Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Camazine (1993)
The regulation of pollen foraging by honey bees: how foragers assess the colony's need for pollenBehavioral Ecology and Sociobiology, 32
T. Schmickl, K. Crailsheim (2006)
A Navigation Algorithm for Swarm Robotics Inspired by Slime Mold Aggregation
K. Crailsheim (1992)
The flow of jelly within a honeybee colonyJournal of Comparative Physiology B, 162
J. Ferber (2001)
Multiagentensysteme
P.-P. Grasse (1959)
La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeursInsectes Sociaux, 6
S. Kornienko, O. Kornienko, P. Levi (2005)
Collective AI: context awareness via communication
Heiko Hamann, H. Wörn (2007)
A Space- and Time-Continuous Model of Self-Organizing Robot Swarms for Design SupportFirst International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007)
J. Seyfried, Marc Szymanski, Natalie Bender, Ramon Estaña, Michael Thiel, H. Wörn (2004)
The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-manipulation
J. McLurkin, Jennifer Smith (2004)
Distributed Algorithms for Dispersion in Indoor Environments Using a Swarm of Autonomous Mobile Robots
S. Kornienko, O. Kornienko, P. Levi (2005)
Minimalistic approach towards communication and perception in microrobotic swarms2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
A. Martinoli, K. Easton, W. Agassounon (2004)
Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed ManipulationThe International Journal of Robotics Research, 23
V. Trianni, R. Groß, T. Labella, E. Sahin, M. Dorigo (2003)
Evolving Aggregation Behaviors in a Swarm of Robots
I. Karsai (1999)
Decentralized Control of Construction Behavior in Paper Wasps: An Overview of the Stigmergy ApproachArtificial Life, 5
O. Soysal, E. Sahin (2006)
A Macroscopic Model for Self-organized Aggregation in Swarm Robotic Systems
T. Schmickl, R. Thenius, K. Crailsheim (2005)
Simulating swarm intelligence in honey bees: foraging in differently fluctuating environments
S. Camazine, J. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz (2001)
Self-Organization in Biological Systems
S. Camazine, Karl Crailsheimb, N. Hrassnigg, Gene Robinson, B. Leonhard, Helga Kropiunigg (1998)
Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L.)Apidologie, 29
D. Sumpter, S. Pratt (2003)
A modelling framework for understanding social insect foragingBehavioral Ecology and Sociobiology, 53
R. Beckers, J. Deneubourg, S. Goss, J. Pasteels (1990)
Collective decision making through food recruitmentInsectes Sociaux, 37
Peng Song, Vijay Kumar (2002)
A potential field based approach to multi-robot manipulationProceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2
P. Valdastri, P. Corradi, A. Menciassi, T. Schmickl, K. Crailsheim, J. Seyfried, P. Dario (2006)
Micromanipulation, communication and swarm intelligence issues in a swarm microrobotic platformRobotics Auton. Syst., 54
I. Rechenberg (1973)
Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
P. Grassé (1967)
Nouvelles expériences sur le Termite de Müller (Macrotermes mülleri) et considérations sur la théorie de la stigmergieInsectes Sociaux, 14
W. Vent (1975)
Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. BroschiertFeddes Repertorium, 86
D. Bruemmer, D. Dudenhoeffer, M. McKay, Matthew Anderson (2002)
A Robotic Swarm for Spill Finding and Perimeter Formation
A Robust Layered Control Syste For A Mobile Robot
Kristina Lerman, A. Galstyan, A. Martinoli, A. Ijspeert (2002)
A Macroscopic Analytical Model of Collaboration in Distributed Robotic SystemsArtificial Life, 7
T. Schmickl, K. Crailsheim (2006)
Trophallaxis among swarm-robots: A biologically inspired strategy for swarm roboticsThe First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006.
