On-line Evolution of Foraging Behaviour in a Population of Real Robots

Heinerman, Jacqueline; Zonta, Alessandro; Haasdijk, Evert; Eiben, A. E.

doi:10.1007/978-3-319-31153-1_14

Cited by 15 publications

(16 citation statements)

References 8 publications

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“…Also, Smithers (1995) initiated an attempt to quantify the robot behaviour. It is also worth noting that given the right conventional controller, robots can benefit from each others knowledge, however only in a population of fixed morphologies (Heinerman et al, 2016;Haasdijk et al, 2012;Matari, 1997). Here, we attempt to quantify and set the standard for a provisional set of morphologies.…”

Section: Related Workmentioning

confidence: 99%

Analysing the Relative Importance of Robot Brains and Bodies

Jelisavcic

Roijers

Eiben

2018

The 2018 Conference on Artificial Life

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The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain highquality robot designs, but also a process that results in many body-brain-fitness data points. Inspired by this perspective, in this paper we investigate the relative importance of robot bodies and brains for a good fitness. We introduce a method to isolate and quantify the effect of the bodies and brains on the quality of the robots and perform a case study. The method is general in that it is not restricted to evolutionary systems. For the case study, we use a system of modular robots, where the bodies are evolvable and the brains are evolvable and learnable. These case studies validate the usefulness of our method and deliver interesting insights into the interplay between bodies and brains in evolutionary robotics.

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Section: Related Workmentioning

confidence: 99%

Analysing the Relative Importance of Robot Brains and Bodies

Jelisavcic

Roijers

Eiben

2018

The 2018 Conference on Artificial Life

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“…For a single robot controller, the task of phototaxis together with obstacle avoidance thus becomes non-trivial. Similar to online evolutionary systems presented in [11], the objective function used herein rewards behaviours (Ab) for each of the sub-goals achieved. The sub-goals herein include motion of the robot towards the light source and avoiding obstacles.…”

Section: Scenariosmentioning

confidence: 99%

On an immuno-inspired distributed, embodied action-evolution cum selection algorithm

Semwal

Kulkarni

Nair

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Traditional Evolutionary Robotics (ER) employs evolutionary techniques to search for a single monolithic controller which can aid a robot to learn a desired task. These techniques suffer from bootstrap and deception issues when the tasks are complex for a single controller to learn. Behaviour-decomposition techniques have been used to divide a task into multiple subtasks and evolve separate subcontrollers for each subtask. However, these subcontrollers and the associated subcontroller arbitrator(s) are all evolved off-line. A distributed, fully embodied and evolutionary version of such approaches will greatly aid online learning and help reduce the reality gap. In this paper, we propose an immunology-inspired embodied action-evolution cum selection algorithm that can cater to distributed ER. This algorithm evolves different subcontrollers for different portions of the search space in a distributed manner just as antibodies are evolved and primed for different antigens in the antigenic space. Experimentation on a collective of real robots embodied with the algorithm showed that a repertoire of antibody-like subcontrollers was created, evolved and shared on-the-fly to cope up with different environmental conditions. In addition, instead of the conventionally used approach of broadcasting for sharing, we present an Intelligent Packet Migration scheme that reduces energy consumption.

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“…Authors in Heinerman et al (2016) investigate the ability of limited number robots to perform a foraging behavior in a limited time. Robots use a feed forward neural network controller.…”

Section: Foraging Related Workmentioning

confidence: 99%

“…The difference between them makes the comparison of efficiency of MAF algorithms hard to do. Heinerman et al (2016): (1) declare that when considering multiple robots with sophisticated sensors such as cameras, simulations may actually run slower than real time, even for a group of small robots and (2) consider that experimenting with real robots encourages the researcher to review the robot's actual behavior during the experiments rather than allowing only post-facto analysis of the metrics gathered by unattended simulation runs. Thus experimenting with real robots enhances the understanding of robot behavior.…”

Section: Real Robotic Implementation Of Foraging Agentsmentioning

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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On-line Evolution of Foraging Behaviour in a Population of Real Robots

Cited by 15 publications

References 8 publications

Analysing the Relative Importance of Robot Brains and Bodies

Analysing the Relative Importance of Robot Brains and Bodies

On an immuno-inspired distributed, embodied action-evolution cum selection algorithm

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

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