Feedback-based Information Roadmap (FIRM): Graph-based Estimation and Control of Robotic Systems Under Uncertainty

This dissertation addresses the problem of stochastic optimal control with imperfect measurements. The main application of interest is robot motion planning under uncertainty. In the presence of process uncertainty and imperfect measurements, the system's state is unknown and a state estimation module is required to provide the information-state (belief), which is the probability distribution function (pdf) over all possible states. Accordingly, successful robot operation in such a setting requires reasoning about the evolution of information-state and its quality in future time steps. In its most general form, this is modeled as a Partially-Observable Markov Decision Process (POMDP) problem. Unfortunately, however, the exact solution of this problem over continuous spaces in the presence of constraints is computationally intractable. Correspondingly, state-of-the-art methods that provide approximate solutions are limited to problems with short horizons and small domains. The main challenge for these problems is the exponential growth of the search tree in the information space, as well as the dependency of the entire search tree on the initial belief. Inspired by sampling-based (roadmap-based) methods, this dissertation proposes a method to construct a "graph" in information space, called Feedback-based Information RoadMap (FIRM). Each FIRM node is a probability distribution and each FIRM edge is a local controller. The concept of belief stabilizers is introduced as a way to steer the current belief toward FIRM nodes and induce belief reachability. The solution provided by the FIRM framework is a feedback law over the information space, which is obtained by switching among locally distributed feedback controllers. Exploiting such a graph in planning, the intractable POMDP problem over continuous spaces is reduced to a tractable MDP (Markov Decision Process) problem over the graph (FIRM) nodes. FIRM is the first graph generated in the information space that preserves the principle of optimality, i.e., the costs associated with different edges of FIRM are independent of each other. Unlike the forward search methods on tree-structures, the plans produced by FIRM are independent of the initial belief (i.e., plans are query-independent). As a result, they are robust and reliable. They are robust in the sense that if the system's belief deviates from the planned belief, then replanning is feasible in real-time, as the computed solution is a feedback over the entire belief graph. Computed plans are reliable in the sense that the probability of violating constraints (e.g., hitting obstacles) can be seamlessly incorporated into the planning law. Moreover, FIRM is a scalable framework, as the computational complexity of its construction is linear in the size of underlying graph as opposed to state-of-the-art methods whose complexity is exponential in the size of underlying graph. In addition to the abstract framework, we present concrete FIRM instantiations for three main classes of robotic systems: holonomic, nonholonomic, and non-pointstabilizable. The abstract framework opens new avenues for extending FIRM to a broader class of systems that are not considered in this dissertation. This includes systems with discrete dynamics or in general systems that are not well-linearizable, systems with non-Gaussian distributions, and systems with unobservable modes. In addition to the abstract framework and concrete instantiations of it, we propose a formal technique for replanning with FIRM based on a rollout-policy algorithm to handle changes in the environment as well as discrepancies between actual and computational models. We demonstrate the performance of the proposed motion planning method on different robotic systems, both in simulation and on physical systems. In the problems we consider, the system is subject to motion and sensing noise. Our results demonstrate a significant advance over existing approaches for motion planning in information space. We believe the proposed framework takes an important step toward making information space planners applicable to real world robotic applications

Decentralized control of Partially Observable Markov Decision Processes using belief space macro-actions

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems

MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems

Cyber-Physical Systems (CPSs) refer to engineering platforms that rely on the inte- gration of physical systems with control, computation, and communication technologies. Autonomous vehicles are instances of CPSs that are rapidly growing with applications in many domains. Due to the integration of physical systems with computational sens- ing, planning, and learning in CPSs, hardware-in-the-loop experiments are an essential step for transitioning from simulations to real-world experiments. This paper proposes an architecture for rapid prototyping of CPSs that has been developed in the Aerospace Controls Laboratory at the Massachusetts Institute of Technology. This system, referred to as MAR-CPS (Measurable Augmented Reality for Prototyping Cyber-Physical Systems), includes physical vehicles and sensors, a motion capture technology, a projection system, and a communication network. The role of the projection system is to augment a physical laboratory space with 1) autonomous vehicles' beliefs and 2) a simulated mission environ- ment, which in turn will be measured by physical sensors on the vehicles. The main focus of this method is on rapid design of planning, perception, and learning algorithms for au- tonomous single-agent or multi-agent systems. Moreover, the proposed architecture allows researchers to project a simulated counterpart of outdoor environments in a controlled, indoor space, which can be crucial when testing in outdoor environments is disfavored due to safety, regulatory, or monetary concerns. We discuss the issues related to the design and implementation of MAR-CPS and demonstrate its real-time behavior in a variety of problems in autonomy, such as motion planning, multi-robot coordination, and learning spatio-temporal fields.Boeing Compan