A. Hayes, A. Martinoli, R. Goodman (2003)
Swarm robotic odor localization: Off-line optimization and validation with real robotsRobotica, 21
T. Schmickl, Christoph Möslinger, K. Crailsheim (2006)
Collective Perception in a Robot Swarm
D. Sumpter, M. Beekman (2003)
From nonlinearity to optimality: pheromone trail foraging by antsAnimal Behaviour, 66
T. Seeley, S. Camazine, J. Sneyd (1991)
Collective decision-making in honey bees: how colonies choose among nectar sourcesBehavioral Ecology and Sociobiology, 28
G. DeGrandi-Hoffman, J. Hagler (2000)
The flow of incoming nectar through a honey bee (Apis mellifera L.) colony as revealed by a protein markerInsectes Sociaux, 47
J. Kennedy, R. C. Eberhart (2001)
Swarm intelligence
D. Payton, Mike Daily, R. Estkowski, M. Howard, Craig Lee (2001)
Pheromone RoboticsAutonomous Robots, 11
D. Payton, R. Estkowski, M. Howard (2004)
Pheromone Robotics and the Logic of Virtual Pheromones
B. Webb, R. Reeve (2003)
Reafferent or Redundant: Integration of Phonotaxis and Optomotor Behavior in Crickets and RobotsAdaptive Behavior, 11
E. Bonabeau, M. Dorigo, G. Theraulaz (1999)
Swarm Intelligence - From Natural to Artificial Systems
R. Beckers, J. Deneubourg, S. Goss (1992)
Trails and U-turns in the Selection of a Path by the Ant Lasius nigerJournal of Theoretical Biology, 159
K. Crailsheim (1997)
Trophallactic interactions in the adult honeybee
T. Kazama, K. Sugawara, Toshinori Watanabe (2004)
Collecting Behavior of Interacting Robots with Virtual Pheromone
T. Schmickl, K. Crailsheim (2004)
Costs of Environmental Fluctuations and Benefits of Dynamic Decentralized Foraging Decisions in Honey BeesAdaptive Behavior, 12
O. Soysal, E. Sahin (2007)
Swarm robotics; second SAB 2006 international workshop
G. Caprari, K. Arras, R. Siegwart (2001)
Robot Navigation in Centimeter Range Labyrinths
K. Crailsheim (1998)
Trophallactic interactions in the adult honeybee (Apis mellifera L.)Apidologie, 29
Heiko Hamann, H. Wörn (2006)
An Analytical and Spatial Model of Foraging in a Swarm of Robots
D. Payton, R. Estkowski, M. Howard (2003)
Compound behaviors in pheromone roboticsRobotics Auton. Syst., 44
K. Støy (2006)
How to Construct Dense Objects with Self-Recondfigurable Robots
J. McLurkin (2004)
Stupid robot tricks : a behavior-based distributed algorithm library for programming swarms of robots
This article presents a bio-inspired communication strategy for large-scale robotic swarms. The strategy is based purely on robot-to-robot interactions without any central unit of communication. Thus, the emerging swarm regulates itself in a purely self-organized way. The strategy is biologically inspired by the trophallactic behavior (mouth-to-mouth feedings) performed by social insects. We show how this strategy can be used in a collective foraging scenario and how the efficiency of this strategy can be shaped by evolutionary computation. Although the algorithm works stable enough that it can be easily parameterized by hand, we found that artificial evolution could further increase the efficiency of the swarm’s behavior. We investigated the suggested communication strategy by simulation of robotic swarms in several arena scenarios and studied the properties of some of the emergent collective decisions made by the robots. We found that our control algorithm led to a nonlinear, but graduated path selection of the emerging trail of loaded robots. They favored the shortest path, but not all robots converged to this trail, except in arena setups with extreme differences in the length of the two possible paths. Finally, we demonstrate how the flexibility of collective decisions that arise through this new strategy can be used in changing environments. We furthermore show the importance of a negative feedback in an environment with changing foraging targets. Such feedback loops allow outdated information to decay over time. We found that task efficiency is constrained by a lower and an upper boundary concerning the strength of this negative feedback.
Autonomous Robots – Springer Journals
Published: Dec 22, 2007
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.