Evolved swarming without positioning information: an application in aerial communication relay

Hauert, Sabine; Zufferey, Jean-Christophe; Floreano, Dario

doi:10.1007/s10514-008-9104-9

Cited by 119 publications

(85 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rather than grids, work by Hauert et al (2009a) evolved controllers for a swarm of robots that could form chains where each robot's heading is synchronised with its neighbouring robots'. The chain can move in the environment while staying connected to the base from which the robots are launched until a user equipped with an emitter is found.…”

Section: Flockingmentioning

confidence: 99%

“…More recently, researchers have been using artificial evolution to automatically design controllers for flying robots (Gaudiano et al, 2005;Hauert et al, 2009a;Richards et al, 2005;Ruini and Cangelosi, 2009). This strategy has the potential to automatically find unthought of controllers that are simple and efficient.…”

Section: Approaches To Controller Designmentioning

confidence: 99%

“…Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours (Hauert et al, 2008(Hauert et al, , 2009aStirling et al, 2010). Flying in formation provides an effective way of de-congesting the airspace.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

View full text Add to dashboard Cite

Section: Flockingmentioning

confidence: 99%

Section: Approaches To Controller Designmentioning

confidence: 99%

See 1 more Smart Citation

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

View full text Add to dashboard Cite

“…Furthermore, robots could incur a cost by searching for rescuers instead of participating to the maintenance of an already established radio network. Consequently, we resorted to teams of genetically related controllers evolved under team-level selection (Hauert, Zufferey, and Floreano, 2009). The genomes of the robots encoded the synaptic weights of the neurocontrollers of the robots.…”

Section: Switzerlandmentioning

confidence: 99%

Methods for Artificial Evolution of Truly Cooperative Robots

Floreano

Keller

2009

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

SwitzerlandCooperation applies the situations where two or more individuals obtain a net benefit by working together. Cooperation is widely spread in nature and takes several forms, ranging from behavioral coordination to sacrifice of one's own life for the benefit of the group. This latter form of cooperation is known as "true cooperation", or "altruism", and is found only in few cases. Truly cooperative robots would be very useful in conditions where unpredictable events may require costly actions by individual robots for the success of the mission. However, the interactions among robots sharing the same environment can affect in unexpected ways the behavior of individual robots, making very difficult the design of rules that produce stable cooperative behavior. It is thus interesting to examine under which conditions truly cooperative behavior evolves in nature and how those conditions can be translated into evolutionary algorithms that are applicable to a wide range of robotic situations.Building on earlier intuitions by Haldane (1955), Hamilton (1964) suggested that altruism can evolve if the cooperator is genetically related to the recipient of help. In this case, even if the cooperator cannot propagate its own genes to the next generation, its altruistic act will increase the probability that a large portion of those genes will be propagated through the reproduction of the recipient of the altruistic act. The theory of kin selection (Maynard-Smith 1964), which developed from Hamilton's model, predicts that the ratio of altruistic individuals in a population is related to the degree of kinship, or genetic relatedness, among individuals. Another explanation for the evolution of altruistic cooperation is provided by the theory of group selection, which argues that altruistic cooperation may also evolve in groups of genetically unrelated individuals that are selected and reproduced together at a higher rate than the single individuals composing the group (Wynne-Edwards 1986). This could happen in situations where the synergetic effect of cooperation by different individuals provides a higher fitness to the group with respect to other competing groups. In those situations, cooperating individuals can be seen as a superorganism that becomes the unit of selection. It has been suggested that group selection may be a driving force behind the transition from unicellular to multicellular organisms (Michod 1999).In the context of evolution experiments with robots (Floreano and Mattiussi, 2008;Nolfi and Floreano, 2000), the hypotheses of kin selection and of group selection translate into the genetic composition of the group of robots and into the method used to select individuals for reproduction. If we consider only the extreme conditions, individuals in a team could be genetically homogeneous (clones) or heterogeneous (they differ from each other); and the fitness could be computed at the level of the team (in which case, the entire team of individuals is reproduced) or at the level of the individual (in which case...

show abstract

“…By synchronizing their maps the UAVs coordinate to avoid redundant searches [14]. Hauert, et al had been investigating the potential of using a swarm of UAVs to establish a wireless communication network [15]. They applied artificial evolution to develop neuronal controllers for the swarm of homogenous agents.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mission Control for UAV Swarm via Task Stimulus Approach

Cheng¹,

Page²,

Olsen³

2013

AJIS

View full text Add to dashboard Cite

Interest in self-organized (SO), multi-robotic systems is increasing because of their flexibility, robustness, and scalability in performing complex tasks. This paper describes a decentralized task allocation model based on both task stimulus intensity and a responding threshold. The response threshold method was developed through observations of social insects. It allows a swarm of insects with a relatively low-level of intelligence to perform complex tasks. In this work, an agent based simulation environment is developed incorporating these ideas. The mission scenario simulated in this study is a wide area search and destroy mission in an initially unknown environment. The mission objectives are to effectively allocate a UAV swarm to both optimize coverage of the search space and attack a target. Rule based behaviours were used to create UAV formations. Two sets of simulations with different swarm size and target numbers were performed. The simulation results show that with task stimulus intensity and a responding threshold, the UAV swarm demonstrates emergent behaviour and individual vehicles respond adaptively to the changing environment.

show abstract

Evolved swarming without positioning information: an application in aerial communication relay

Cited by 119 publications

References 21 publications

Underwater Robot Swarms: Challenges and Opportunities

Underwater Robot Swarms: Challenges and Opportunities

Methods for Artificial Evolution of Truly Cooperative Robots

Dynamic Mission Control for UAV Swarm via Task Stimulus Approach

Contact Info

Product

Resources

About