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Mauro Birattari - One of the best experts on this subject based on the ideXlab platform.
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The TAM: abstracting complex tasks in Swarm Robotics research
Swarm Intelligence, 2015Co-Authors: Arne Brutschy, Marco Brambilla, Marco Dorigo, Giovanni Pini, Lorenzo Garattoni, Gianpiero Francesca, Mauro BirattariAbstract:Research in Swarm Robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations—no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in Swarm Robotics research. This approach is based on a physical device called the “task abstraction module” (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck—research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a Swarm of e-puck robots.
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a Swarm Robotics approach to task allocation under soft deadlines and negligible switching costs
Simulation of Adaptive Behavior, 2014Co-Authors: Yara Khaluf, Mauro Birattari, Heiko HamannAbstract:Developing Swarm Robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows Swarm Robotics systems to execute tasks characterized by soft deadlines and to minimize the costs associated with missing the task deadlines.
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A Swarm Robotics approach to task allocation under soft deadlines and negligible switching costs
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014Co-Authors: Yara Khaluf, Mauro Birattari, Heiko HamannAbstract:Developing Swarm Robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows Swarm Robotics systems to execute tasks characterized by soft deadlines and to minimize the costs associated with missing the task deadlines. © 2014 Springer International Publishing Switzerland.
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Swarm Robotics
Scholarpedia, 2014Co-Authors: Marco Dorigo, Mauro Birattari, Marco BrambillaAbstract:Swarm Robotics is a novel approach to the coordination of large numbers of robots and has emerged as the application of Swarm intelligence to multi-robot systems. Different from other Swarm intelligence studies, Swarm Robotics puts emphases on the physical embodiment of individuals and realistic interactions among the individuals and between the individuals and the environment. In this chapter, we present a brief review of this new approach. We first present its definition, discuss the main motivations behind the approach, as well as its distinguishing characteristics and major coordination mechanisms. Then we present a brief review of Swarm Robotics research along four axes; namely design, modelling and analysis, robots and problems.
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on the use of bio pepa for modelling and analysing collective behaviours in Swarm Robotics
Swarm Intelligence, 2013Co-Authors: Mieke Massink, Diego Latella, Marco Brambilla, Marco Dorigo, Mauro BirattariAbstract:In this paper we analyse a Swarm Robotics system using Bio-PEPA. Bio-PEPA is a process algebra language originally developed to analyse biochemical systems. A Swarm Robotics system can be analysed at two levels: the macroscopic level, to study the collective behaviour of the system, and the microscopic level, to study the robot-to-robot and robot-to- environment interactions. In general, multiple models are necessary to analyse a system at different levels. However, developing multiple models increases the effort needed to analyse a system and raises issues about the consistency of the results. Bio-PEPA, instead, allows the researcher to perform stochastic simulation, fluid flow (ODE) analysis and statistical model checking using a single description, reducing the effort necessary to perform the analysis and ensuring consistency between the results. Bio-PEPA is well suited for Swarm Robotics systems: by using Bio-PEPA it is possible to model distributed systems and their space- time characteristics in a natural way. We validate our approach by modelling a collective decision-making behaviour.
Marco Dorigo - One of the best experts on this subject based on the ideXlab platform.
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managing byzantine robots via blockchain technology in a Swarm Robotics collective decision making scenario
Adaptive Agents and Multi-Agents Systems, 2018Co-Authors: Volker Strobel, Eduardo Castello Ferrer, Marco DorigoAbstract:While Swarm Robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots---i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current Swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in Swarm Robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure Swarm coordination mechanisms and to identify and exclude Byzantine Swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the Swarm.
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The TAM: abstracting complex tasks in Swarm Robotics research
Swarm Intelligence, 2015Co-Authors: Arne Brutschy, Marco Brambilla, Marco Dorigo, Giovanni Pini, Lorenzo Garattoni, Gianpiero Francesca, Mauro BirattariAbstract:Research in Swarm Robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations—no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in Swarm Robotics research. This approach is based on a physical device called the “task abstraction module” (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck—research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a Swarm of e-puck robots.
