Automated tracking of pallets in warehouses based on asymmetric ultrasound observation models

We consider the use of wireless sensor networks to automatically track “perceptive pallets” of materials in ware-houses for the purpose of monitoring volumetric and spatial constraints. A combination of radio frequency and ultrasound chirping produces position estimates that are noisy and prone to error. To address this, we measure and characterize the ultrasound response from standard “Cricket” wireless sensor motes and beacons. We develop a non-parametric particle filtering approach to estimate trajectories of moving motes and introduce two asymmetric observation models that incorporate measured cardioid-shaped response patterns of ultrasound.

Collaborator: Automation Lab of Professor Ken Goldberg, EECS UC Berkeley