Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Tough guys don't dance: intention movements and the evolution of signalling in animal contests
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Cellular Robotics and Micro Robotics Systems
Cellular Robotics and Micro Robotics Systems
IEEE Spectrum - Modular robots change shape to conquer tasks and tough terrain
Super Mechano-System: New Perspective for Versatile Robotic System
ISER '00 Experimental Robotics VII
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Swarm-Bot: A New Distributed Robotic Concept
Autonomous Robots
Cooperation through self-assembly in multi-robot systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Evolution of Solitary and Group Transport Behaviors for Autonomous Robots Capable of Self-Assembling
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Scalability in evolved neurocontrollers that guide a swarm of robots in a navigation task
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
From solitary to collective behaviours: decision making and cooperation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Autonomous Self-Assembly in Swarm-Bots
IEEE Transactions on Robotics
Self-Organized Coordinated Motion in Groups of Physically Connected Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Alife in the galapagos: migration effects on neuro-controller design
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
To grip, or not to grip: evolving coordination in autonomous robots
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Towards artificial evolution of complex behaviors observed in insect colonies
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
L-system-driven self-assembly for swarm robotics
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Programming and evolving physical self-assembling systems in three dimensions
Natural Computing: an international journal
Multivariate context collection in mobile sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 0.00 |
This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.