Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. Note to Practitioners - This paper proposes a fast, robust classification method for online sensor data classification. The proposed method is designed to cope with high levels of sensor outliers. The robustness of the method and its computational efficiency make it particularly appealing for online sensor data classification in challenging field conditions in which the presence of sensor outliers causes practical difficulties for most existing classification algorithms.

Planning under motion and observation uncertainties requires the solution of a stochastic control problem in the space of feedback policies. In this paper, by restricting the policy class to the linear feedback polices, we reduce the general (n2+ n)-dimensional belief space planning problem to an (n)-dimensional problem. As opposed to the previous literature that search in the space of open-loop optimal control policies, we obtain this reduction in the space of closed-loop policies by obtaining a Linear Quadratic Gaussian (LQG) design with the best nominal performance. Then, by taking the entire underlying trajectory of the LQG controller as the decision variable, we pose a coupled design of the trajectory and estimator (while keeping the design of the controller separate) as a NonLinear Program (NLP) that can be solved by a general NLP solver. We prove that under a first-order approximation and a careful usage of the separation principle, our approximations are valid. We provide an analysis on the existing major belief space planning methods and show that our algorithm keeps the lowest computational burden while searching in the policy space. Finally, we extend our solution to contain general state and control constraints. Our simulation results support our design.

Belief space planning is concerned with the problem of finding the control policy under process and measurement uncertainties. Formulated as a stochastic control problem, the solution of a general Decentralized Partially Observed Markov Decision Process (Dec-POMDP) is a collection of feedback policies for individual agents, maximizing a joint value function. In this paper, we design (m) number of Linear Quadratic Gaussian (LQG) policies for (m) number of agents maximizing the joint performance of the team. Casting the problem as a NonLinear Program (NLP), we propose a framework that reduces the optimization dimension from ((mn)2+ mn) to (mn) with (n) referring to the dimension of each individual agent's state space. As a result, the proposed method reduces the formidable generic Dec-POMDP to a computationally tractable multi-agent planning under uncertainty. Our results in 2D and 3D environments demonstrate the performance of the algorithm and its ability to predict and avoid inter-agent collisions.

Planning under process and measurement uncertainties is a challenging problem. In its most general form, it can be modeled as a Partially Observed Markov Decision Process (POMDP) problem. However POMDPs are generally difficult to solve when the underlying spaces are continuous, particularly when beliefs are non-Gaussian, and the difficulty is further exacerbated when there are also non-convex constraints on states. Existing algorithms to address such challenging POMDPs are expensive in terms of computation and memory. In this paper, we provide a feedback policy in non-Gaussian belief space by solving a convex program for common non-linear observation models. The solution involves a Receding Horizon Control strategy using particle filters for the non-Gaussian belief representation. We develop a way of capturing non-convex constraints in the state space and adapt the optimization to incorporate such constraints, as well. A key advantage of this method is that it does not introduce additional variables in the optimization problem and is therefore more scalable than existing constrained problems in belief space. We demonstrate the performance of the method on different scenarios.