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Swarm Robotics
Scholarpedia, 2014Co-Authors: Marco Dorigo, Mauro Birattari, Marco BrambillaAbstract:Swarm Robotics is a novel approach to the coordination of large numbers of robots and has emerged as the application of Swarm intelligence to multi-robot systems. Different from other Swarm intelligence studies, Swarm Robotics puts emphases on the physical embodiment of individuals and realistic interactions among the individuals and between the individuals and the environment. In this chapter, we present a brief review of this new approach. We first present its definition, discuss the main motivations behind the approach, as well as its distinguishing characteristics and major coordination mechanisms. Then we present a brief review of Swarm Robotics research along four axes; namely design, modelling and analysis, robots and problems.
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evolutionary Swarm Robotics a theoretical and methodological itinerary from individual neuro controllers to collective behaviour
2014Co-Authors: Vito Trianni, Elio Tuci, C Ampatzis, Marco DorigoAbstract:This chapter contains sections titled: 7.1 Introduction, 7.2 Swarm Robotics and the Swarm-bots, 7.3 Experiments, 7.4 Discussion, Note, References
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on the use of bio pepa for modelling and analysing collective behaviours in Swarm Robotics
Swarm Intelligence, 2013Co-Authors: Mieke Massink, Diego Latella, Marco Brambilla, Marco Dorigo, Mauro BirattariAbstract:In this paper we analyse a Swarm Robotics system using Bio-PEPA. Bio-PEPA is a process algebra language originally developed to analyse biochemical systems. A Swarm Robotics system can be analysed at two levels: the macroscopic level, to study the collective behaviour of the system, and the microscopic level, to study the robot-to-robot and robot-to- environment interactions. In general, multiple models are necessary to analyse a system at different levels. However, developing multiple models increases the effort needed to analyse a system and raises issues about the consistency of the results. Bio-PEPA, instead, allows the researcher to perform stochastic simulation, fluid flow (ODE) analysis and statistical model checking using a single description, reducing the effort necessary to perform the analysis and ensuring consistency between the results. Bio-PEPA is well suited for Swarm Robotics systems: by using Bio-PEPA it is possible to model distributed systems and their space- time characteristics in a natural way. We validate our approach by modelling a collective decision-making behaviour.
Erol Şahin - One of the best experts on this subject based on the ideXlab platform.
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Swarm Robotics: From Sources of Inspiration to Domains of Application
2010Co-Authors: Erol ŞahinAbstract:Swarm Robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerging approach by 1) describing the desirable properties of Swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term Swarm Robotics, and putting forward a set of criteria that can be used to distinguish Swarm Robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of Swarm robotic systems.
