428 research outputs found
Active Classification for POMDPs: a Kalman-like State Estimator
The problem of state tracking with active observation control is considered
for a system modeled by a discrete-time, finite-state Markov chain observed
through conditionally Gaussian measurement vectors. The measurement model
statistics are shaped by the underlying state and an exogenous control input,
which influence the observations' quality. Exploiting an innovations approach,
an approximate minimum mean-squared error (MMSE) filter is derived to estimate
the Markov chain system state. To optimize the control strategy, the associated
mean-squared error is used as an optimization criterion in a partially
observable Markov decision process formulation. A stochastic dynamic
programming algorithm is proposed to solve for the optimal solution. To enhance
the quality of system state estimates, approximate MMSE smoothing estimators
are also derived. Finally, the performance of the proposed framework is
illustrated on the problem of physical activity detection in wireless body
sensing networks. The power of the proposed framework lies within its ability
to accommodate a broad spectrum of active classification applications including
sensor management for object classification and tracking, estimation of sparse
signals and radar scheduling.Comment: 38 pages, 6 figure
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
In this paper we present a hierarchical multi-rate control architecture for
nonlinear autonomous systems operating in partially observable environments.
Control objectives are expressed using syntactically co-safe Linear Temporal
Logic (LTL) specifications and the nonlinear system is subject to state and
input constraints. At the highest level of abstraction, we model the
system-environment interaction using a discrete Mixed Observable Markov
Decision Problem (MOMDP), where the environment states are partially observed.
The high level control policy is used to update the constraint sets and cost
function of a Model Predictive Controller (MPC) which plans a reference
trajectory. Afterwards, the MPC planned trajectory is fed to a low-level
high-frequency tracking controller, which leverages Control Barrier Functions
(CBFs) to guarantee bounded tracking errors. Our strategy is based on model
abstractions of increasing complexity and layers running at different
frequencies. We show that the proposed hierarchical multi-rate control
architecture maximizes the probability of satisfying the high-level
specifications while guaranteeing state and input constraint satisfaction.
Finally, we tested the proposed strategy in simulations and experiments on
examples inspired by the Mars exploration mission, where only partial
environment observations are available
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