Decentralized State Estimation using Robotic Sensor Networks

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Freundlich, Charles


Zavlanos, Michael M
Cummings, Mary

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This dissertation proposes three control algorithms for active sensing with one or several autonomous robots.

The algorithms all rely on models of the information content of the sensor measurement with respect to the relative poses between sensors and subjects.

The approaches each predict how new information may impact the uncertainty in the subjects, controlling sensors to new locations or trajectories from where these uncertainties will me minimized.

The first algorithm deals with the Next-Best-View (NBV) problem for a single robot, where the goal is to control a mobile camera so that the next image of a set of possibly mobile targets will be as informative as possible.

The NBV controller is designed for a rig that hosts two cameras in a fronto-parallel arrangement, commonly known as stereo vision.

Assuming that the objects, landmarks, or targets being estimated are visible by both cameras in the rig and that these observations are corrupted by zero-mean Gaussian errors, the control algorithm moves the rig through pose space in order to reduce the expected Kalman-filtered uncertainty in the next location point-estimate.

This is done by differentiating the KF output error covariance matrix with respect to the sensor pose, which results in a nonlinear control problem.

The controller is decomposed so that first the robot computes the NBV in coordinates relative to the body-frame of the stereo rig, and then it moves in pose space to realize this view.

When an image is acquired, a switching signal changes the goal of pose control, giving rise to a stable hybrid system.

Experiments of on a real robot localizing targets in a laboratory setting are presented.

The second algorithm addresses the problem of estimating a finite set of hidden state vectors using a mobile robotic sensor network.

For every hidden state that needs to be estimated, a local Dynamic Program (DP) in the joint state-space of robot positions and state uncertainties determines robot paths and associated sequences of state observations that collectively minimize the estimation uncertainty.

It divides the collection of hidden states into clusters based on a prior belief of their geographic locations and, for each cluster, defines a second DP that determines how far along the local optimal trajectories the robot should travel before transitioning to estimating the next hidden state within the cluster.

Finally, a distributed assignment algorithm dynamically allocates controllers to the robot team from the set of optimal control policies at every cluster.

Assuming Gaussian priors on the hidden state vectors, the distributed state estimation method scales gracefully to large teams of mobile robots and hidden vectors and provide extensive simulations and real-world experiments using stereoscopic vision sensors to illustrate the approach.

The third chapter addresses the problem of controlling a network of mobile sensors so that a set of hidden states are estimated up to a user-specified accuracy. The sensors take measurements and fuse them online using an Information Consensus Filter (ICF). At the same time, the local estimates guide the sensors to their next best configuration. This leads to an LMI-constrained optimization problem that we solve by means of a new distributed random approximate projections method. The new method is robust to the state disagreement errors that exist among the robots as the ICF fuses the collected measurements. Assuming that the noise corrupting the measurements is zero-mean and Gaussian and that the robots are self localized in the environment, the integrated system converges to the next best positions from where new observations will be taken. This process is repeated with the robots taking a sequence of observations until the hidden states are estimated up to the desired user-specified accuracy. It presents simulations of sparse landmark localization, where the robotic team is achieves the desired estimation tolerances while exhibiting interesting emergent behavior.

Experiments of the first two algorithms are also presented.





Freundlich, Charles (2016). Decentralized State Estimation using Robotic Sensor Networks. Dissertation, Duke University. Retrieved from


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