# Distributed Hierarchical Control for State Estimation With Robotic Sensor Networks

## TL;DR

This paper introduces a scalable hierarchical control framework for active state estimation in robotic sensor networks, combining dynamic programming and decentralized assignment to efficiently manage large state spaces and multiple robots.

## Contribution

It proposes a novel hierarchical DP approach and a decentralized cluster assignment algorithm, enabling scalable and efficient active state estimation.

## Key findings

- Framework performs well in simulations and real-world experiments.
- Method scales effectively to large numbers of states and sensors.
- Achieves near-optimal state estimation with reduced computational complexity.

## Abstract

This paper addresses active state estimation with a team of robotic sensors. The states to be estimated are represented by spatially distributed, uncorrelated, stationary vectors. Given a prior belief on the geographic locations of the states, we cluster the states in moderately sized groups and propose a new hierarchical Dynamic Programming (DP) framework to compute optimal sensing policies for each cluster that mitigates the computational cost of planning optimal policies in the combined belief space. Then, we develop a decentralized assignment algorithm that dynamically allocates clusters to robots based on the pre-computed optimal policies at each cluster. The integrated distributed state estimation framework is optimal at the cluster level but also scales very well to large numbers of states and robot sensors. We demonstrate efficiency of the proposed method in both simulations and real-world experiments using stereoscopic vision sensors.