Distributed Hierarchical Control for State Estimation With Robotic Sensor Networks

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.

Team optimal decentralized estimation by multiple agents with partial history sharing information structure

“…In addition, there exists another kind of significance decentralized problems named by team optimal decentralized estimation, where all the agents estimate the state based on local information to minimize an error function. This kind of problems has wide applications in engineering, energy, and network control [28][29][30], which have been extensively studied. For example, [31] presented a novel team optimal decentralized estimation for a team of uninhabited aerial vehicles cooperating under communication imperfections.…”

Distributed State Estimation Using Intermittently Connected Robot Networks

“…Distributed State Estimation (DSE) using mobile robot networks has a number of important applications, including robot localization [1], [2], SLAM [3]- [6], coverage [7], target localization [8]- [10] and tracking [11]- [16] among others. In these applications, the robots are equipped with sensing devices and collect information in order to minimize the uncertainty of the state.…”