Cooperative Kalman filters for cooperative exploration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Cooperative exploration requires multiple robotic sensor platforms to navigate in an unknown scalar field to reveal its global structure. Sensor readings from the platforms are combined into estimates to direct motion and reduce noise. We show that the combined estimates for the field value, the gradient and the Hessian satisfy an information dynamic model that does not depend on motion models of the platforms. Based on this model, we design cooperative Kalman filters that apply to general cooperative exploration missions. We rigorously justify a set of sufficient conditions that guarantee the convergence of the cooperative Kalman filters. These sufficient conditions provide guidelines on mission design issues such as the number of platforms to use, the shape of the platform formation, and the motion for each platforms.

Original languageEnglish (US)
Title of host publication2008 American Control Conference, ACC
Number of pages6
StatePublished - 2008
Externally publishedYes
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2008 American Control Conference, ACC
Country/TerritoryUnited States
CitySeattle, WA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Cooperative Kalman filters for cooperative exploration'. Together they form a unique fingerprint.

Cite this