no code implementations • 17 Dec 2022 • Wenjun Mei, Julien M. Hendrickx, Ge Chen, Francesco Bullo, Florian Dörfler
Moreover, we prove a necessary and sufficient graph-theoretic condition for the almost-sure convergence to consensus, as well as a sufficient graph-theoretic condition for almost-sure persistent dissensus.
no code implementations • 17 Jan 2022 • Samuel C. Pinto, Shirantha Welikala, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
For a given visiting sequence, we prove that in an optimal dwelling time allocation the peak uncertainty is the same among all the targets.
no code implementations • 2 Aug 2021 • Farhad Mehdifar, Charalampos P. Bechlioulis, Julien M. Hendrickx, Dimos V. Dimarogonas
This work proposes a novel 2-D formation control scheme for acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) based on bipolar coordinates with (almost) global convergence to the desired shape.
no code implementations • 1 Apr 2021 • Samuel C. Pinto, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states.
no code implementations • 24 Mar 2021 • Mingming Shi, Julien M. Hendrickx
We analyze the conditions under which the emergence of frequently observed echelon formation can be explained solely by the maximization of energy savings.
no code implementations • 1 Feb 2019 • Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama
The algorithm has a relative error decay that scales with the square root of the graph resistance, and provide a matching lower bound (up to log factors).
1 code implementation • 11 May 2017 • Adrien B. Taylor, Julien M. Hendrickx, François Glineur
We establish the exact worst-case convergence rates of the proximal gradient method in this setting for any step size and for different standard performance measures: objective function accuracy, distance to optimality and residual gradient norm.
Optimization and Control