Crystalline Robots: Self-Reconfiguration with Compressible Unit Modules
Autonomous Robots
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Automated Design of Adaptive Controllers for Modular Robots using Reinforcement Learning
International Journal of Robotics Research
Miche: Modular Shape Formation by Self-Disassembly
International Journal of Robotics Research
Three-Dimensional Construction with Mobile Robots and Modular Blocks
International Journal of Robotics Research
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Stochastic modular robotic systems: a study of fluidic assembly strategies
IEEE Transactions on Robotics
Ultrastable neuroendocrine robot controller
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Stochastic assembly approaches can reduce the power, computation, and/or actuation demands on assembly systems by taking advantage of probabilistic processes. At the same time, however, they relinquish the efficiency and predictability of deterministic alternatives. This makes planning error-free assembly sequences challenging, particularly in the face of changing environmental conditions or goals. Here we address these challenges with an on-line approach to assembly planning for stochastically reconfigurable systems where the spatial and temporal availability of modules is uncertain, either due to a stochastic assembly mechanism, resource fluctuations, or large numbers of uncoordinated agents. We propose an assembly algorithm that is guaranteed to find an assembly path for finite-sized, connected objects. This is achieved by sampling the space of possible assembly paths to the target structure that satisfy assembly constraints. Assembly is accelerated by pursuing multiple paths in parallel. The algorithm computes these parallel assembly paths on-line during assembly and is thus able to adapt to changing conditions, as well as predict the remaining assembly time. For situations where the number of paths found exceeds the number that can be pursued in parallel, the assembly algorithm further maximizes assembly rates according to domain-specific local assembly costs.