Sharing memory robustly in message-passing systems
Journal of the ACM (JACM)
Distributed Algorithms
Very Large Scale Spatial Computing
UMC '02 Proceedings of the Third International Conference on Unconventional Models of Computation
Botanical computing: a developmental approach to generating interconnect topologies on an amorphous computer
Programmable self-assembly: constructing global shape using biologically-inspired local interactions and origami mathematics
Infrastructure for Engineered Emergence on Sensor/Actuator Networks
IEEE Intelligent Systems
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Approximate distributed Kalman filtering in sensor networks with quantifiable performance
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Self-adapting modular robotics: a generalized distributed consensus framework
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Programming an amorphous computational medium
UPP'04 Proceedings of the 2004 international conference on Unconventional Programming Paradigms
Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Robotic swarms, like all spatial computers, are a challenging environment for the execution of distributed consensus algorithms due to their scale, diameter, and frequent failures. Exact consensus is generally impractical on spatial computers, so we consider approximate consensus algorithms. In this paper, we show that the family of self-organizing protocols based on the graph Laplacian of a network[19] are impractical as well. With respect to the structure of a finite-neighborhood spatial computer, we find that these protocols have an expected convergence time of O(diameter2) when the inputs are strongly correlated with location. Verifying this result in simulation, we further determine that the constant factor on the convergence time is high, rendering Laplacian-based approximate consensus unsuitable for general use on spatial computers.