RT @msminirobot
Looking forward to an exciting conference summer!
1 full paper accepted at IEEE CEC 2023 (w/ Tristan & @SwarmDynamics )
and
1 extended abstract (w/ Christopher & @SwarmDynamics) & 1 full paper (joint work with LMU) at @ALifeConf
RT @msminirobot
My dissertation "The Predicting Swarm:
Evolving Collective Behaviors for Robot Swarms by Minimizing Surprise" is available online now. Just in case you're still looking for something to read over the weekend...
usually we don't cluster our publications but
1 full paper & 2 extended abstracts
accepted at #ALIFE2023
collaborations with @msminirobot @mohsen_raoufi @PRomanczuk & Thomas Schmickl
Hope you have fun in Japan. :)
https://2023.alife.org
@ALifeConf #swarm #robotics #evolution
“our CEC 2023 paper got accepted Evolution of Collective Decision-Making Mechanisms for Collective Perception by @msminirobot and Tristan competitive evolved decision-making, compared to voter m.&majority rule, analysis of scalability with problem difficulty #swarm #robotics”
Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.
check this out:
