Three-Dimensional Construction with Mobile Robots and Modular Blocks
International Journal of Robotics Research
Optimized stochastic policies for task allocationin swarms of robots
IEEE Transactions on Robotics
Abstractions and algorithms for assembly tasks with large numbers of robots and parts
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Aggregation-mediated collective perception and action in a group of miniature robots
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2
Development of top-down analysis of distributed assembly tasks
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
How reverse reactions influence the yield of self-assembly robots
International Journal of Robotics Research
International Journal of Robotics Research
Complexity measures for distributed assembly tasks
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Task partitioning via ant colony optimization for distributed assembly
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
International Journal of Swarm Intelligence Research
Towards solving an obstacle problem by the cooperation of UAVs and UGVs
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We present a decentralized, scalable approach to assembling a group of heterogeneous parts into different products using a swarm of robots. While the assembly plans are predetermined, the exact sequence of assembly of parts and the allocation of subassembly tasks to robots are determined by the interactions between robots in a decentralized fashion in real time. Our approach is based on developing a continuous abstraction of the system derived from models of chemical reactions and formulating the strategy as a problem of selecting rates of assembly and disassembly. These rates are mapped onto probabilities that determine stochastic control policies for individual robots, which then produce the desired aggregate behavior. This top-down approach to determining robot controllers also allows us to optimize the rates at the abstract level to achieve fast convergence to the specified target numbers of products. Because the method incorporates programs for assembly and disassembly, changes in demand can lead to reconfiguration in a seamless fashion. We illustrate the methodology using a physics-based simulator with examples involving 15 robots and two types of final products.