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special issue on Swarm Robotics
Swarm Intelligence, 2008Co-Authors: Erol Şahin, Alan WinfieldAbstract:Swarm Robotics is a new approach to the coordination of multi-robot systems. In contrast with traditional multi-robot systems which use centralised or hierarchical control and communication systems in order to coordinate robots’ behaviours, Swarm Robotics adopts a decentralised approach in which the desired collective behaviours emerge from the local interactions between robots and their environment. Such emergent or self-organised collective behaviours are inspired by, and in some cases modelled on, the Swarm intelligence observed in social insects. The potential for Swarm Robotics is considerable. Any task in which physically distributed objects need to be explored, surveyed, collected, harvested, rescued, or assembled into structures is a potential real-world application for Swarm Robotics. The key advantage of the Swarm Robotics approach is robustness, which manifests itself in a number of ways. Firstly, because a Swarm of robots consists of a number of relatively simple and typically homogeneous robots, which are not pre-assigned to specific roles or tasks within the Swarm, then the Swarm can self-organise or dynamically re-organise the way individual robots are deployed. Secondly, and for the same reasons, the Swarm approach is highly tolerant to the failure of individual robots. Thirdly, the fact that control is completely decentralised means that there is no common-mode failure point or vulnerability in the Swarm. Indeed, it could be said that the high level of robustness evident in robotic Swarms comes for free in the sense that it is intrinsic to the Swarm Robotics approach, which contrasts with the high engineering cost of fault tolerance in conventional robotic systems. The realisation of the potential of Swarm Robotics requires the solution of a number of very challenging problems. Firstly, in algorithm design: Swarm roboticists face the problem of designing both the physical morphology and behaviours of the individual robots such that when those robots interact with each other and their environment, the desired overall collective behaviours will emerge. At present there are no principled approaches to the design of low-level behaviours for a given desired collective behaviour. Secondly, in implementation
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Special issue on Swarm Robotics
Swarm Intelligence, 2008Co-Authors: Erol Şahin, Alan WinfieldAbstract:Swarm Robotics is a new approach to the coordination of multi-robot systems. In contrast with traditional multi-robot systems which use centralised or hierarchical control and com-munication systems in order to coordinate robots' behaviours, Swarm Robotics adopts a de-centralised approach in which the desired collective behaviours emerge from the local in-teractions between robots and their environment. Such emergent or self-organised collective behaviours are inspired by, and in some cases modelled on, the Swarm intelligence observed in social insects. The potential for Swarm Robotics is considerable. Any task in which physically distrib-uted objects need to be explored, surveyed, collected, harvested, rescued, or assembled into structures is a potential real-world application for Swarm Robotics. The key advantage of the Swarm Robotics approach is robustness, which manifests itself in a number of ways. Firstly, because a Swarm of robots consists of a number of relatively simple and typically homoge-neous robots, which are not pre-assigned to specific roles or tasks within the Swarm, then the Swarm can self-organise or dynamically re-organise the way individual robots are deployed. Secondly, and for the same reasons, the Swarm approach is highly tolerant to the failure of individual robots. Thirdly, the fact that control is completely decentralised means that there is no common-mode failure point or vulnerability in the Swarm. Indeed, it could be said that the high level of robustness evident in robotic Swarms comes for free in the sense that it is intrinsic to the Swarm Robotics approach, which contrasts with the high engineering cost of fault tolerance in conventional robotic systems. The realisation of the potential of Swarm Robotics requires the solution of a number of very challenging problems. Firstly, in algorithm design: Swarm roboticists face the problem of designing both the physical morphology and behaviours of the individual robots such that when those robots interact with each other and their environment, the desired overall collec-tive behaviours will emerge. At present there are no principled approaches to the design of low-level behaviours for a given desired collective behaviour. Secondly, in implementation Swarm Intell (2008) 2: 69–72 and test: to build and rigorously test a Swarm of robots in the laboratory requires a con-siderable experimental infrastructure. Real-robot experiments thus typically proceed hand-in-hand with simulation and good tools are essential. Thirdly, in analysis and modelling: a robotic Swarm is typically a stochastic, non-linear system and constructing mathematical models for both validation and parameter optimisation is challenging. Such models would surely be an essential part of constructing a safety argument for real-world applications. There are, at the time of writing, no known real-world applications of Swarm Robotics and, given the challenges outlined above, this is perhaps not surprising. However, as the papers of this special issue demonstrate, the field of Swarm Robotics is developing very strongly, and we predict that real-world applications of Swarm robotic systems will emerge in the near future. A total of seventeen papers were submitted to this special issue and, following a rigorous process of anonymous review, eight have been selected for publication. The papers included in this special issue cover a broad spectrum of the challenges outlined above, from Design and Algorithms including self-assembly, self-organised flocking, self-organised distribution, evolutionary Robotics and – with an applications focus – Swarming of Micro Air Vehicles for communications relay. One paper focuses on a new generation of simulation tool, and two papers on new approaches to mathematical modelling and analysis. Outlined below, the eight papers of this special issue strongly represent the state-of-the-art in this vibrant field of research. Design and algorithms
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A review of studies in Swarm Robotics
Turkish Journal of Electrical Engineering and Computer Sciences, 2007Co-Authors: Levent Bayindir, Erol ŞahinAbstract:Swarm Robotics is a new approach to the coordination of large numbers of relatively simple robots. The approach takes its inspiration from the system-level functioning of social insects which demonstrate three desired characteristics for multi-robot systems: robustness, flexibility and scalability. In this paper we have presented a preliminary taxonomy for Swarm Robotics and classified existing studies into this taxonomy after investigating the existing surveys related to Swarm Robotics literature. Our parent taxonomic units are modeling, behavior design, communication, analytical studies and problems. We are classifying existing studies into these main axes. Since existing reviews do not have enough number of studies reviewed or do have less numbers of or less appropriate categories, we believe that this review will be helpful for Swarm Robotics researchers.
