Decentralized group control of autonomous robots swarm without data routing

To solve a wide class of practical problems in engineering practice, groups of robots with varying network topology are used; these groups are controlled by decentralized algorithms. The emphasis is on decentralizing computing in group, but at the communications protocol layer, the network remains centralized using data routing. This article discusses the task of group control, in which there is no traditional data packets routing. The procedure of simulation modeling and the hardware stand that implements it are described.

Decentralized Algorithms for Weapon-Target Assignment in Swarming Combat System

Swarming small unmanned aerial or ground vehicles (UAVs or UGVs) have attracted the attention of worldwide military powers as weapons, and the weapon-target assignment (WTA) problem is extremely significant for swarming combat. The problem involves assigning weapons to targets in a decentralized manner such that the total damage effect of targets is maximized while considering the nonlinear cumulative damage effect. Two improved optimization algorithms are presented in the study. One is the redesigned auction-based algorithm in which the bidding rules are properly modified such that the auction-based algorithm is applied for the first time to solve a nonlinear WTA problem. The other one is the improved task swap algorithm that eliminates the restriction in which the weights of the edges on graph G must be positive. Computational results for up to 120 weapons and 110 targets indicate that the redesigned auction-based algorithm yields an average improvement of 37% over the conventional auction-based algorithm in terms of solution quality while the additional running time is negligible. The improved task swap algorithm and the other two popular task swap algorithms almost achieve the same optimal value, while the average time-savings of the proposed algorithm correspond to 53% and 74% when compared to the other two popular task swap algorithms. Furthermore, the hybrid algorithm that combines the above two improved algorithms is examined. Simulations indicate that the hybrid algorithm exhibits superiority in terms of solution quality and time consumption over separately implementing the aforementioned two improved algorithms.

The idemetric property: when most distances are (almost) the same

We introduce the idemetric property, which formalizes the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard amongst small-world network models. Modulo reasonable sparsity assumptions, we are then able to show that a strong form of idemetricity is actually equivalent to a very weak expander condition (PUMP). This provides a direct way of providing short proofs that small-world network models such as the Watts-Strogatz model are strongly idemetric (for a wide range of parameters), and also provides further evidence that being idemetric is a common property. We then consider how satisfaction of the idemetric property is relevant to algorithm design. For idemetric graphs, we observe, for example, that a single breadth-first search provides a solution to the all-pairs shortest paths problem, so long as one is prepared to accept paths which are of stretch close to 2 with high probability. Since we are able to show that Kleinberg's model is idemetric, these results contrast nicely with the well known negative results of Kleinberg concerning efficient decentralized algorithms for finding short paths: for precisely the same model as Kleinberg's negative results hold, we are able to show that very efficient (and decentralized) algorithms exist if one allows for reasonable preprocessing. For deterministic distributed routing algorithms we are also able to obtain results proving that less routing information is required for idemetric graphs than in the worst case in order to achieve stretch less than 3 with high probability: while Ω ( n 2 ) routing information is required in the worst case for stretch strictly less than 3 on almost all pairs, for idemetric graphs the total routing information required is O ( n log( n )).

COINSTAC: Decentralizing the future of brain imaging analysis

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.