Quantized Coordination Algorithms for Rendezvous and Deployment

By Ruggero Carli, Francesco Bullo. 2009

94 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Wenwu Yu
2. Guanrong Chen
3. Wei Ren
4. Yongcan Cao
5. Francesco Bullo
6. Ruggero Carli
7. Sandro Zampieri
8. Frank L. Lewis

In Control and Systems Engineering

1. Guanrong Chen
2. Wenwu Yu
3. Yongcan Cao
4. Wei Ren
5. Francesco Bullo
6. Sandro Zampieri
7. Frank Allgöwer
8. Ulrich Münz

Citations in the corpus

• An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, methodology): “In [144], coding/decoding strategies were introduced to the quantized consensus algorithms, whe re it was shown that the convergence rate depends on the accuracy of the quantization but not the codin g/decoding schemes.”
• Distributed Control of Robotic Networks (2009, background): “Rendezvous under communication quantization is studied in Fagnani et al. (2004) and Carli and Bullo (2009).”
• Quantized average consensus via dynamic coding/decoding schemes (2008, methodology): “A similar approach has been introduced in the context of the multi-agent coordination laws, rendezvous and deployment [8].”
• Delay robustness in consensus problems (2010, background): “Recently, these algorithms were extended to non-ideal communication networks, e.g. considering quantization effects (Carli & Bullo, 2009; Kashyap, Başar, & Srikant, 2007) or the loss and delay of information (Cao, Morse, & Anderson, 2008; Xiao & Wang, 2008a).”