Details

Autor(en) / Beteiligte

• Ferrari, S
• Foderaro, G
• Tremblay, A

Titel

A probability density function approach to distributed sensors' path planning

Ist Teil von

• 2010 IEEE International Conference on Robotics and Automation, 2010, p.432-439

Ort / Verlag

IEEE

Erscheinungsjahr

2010

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• 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.