Fom Local Actions to Global Tasks: Stigmergy and Collective Robotics

Beckers, R; Holland, Owen; Deneubourg, Jean‐Louis

doi:10.1007/978-94-010-0870-9_63

Cited by 204 publications

(171 citation statements)

References 6 publications

Supporting

Mentioning

164

Contrasting

Unclassified

Order By: Relevance

“…Results One of the pioneering work in object clustering is the one by Beckers et al (1994). In this work, the robots follow a very simple behavior: they explore the environment at random and, when they find an object, they pick it up.…”

Section: Source Of Inspirationmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

View full text Add to dashboard Cite

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.

show abstract

Section: Source Of Inspirationmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Note that we do not need to specify α andα-they enter the model only through R G (throughout this article we will use R G = 0.35, the value reported in [14]). 2 Below we provide a detailed analysis of these equations.…”

Section: The Dynamical Modelmentioning

confidence: 99%

“…In the last few years, the swarm intelligence control principles have been successfully applied to a series of case studies in collective robotics: aggregation [2,20,21] and segregation [13], beacon and odor localization [11,12], collaborative mapping [4], collaborative transportation [15,17], work division and task allocation [1,16], and flocking and foraging [23]. All these works have been performed using groups of simple, autonomous robots or embodied simulated agents, exploiting local communication forms among teammates (implicit, through the environment, or explicit, wireless communication), and fully distributed control.…”

Section: Introductionmentioning

confidence: 99%

A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

et al. 2001

View full text Add to dashboard Cite

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,. 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.

show abstract

“…The robotics community became heavily engaged in the late 1980's first with a focus on simulation [35,36] and then physical implementations [37,38]. Collective behavior in animal groups was an early inspiration [39], with a behavioral-based approach becoming popular in the 1990's [40]. However, according to [34] in 1997, "few applications of cooperative robotics" had been reported, and supporting theory was "still in its formative stages.…”

Section: Background and Historymentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

View full text Add to dashboard Cite

Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

show abstract

Fom Local Actions to Global Tasks: Stigmergy and Collective Robotics

Cited by 204 publications

References 6 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

Cooperative Vehicle Environmental Monitoring

Contact Info

Product

Resources

About