14,257 research outputs found
Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
Animal and robot social interactions are interesting both for ethological
studies and robotics. On the one hand, the robots can be tools and models to
analyse animal collective behaviours, on the other hand, the robots and their
artificial intelligence are directly confronted and compared to the natural
animal collective intelligence. The first step is to design robots and their
behavioural controllers that are capable of socially interact with animals.
Designing such behavioural bio-mimetic controllers remains an important
challenge as they have to reproduce the animal behaviours and have to be
calibrated on experimental data. Most animal collective behavioural models are
designed by modellers based on experimental data. This process is long and
costly because it is difficult to identify the relevant behavioural features
that are then used as a priori knowledge in model building. Here, we want to
model the fish individual and collective behaviours in order to develop robot
controllers. We explore the use of optimised black-box models based on
artificial neural networks (ANN) to model fish behaviours. While the ANN may
not be biomimetic but rather bio-inspired, they can be used to link perception
to motor responses. These models are designed to be implementable as robot
controllers to form mixed-groups of fish and robots, using few a priori
knowledge of the fish behaviours. We present a methodology with multilayer
perceptron or echo state networks that are optimised through evolutionary
algorithms to model accurately the fish individual and collective behaviours in
a bounded rectangular arena. We assess the biomimetism of the generated models
and compare them to the fish experimental behaviours.Comment: 10 pages, 4 figure
Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance
Methods of general applicability are searched for in swarm intelligence with
the aim of gaining new insights about natural swarms and to develop design
methodologies for artificial swarms. An ideal solution could be a `swarm
calculus' that allows to calculate key features of swarms such as expected
swarm performance and robustness based on only a few parameters. To work
towards this ideal, one needs to find methods and models with high degrees of
generality. In this paper, we report two models that might be examples of
exceptional generality. First, an abstract model is presented that describes
swarm performance depending on swarm density based on the dichotomy between
cooperation and interference. Typical swarm experiments are given as examples
to show how the model fits to several different results. Second, we give an
abstract model of collective decision making that is inspired by urn models.
The effects of positive feedback probability, that is increasing over time in a
decision making system, are understood by the help of a parameter that controls
the feedback based on the swarm's current consensus. Several applicable
methods, such as the description as Markov process, calculation of splitting
probabilities, mean first passage times, and measurements of positive feedback,
are discussed and applications to artificial and natural swarms are reported
Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics
Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe
A macroscopic analytical model of collaboration in distributed robotic systems
In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased
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