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Swarm Robotics : From Sources of Inspiration
Swarm robotics workshop: state-of-the-art survey, 2005Co-Authors: Erol ŞahinAbstract:Swarm Robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerging approach by 1) describing the desirable properties of Swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term Swarm Robotics, and putting forward a set of criteria that can be used to distinguish Swarm Robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of Swarm robotic systems.
Heiko Hamann - One of the best experts on this subject based on the ideXlab platform.
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Fundamentals of Swarm Robotics - An Interdisciplinary Approach
Cognitive Systems Monographs, 2020Co-Authors: Heiko HamannAbstract:In the following we give definitions for concepts that are fundamental in Swarm Robotics. These concepts will be used throughout this work.
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Scenarios of Swarm Robotics
Swarm Robotics: A Formal Approach, 2018Co-Authors: Heiko HamannAbstract:We do an extensive check of what typical scenarios of Swarm Robotics have been investigated and what methods have been published.This is an extensive guide through the literature on Swarm Robotics. It is structured by the investigated scenarios and starts from tasks of low complexity, such as aggregation and dispersion. A discussion of pattern formation, object clustering, sorting, and self-assembly follows. Collective construction is already a rather complex scenario that combines several subtasks, such as collective decision-making and collective transport. We take the example of collective manipulation to discuss the interesting phenomenon of super-linear performance increase. Not only the Swarm performance increases with increasing Swarm size but even the individual robot’s efficiency. Flocking, collective motion, foraging, and shepherding are discussed as typical examples of Swarm behaviors. Bio-hybrid systems as combinations of robots and living organisms are quickly introduced. We conclude with a discussion of what could arguably be called “Swarm Robotics 2.0”—a few recent very promising approaches, such as error detection, security, Swarms as interfaces, and Swarm Robotics as field Robotics.
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a Swarm Robotics approach to task allocation under soft deadlines and negligible switching costs
Simulation of Adaptive Behavior, 2014Co-Authors: Yara Khaluf, Mauro Birattari, Heiko HamannAbstract:Developing Swarm Robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows Swarm Robotics systems to execute tasks characterized by soft deadlines and to minimize the costs associated with missing the task deadlines.
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A Swarm Robotics approach to task allocation under soft deadlines and negligible switching costs
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014Co-Authors: Yara Khaluf, Mauro Birattari, Heiko HamannAbstract:Developing Swarm Robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows Swarm Robotics systems to execute tasks characterized by soft deadlines and to minimize the costs associated with missing the task deadlines. © 2014 Springer International Publishing Switzerland.
Eliseo Ferrante - One of the best experts on this subject based on the ideXlab platform.
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Swarm Robotics: A review from the Swarm engineering perspective
Swarm Intelligence, 2013Co-Authors: Marco Brambilla, Eliseo Ferrante, Mauro Birattari, Marco DorigoAbstract:Swarm Robotics is an approach to collective Robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, Swarm Robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of Swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of Swarm Robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a Swarm Robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of Swarm Robotics as an engineering discipline and with suggestions for future research directions.
