Distributed intelligent algorithms for robotic sensor networks monitoring discontinuous anisotropic environmental fields

Robotic sensor networks, at the junction between distributed robotics and wireless sensor networks, represent a strategic convergence between mobile and networked systems. In this thesis, we have begun to explore this crossover, and where possible, to bring tools, experience, and insight from the field of robotics to bear in the field of sensor networks. We present here a formal and general framework for the classification and construction of distributed intelligent controllers to facilitate implementation, understanding, and analysis, including a complete parameterized system description, and its corresponding generalized performance metrics. The methods shown are capable of uniquely and unambiguously describing any mechanism for distributed control of a robotic sensor network engaged in a monitoring task. A variety of simple distributed intelligent algorithms are illustrated within this framework, which introduce methods for activity control in time, space, and mobility. Appropriate tools, equipment, and controlled testing environments for systematic experimentation have been designed and built, both for a physical system and for corresponding experimentally validated simulations. The general methods presented are intended neither as an exhaustive collection of possible controllers, nor as a replacement for application-specific solutions, but as a flexible, reusable roadmap for system design allowing a user to make educated design choices systematically and rigorously while encoding available information into the provided template, adapting the control model to the constraints of any given specific scenario, accounting for issues of data quality, measurement, communication, mobility, or any combination of the above.

Martinoli, Alcherio
Lausanne, EPFL
Other identifiers:
urn: urn:nbn:ch:bel-epfl-thesis4247-4

Note: The status of this file is: EPFL only

 Record created 2008-10-09, last modified 2018-01-28

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