Towards a reliable set-up for bio-inspired collective experiments with real robots

Martinoli, Alcherio; Franzi, E.; Matthey, O.

doi:10.1007/bfb0112995

Cited by 14 publications

(10 citation statements)

References 10 publications

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“…8a shows the experimental results obtained using a special tool developed at our laboratory to extend the autonomy of the Khepera robots (Martinoli et al, 1997). Although a single experiment replicated only three times can not be representative, the figure shows that after 3000 seconds the team fitness becomes saturated due to interferences.…”

Section: Resultsmentioning

confidence: 99%

Probabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots

Martinoli¹,

Mondada

1998

Distributed Autonomous Robotic Systems 3

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Abstract. This paper describes the implementation and modelling of a biologically inspired collective behaviour. The experiments are concerned with the gathering and clustering of randomly distributed small cylinders. Each experiment has been repeated ten times in a simulated environment (parametric simulation) and carried out five times with a group of ten Khepera miniature mobile robots. The simulated and experimental results are compared, quantified and discussed showing the advantages and the weaknesses of both approaches.

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Section: Resultsmentioning

confidence: 99%

Probabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots

Martinoli¹,

Mondada

1998

Distributed Autonomous Robotic Systems 3

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“…For example, in our experiments, we use an electrified floor to provide power to our robots. While building our floor, we learned of two other research groups that have built floors of similar construction [28,29]. These parallel achievements attest to the viability and utility of this power supply approach.…”

Section: Powermentioning

confidence: 88%

“…The scalability of ETP using a centralized evolutionary algorithm is limited, as is that of any centralized control system. For example, centralized coordination of evaluation and reproduction can potentially result in communication bottlenecks [28] as the number of robots increases. Hence, we aspire to decentralization in the adaptive mechanism in the same way that Brooks aspires to decentralization in the control of individual robots.…”

Section: Embodied Evolution: the Transition From Etp To Scalable Etpmentioning

confidence: 99%

Embodied Evolution: Distributing an evolutionary algorithm in a population of robots

Watson

Ficici

Pollack

2002

Robotics and Autonomous Systems

222

164

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We introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical robots. Several issues identified by researchers in the evolutionary robotics community as problematic for the development of ER are alleviated by the use of a large number of robots being evaluated in parallel. Particularly, EE avoids the pitfalls of the simulate-and-transfer method and allows the speed-up of evaluation time by utilizing parallelism. The more novel features of EE are that the evolutionary algorithm is entirely decentralized, which makes it inherently scalable to large numbers of robots, and that it uses many robots in a shared task environment, which makes it an interesting platform for future work in collective robotics and Artificial Life. We have built a population of eight robots and successfully implemented the first example of Embodied Evolution by designing a fully decentralized, asynchronous evolutionary algorithm. Controllers evolved by EE outperform a hand-designed controller in a simple application. We introduce our approach and its motivations, detail our implementation and initial results, and discuss the advantages and limitations of EE.

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“…The experiments are only carried out with Webots and with the probabilistic model, but are based on the IrDA communication turrets developed for the Khepera robots (Martinoli, Franzi, & Matthey, 1997). These turrets allow local communication through 4 directional IR emitters and receivers, separated by angles of 90 degrees.…”

Section: Communicationmentioning

confidence: 99%

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et al. 2001

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This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground -an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical setup composed of groups of 2 to 6 Khepera robots, and in Webots, a 3D simulator of Khepera robots.The results using these two implementations are compared with the predictions of a probabilistic modelling methodology (Martinoli, Ijspeert, & Mondada, 1999a;Martinoli, Ijspeert, & Gambardella, 1999b) which is here extended for the characterization and the prediction of a collaborative manipulation experiment. Instead of computing trajectories and sensory information, the probabilistic model represents the collaboration dynamics as a set of stochastic events based on simple geometrical considerations. It is shown that the probabilistic model qualitatively and quantitatively predicts the collaboration dynamics. It is significantly faster than a traditional sensor-based simulator such as Webots, and its minimal set of parameters allows the experimenter to better identify the effect of characteristics of individual robots on the team performance.Using these three implementations (the real robots, Webots and the probabilistic model), we make a quantitative investigation of the influence of the number of workers (i.e robots) and of the primary parameter of the robots' controller -the gripping time parameter-on the collaboration rate, i.e. the number of sticks successfully taken out of the ground over time. It is found that the experiment presents two significantly different dynamics depending on the ratio between the amount of work (the number of sticks) and the number of robots, and that there is a superlinear increase of the collaboration rate with the number of robots. Furthermore, we investigate the usefulness of heterogeneity in the controllers' parameters and of a simple signalling scheme among the robots. Results show that, compared to homogeneous groups of robots without communication, heterogeneity and signalling can significantly increase the collaboration rate when there are fewer robots than sticks, while presenting a less noticeable or even negative effect otherwise.

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Towards a reliable set-up for bio-inspired collective experiments with real robots

Cited by 14 publications

References 10 publications

Probabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots

Probabilistic Modelling of a Bio-Inspired Collective Experiment with Real Robots

Embodied Evolution: Distributing an evolutionary algorithm in a population of robots

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