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evolution of task partitioning in Swarm Robotics
European Conference on Artificial Life, 2013Co-Authors: Eliseo Ferrante, Edgar A Duenezguzman, Ali Emre Turgut, Tom WenseleersAbstract:Task-partitioning refers to the process whereby a task is divided into two or more sub-tasks. Through task partitioning both efficiency and effectiveness can be improved provided the right environmental conditions. We synthesize self-organized task partitioning behaviors for a Swarm of mobile robots using artificial evolution. Through validation experiments, we show that the synthesized behaviors exploits behavioral specialization despite being based on homogeneous individual behaviors. Introduction Social insects exhibit astonishing levels of social organization (Wilson, 1971). One of the organizational paradigms used by social insects is division of labour, whereby they perform complex tasks by having parts of the colonies specializing into sub-tasks (Wilson, 1971). Two key concepts are fundamental in division of labour: task partitioning, which is the process whereby individuals divide a complex task into simpler sub-tasks (Ratnieks and Anderson, 1999); and task allocation, whereby individuals specialize to perform one among these sub-tasks. We study task partitioning in the context of Swarm Robotics. Swarm Robotics aims at designing collective behaviors for Swarms of autonomous robots based on selforganization and Swarm intelligence principles rather than on centralized or global coordination (Brambilla et al., 2013). Through task partitioning, Swarm Robotics systems can increase flexibility and performance, better exploit specialization and reduce interference (Pini et al., 2013). Differently from existing work mainly realized through hand-coded design methods (Pini et al., 2013), in this paper we use evolutionary Swarm Robotics methods (Trianni, 2008). We tackle a foraging task, a classical benchmark problem in Swarm Robotics (Brambilla et al., 2013). We show that the Swarm can evolve to self-organize into task partitioning in the considered environment. Evolutionary method and experimental setup We use GESwarm (Ferrante et al., 2013), which allows the synthesis of readable and reverse engineerable individual behaviors that self-organize into the desired collective behaviors. GESwarm is based on Grammatical Evolution (GE)(O’Neill and Ryan, 2003) and uses a grammar that can express a rich variety of Swarm Robotics behaviors. GESwarm automatically combines existing low-level individual behaviors into more complex strategies, and produces a set of readable rules that switch between low-level behaviors in response to internal or external stimuli. We consider foraging in the environment shown in Figure 1a. Robots need to collect items from a region that we call source area and bring them to a region that we call nest area. In the source, 5 objects are present and are replaced each time robots pick them up. A light source is placed far beyond the source. The light allows the robots to navigate using their low-level behaviors: they perform phototaxis to go towards the source and anti-phototaxis to go towards the nest. Robots can also do random walk. An obstacle avoidance behavior is present and always active. The source is connected to the nest through a slope area and a cache area. Robots climbing the slope upwards navigate at a reduced speed. Items dropped on the slope slide down, stopping at the cache. Existing items in the cache can be pushed further down by new sliding items. Thus the cache can filled almost uniformly when many objects are dropped on the slope. We use GEVA for grammatical evolution. We execute 10 evolutionary runs consisting of 2000 generations and 100 individuals. Individuals are individual behaviors executed by all robots in each Swarm. Each collective behavior produced by these individual behaviors is evaluated 3 times in a Swarm of 4 robots. We use a single-point crossover with probability 0.3 and a mutation probability of 0.05. We choose a generational-type of replacement with 5% elitism and a roulette-wheel selection mechanism. The fitness function in the initial part of the evolution is the number of items collected by the best robot (fitness A). This allows for neutral variation: evolution can explore solutions based on behavioral specializing without a detrimental effect on the fitness, producing a smoother fitness landscape. In the final phase of the evolution, the fitness is set to the total number of collected items (fitness B), the actual objective GEVA homepage, http://ncra.ucd.ie/Site/GEVA.html
