Publications [#289472] of Silvia Ferrari
Papers Published
- Ferrari, S; Foderaro, G; Tremblay, A, A probability density function approach to distributed sensors' path planning,
Proceedings Ieee International Conference on Robotics and Automation
(August, 2010),
pp. 432-439, IEEE, ISSN 1050-4729 [doi]
(last updated on 2021/09/05)Abstract:
A novel artificial-potential function approach is presented for planning the paths of distributed sensor networks in a complex dynamic environment. The approach implements a novel potential function generated from a probability density function (PDF) parameterized by an adaptive Gaussian mixture that is optimized to meet network-level objectives, such as cooperative track detection. The PDF represents the goal density that would be obtained by sampling a statistically-significant number of sensors from the mixture. However, since a smaller number of sensors may be deployed, and each sensor is represented by a disk, the potential function is generated by multiplying the PDF by a likelihood update model that produces networks with disjoint fields-of-view. The approach is demonstrated through numerical simulations involving ocean sensor networks deployed in a region of interest near the New Jersey coast.