate to our previous work on information flow in swarms (Pitonakova et al, 2016) and provide examples of real-world applications where the two types of self-regulated swarms could be used.…”

Section: Introductionmentioning

confidence: 65%

Task Allocation in Foraging Robot Swarms: The Role of Information Sharing

Pitonakova¹,

Crowder²,

Bullock³

2016

Proceedings of the Artificial Life Conference 2016

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Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: nonsocial, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms.

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Section: Introductionmentioning

confidence: 65%

Task Allocation in Foraging Robot Swarms: The Role of Information Sharing

Pitonakova¹,

Crowder²,

Bullock³

2016

Proceedings of the Artificial Life Conference 2016

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“…Better worksites were advertised in the base for a longer amount of time, while the regulation of information flow was achieved by allowing agents to choose randomly between advertised worksites, preventing all robots from adopting the same choice. In [20], we explored preferentially foraging bee-inspired swarms in dynamic environments where worksite locations remained the same, but where worksite utilities changed over time. The task of the swarms was to collect resource from the worksites into the base and to switch to a worksite with a higher utility when the environment changed.…”

Section: Discussionmentioning

confidence: 99%

“…The simulated MarXbots [1] were differentially steered circular robots with a radius of 8.5cm. The robots could communicate with each other using a range and bearing module with a signal range of 5 m. We have previously described the robot model in [20]. There were two types of robot swarm, that we parametrised for the best performance in a series of environments [22]: -Broadcaster (Figure 2a): Robots left the base immediately at the beginning of an experiment to start scouting for worksites.…”

Section: Robotsmentioning

confidence: 99%

“…In Bee swarms, robots exchange information in a designated area, that they return to periodically. Our previous work [20,22] suggests that Broadcaster swarms outperform Bee swarms in many foraging environments due to their ability to respond to environmental changes faster, but that Bee swarms are more suitable during central-place foraging in environments with a low worksite density, since their recruitment strategy allows the robots to share information with each other relatively quickly. Here we show that the rapid spread of information through Bee swarms damages their performance when individuals preferentially choose worksites with higher utility, since most of the swarm may tend to concentrate on a single worksite, increasing the negative effects of congestion.…”

Section: Introductionmentioning

confidence: 99%

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The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms

Pitonakova

Crowder

Bullock

2018

Lecture Notes in Computer Science

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Instead of committing to the first source of reward that it discovers, an agent engaged in "preferential foraging" continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.

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“…They use odometry to recognize their location and the food location. In Pitonakova et al (2016) authors propose a foraging algorithm inspired by foraging bees. The environment used is divided into two regions (one is a dancing floor and the other is an unloading area).…”

Section: Foraging Related Workmentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

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BackgroundSwarm intelligence provides the design and implementation of systems composed of many simple individuals who interact locally and produce remarkable behavior as a whole (Dudek et al. 1996). It provides multiple benefits such as robustness where the performance of the system is not affected significantly with the failure of individuals, simplicity of computational and perceptual capabilities of individuals but still allowing global complex behaviors and scalability of the control mechanism that does not depend on the number of agents (Mitton and Simplot-Ryl 2014). The application of swarm intelligence to collective robotics is identified as Swarm Robotics in El Zoghby et al. (2014). Many artificial systems such as distributed computing systems and artificial intelligence systems are characterized by complex behaviors that emerge as a result of the nonlinear spatio-temporal interactions among a large number of system components at different levels of organization. These systems are known as Complex Adaptive Systems (CAS) as stated by Lansing (2003). Holland (2006) also considers CAS as dynamic systems able to adapt in and evolve with a changing environment. MAF problem is a benchmark problem for swarm robotics. It can be seen as a CAS and defined like in Niazi and Hussain Abstract Background: The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.Results: First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems Conclusions:Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic. Zedadra et al. Complex Adapt Syst Model (2017) et al. Complex Adapt Syst Model (2017) 5:3 (2012) as a system made up of multiple simple individuals which interact in a nonlinear fashion, thereby giving rise to global and often unpredictable behaviors. A good way to understand a CAS is to study them in special cases, thus to simulate dedicated behavior from particular perspectives. Holland (2006) states that the analysis of CAS is done through a combination of applied, theoretical and experimental methods (e.g. mathematics and computer simulations). Authors in Fortino and North (2013) state that Agent-Based Modeling (ABM) has proven to provide an effective set of tools for modeling and simulating different ...

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Information flow principles for plasticity in foraging robot swarms

Cited by 42 publications

References 66 publications

Task Allocation in Foraging Robot Swarms: The Role of Information Sharing

Task Allocation in Foraging Robot Swarms: The Role of Information Sharing

The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms

Multi-Agent Foraging: state-of-the-art and research challenges

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