Distributed Hierarchical Control for State Estimation With Robotic Sensor Networks
This work addresses efficient state estimation for robotic sensor networks, offering a scalable solution that is incremental in nature.
The paper tackles the problem of active state estimation with robotic sensor networks by proposing a hierarchical Dynamic Programming framework and decentralized assignment algorithm, achieving optimal cluster-level performance and scalability to large numbers of states and robots, as demonstrated in simulations and real-world experiments.
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.