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Recent progress in both Artificial Intelligence (AI) and Robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially Human-Robot Interaction (HRI) for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (i) execute action sequences to complete user requests, (ii) efficiently ask questions to resolve user requests, (iii) understand human commands given in natural language, and (iv) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform.

Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this article, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used for solving task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general purpose planning systems for particular robot task planning domains.

Engineering has not only developed in the field of medicine but has also become quite established in the field of dentistry, especially Orthodontics. Finite element analysis (FEA) is a computational procedure to calculate the stress in an element, which performs a model solution. This structural analysis allows the determination of stress resulting from external force, pressure, thermal change, and other factors. This method is extremely useful for indicating mechanical aspects of biomaterials and human tissues that can hardly be measured in vivo. The results obtained can then be studied using visualization software within the finite element method (FEM) to view a variety of parameters, and to fully identify implications of the analysis. This is a review to show the applications of FEM in Orthodontics. It is extremely important to verify what the purpose of the study is in order to correctly apply FEM.

Abstract-Autonomous vehicles have seen great advancements in recent years, and such vehicles are now closer than ever to being commercially available. The advent of driverless cars provides opportunities for optimizing traffic in ways not possible before. This paper introduces an open source multiagent microscopic traffic simulator called AORTA, which stands for Approximately Orchestrated Routing and Transportation Analyzer, designed for optimizing autonomous traffic at a city-wide scale. AORTA creates scale simulations of the real world by generating maps using publicly available road data from OpenStreetMap (OSM). This allows simulations to be set up through AORTA for a desired region anywhere in the world in a matter of minutes. AORTA allows for traffic optimization by creating intelligent behaviors for individual driver agents and intersection policies to be followed by these agents. These behaviors and policies define how agents interact with one another, control when they cross intersections, and route agents to their destination. This paper demonstrates a simple application using AORTA through an experiment testing intersection policies at a city-wide scale.

Abstract. In 2012, UT Austin Villa claimed Standard Platform League championships at both the US Open and RoboCup 2012 in Mexico City. This paper describes the key contributions that led to the team's victories. First, UT Austin Villa's code base was developed on a solid foundation with a flexible architecture that enables easy testing and debugging of code. Next, the vision code was updated this year to take advantage of the dual cameras and better processor of the new V4 Nao robots. To improve localization, a custom localization simulator allowed us to implement and test a full team solution to the challenge of both goals being the same color. The 2012 team made use of Northern Bites' port of B-Human's walk engine, combined with novel kicks from the walk. Finally, new behaviors and strategies take advantage of opportunities for the robot to take time to setup for a long kick, but kick very quickly when opponent robots are nearby. The combination of these contributions led to the team's victories in 2012.

Numerous cephalometric norms or standards have been given in past for various population in terms of age and ethinic origin. An accurate sagittal jaw relationship is critically important in orthodontic diagnosis and treatment planning. Various angular and linear measurements have been proposed that can be inaccurate because they depend on various factors. [1,2] For example Wits appraisal, ANB angle and nasion perpendicular are currently used by practitioners to diagnose the sagittal jaw relationship but all of these measurements have their limitations.McNamara's main goal in his analysis was to relate the teeth, jaws, and cranial bases to each other. [3] "Nasion Perpendicular to Frankfort Horizontal" is considered in his analysis but limitations include cases where the anteroposterior (A-P) position of nasion is more posterior thus nasion perpendicular is not accurate, and, therefore, an adjustment in the value should be made. [4] Jacobson's Wits appraisal is accurate for determining a discrepancy, but it is unable to determine which jaw is the discrepant. In his analysis accurate construction of the functional occlusal plane is of vital importance and the difficulty of accurately constructing results in a false appraisal of the jaw relationship, [4] also Chang reported that the Wits appraisal was found to evaluate the sagittal dental relationship along the occlusal plane and not the skeletal relationship. [5] Steiner proposed using nasion as the base for angular measurements to determine the position of the maxilla and mandible sagittally. [6] He used the ANB angle because it described the relationship between the maxilla and the mandible but

Abstract. Ground truth detection systems can be a crucial step in evaluating and improving algorithms for self-localization on mobile robots. Selecting a ground truth system depends on its cost, as well as on the detail and accuracy of the information it provides. In this paper, we present a low cost, portable and real-time solution constructed using the Microsoft Kinect RGB-D Sensor. We use this system to find the location of robots and the orange ball in the Standard Platform League (SPL) environment in the RoboCup competition. This system is fairly easy to calibrate, and does not require any special identifiers on the robots. We also provide a detailed experimental analysis to measure the accuracy of the data provided by this system. Although presented for the SPL, this system can be adapted for use with any indoor structured environment where ground truth information is required.