Association of HLA class II Alleles with Childhood Asthma and Total IgE Levels

Asthma is a complex and multifactorial disorder. Several studies have reported association between different HLA- DQB1 and HLA- DRB1 alleles and allergic asthma. The aim of the present study was to investigate the association of HLA-class II alleles and haplotypes, with total serum IgE and the results of the skin prick test in Iranian children with allergic asthma. A total of 112 patients with allergic asthma symptoms (75 males and 37 females) were selected randomly from the pediatric hospital. In some patients total serum IgE and prick test were determined. Data of this study shows that HLA-DRB1*12 significantly increased in asthmatic patients (4.5% vs. 0%, P-value=0.04). HLA-DQB1*0603 and 0604 alleles were significantly higher in asthmatics than those in normal controls (10% vs. 0%, P-value= 0.0001; and 9.3% vs. 3.7%, P-value= 0.04, respectively). The statistical significance was relinquished after p value correction for all alleles except for HLA-DQB1*0602 (Pc=0.03) and HLA-DQB1*0603 (Pc=0.0015). Conversely, HLA-DQB1*0501 and 0602 were decreased in asthmatics compared to normal controls (7.5% vs. 13.5%, P-value= 0.05; and 4% vs. 12.5%, P-value= 0.002, respectively). The mean of total IgE in patients was 483 IU, and it was significantly high about 1140 IU in asthmatic patients with positive skin prick test to house dust. The most frequent alleles in asthmatic patients with the total IgE>200 IU/mL were HLA-DRB1*11and 1401, HLA-DQA1*0505, HLA-DQB1*0301 and in patients with total Ig

High rate treatment of hospital wastewater using activated sludge process induced by high-frequency ultrasound

The biomass concentration of conventional activated sludge (CAS) process due to low sludge sedimentation in clarifiers is limited to 3000 mg/L. In this study, high-frequency ultrasound wave (1.8 MHz) was applied to enhance the CAS process performance using high Mixed Liquor Suspended Solid (MLSS) concentration. The study conducted using a pilot scale CAS bioreactor (with and without ultrasound) and their performance for treating a hospital wastewater were compared. Experimental conditions were designed based on a Central Composite Design (CCD). The sets of data analyzed, modeled and optimized using Response Surface Methodology (RSM). The effect of MLSS concentration 3000–8000 mg/L and hydraulic retention time (HRT) 2–8 h are considered as operating variables to investigate on process responses. The obtained results showed that high-frequency ultrasound was significantly decreased the sludge volume index (SVI) 50% and effluent turbidity about 88.5% at high MLSS. Also, observed that COD removal of both systems was nearly similar, as the maximum COD removal for sonicated and non-sonicated systems were 92 and 92.5% respectively. However, this study demonstrates that the ultrasound irradiation has not had any negative effect on the microbial activity

Information-based Active SLAM via topological feature graphs

Exploring an unknown space and building maps is a fundamental capability for mobile robots. For fully autonomous systems, the robot would further need to actively plan its paths during exploration. The problem of designing robot trajectories to actively explore an unknown environment and minimize the map error is referred to as active simultaneous localization and mapping (active SLAM). Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources.United States. Army Research Office (Grant W911NF-11-1-0391)United States. Office of Naval Research (Grant N00014-11-1-0688)National Science Foundation (Award IIS-1318392

Effectiveness of intravenous Dexamethasone versus Propofol for pain relief in the migraine headache: A prospective double blind randomized clinical trial

Abstract Background There are many drugs recommended for pain relief in patients with migraine headache. Methods In a prospective double blind randomized clinical trial, 90 patients (age ≥ 18) presenting to Emergency medicine Department with Migraine headache were enrolled in two equal groups. We used intravenous propofol (10 mg every 5–10 minutes to a maximum of 80 mg, slowly) and intravenous dexamethasone (0.15 mg/kg to a maximum of 16 mg, slowly), in group I and II, respectively. Pain explained by patients, based on VAS (Visual Analogue Scale) was recorded at the time of entrance to ED, and after injection. Data were analyzed by paired samples t test, using SPSS 16. P  Results The mean of reported pain (VAS) was 8 ± 1.52 in propofol group and 8.11 ± 1.31 in dexamethasone group at presenting time (P > 0.05). The VAS in propofol group was obviously decreased to 3.08 ± 1.7, 1.87 ± 1.28 and 1.44 ± 1.63 after 10, 20 and 30 minutes of drug injection, respectively. The VAS in dexamethasone group was 5.13 ± 1.47, 3.73 ± 1.81 and 3.06 ± 2 after 10, 20 and 30 minutes of drug injection, respectively. The mean of reported VAS in propofol group was less than dexamethasone group at the above mentioned times (P  Conclusions Intravenous propofol is an efficacious and safe treatment for patients presenting with Migraine headache to the emergency department. Trial registration Clinical Trials IRCT201008122496N4</p