@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Gal A. Kaminka's publication pages at
@COMMENT http://www.cs.biu.ac.il/~galk/publications/
@Article{tro08, 
  author = 	 {Gal A. Kaminka and Ruti Schechter-Glick and Vladimir Sadov}, 
  title = 	 {Using Sensor Morphology for Multi-Robot Formations}, 
  journal = 	{{IEEE} Transactions on Robotics}, 
  year = 	{2008}, 
  OPTkey = {}, 
  OPTvolume = {}, 
  OPTnumber = {}, 
  pages = {271--282}, 
  OPTmonth = {}, 
  OPTnote = {}, 
  OPTannote = {}, 
  OPTdoi = {http://dx.doi.org/10.1109/TRO.2008.918054},
  wwwnote = {},
  abstract = {Not all robots are created equal (at least in terms of sensors), and  
  thus must be treated differently when coordinating. We presented a novel 
  representation of the sensing capabilities of robots in formations, which move 
  while maintaining their relative positions in a pre-defined geometric shape. 
  Previous work has examined formation-maintenance algorithms that ensure 
  the theoretical stability of the formation, but often ignored the question of 
  using information about sensor capabilities to increase the robustness of the 
  formation in practice. For each geometric formation, an exponential number of 
  stable controllers exists. Thus a key question is how to select (construct) a  
  formation controller that optimizes desired properties, such as sensor usage for 
  robustness. This paper presents a monitoring multi-graph framework for  
  formation controller selection, based on sensor-morphology considerations. We 
  instantiate the framework, and present two contributions. First, we show that 
  graph-theoretic techniques can then be used to efficiently compute sensing 
  policies that maintain a given formation. In particular, separation-bearing 
  (distance-angle) control laws are automatically constructed for each individual 
  robot in the formation, taking into account its specifically sensor morphology. 
  Second, we present a protocol allowing controllers to be switched on-line, to 
  allow robots to adjust to sensory failures. We report on results from 
  comprehensive experiments with physical robots. The results show that the use 
  of the dynamic protocol allows formations of real robots to move significantly 
  faster and with greater precision, while reducing the number of formation 
  failures, due to sensor limitations.}, 
}