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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemNavigation behavior design and representations for a people aware mobile robot system(Georgia Institute of Technology, 2016-01-15) Cosgun, AkanselThere are millions of robots in operation around the world today, and almost all of them operate on factory floors in isolation from people. However, it is now becoming clear that robots can provide much more value assisting people in daily tasks in human environments. Perhaps the most fundamental capability for a mobile robot is navigating from one location to another. Advances in mapping and motion planning research in the past decades made indoor navigation a commodity for mobile robots. Yet, questions remain on how the robots should move around humans. This thesis advocates the use of semantic maps and spatial rules of engagement to enable non-expert users to effortlessly interact with and control a mobile robot. A core concept explored in this thesis is the Tour Scenario, where the task is to familiarize a mobile robot to a new environment after it is first shipped and unpacked in a home or office setting. During the tour, the robot follows the user and creates a semantic representation of the environment. The user labels objects, landmarks and locations by performing pointing gestures and using the robot's user interface. The spatial semantic information is meaningful to humans, as it allows providing commands to the robot such as ``bring me a cup from the kitchen table". While the robot is navigating towards the goal, it should not treat nearby humans as obstacles and should move in a socially acceptable manner. Three main navigation behaviors are studied in this work. The first behavior is the point-to-point navigation. The navigation planner presented in this thesis borrows ideas from human-human spatial interactions, and takes into account personal spaces as well as reactions of people who are in close proximity to the trajectory of the robot. The second navigation behavior is person following. After the description of a basic following behavior, a user study on person following for telepresence robots is presented. Additionally, situation awareness for person following is demonstrated, where the robot facilitates tasks by predicting the intent of the user and utilizing the semantic map. The third behavior is person guidance. A tour-guide robot is presented with a particular application for visually impaired users.
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ItemSemantic mapping for service robots: building and using maps for mobile manipulators in semi-structured environments(Georgia Institute of Technology, 2015-04-08) Trevor, Alexander J. B.Although much progress has been made in the field of robotic mapping, many challenges remain including: efficient semantic segmentation using RGB-D sensors, map representations that include complex features (structures and objects), and interfaces for interactive annotation of maps. This thesis addresses how prior knowledge of semi-structured human environments can be leveraged to improve segmentation, mapping, and semantic annotation of maps. We present an organized connected component approach for segmenting RGB-D data into planes and clusters. These segments serve as input to our mapping approach that utilizes them as planar landmarks and object landmarks for Simultaneous Localization and Mapping (SLAM), providing necessary information for service robot tasks and improving data association and loop closure. These features are meaningful to humans, enabling annotation of mapped features to establish common ground and simplifying tasking. A modular, open-source software framework, the OmniMapper, is also presented that allows a number of different sensors and features to be combined to generate a combined map representation, and enabling easy addition of new feature types.
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ItemTime-optimal sampling-based motion planning for manipulators with acceleration limits(Georgia Institute of Technology, 2015-04-07) Kunz, TobiasRobot actuators have physical limitations in how fast they can change their velocity. The more accurately planning algorithms consider these limitations, the better the robot is able to perform. Sampling-based algorithms have been successful in geometric domains, which ignore actuator limitations. They are simple, parameter-free, probabilistically complete and fast. Even though some algorithms like RRTs were specifically designed for kinodynamic problems, which take actuator limitations into account, they are less efficient in these domains or are, as we show, not probabilistically complete. A common approach to this problem is to decompose it, first planning a geometric path and then time-parameterizing it such that actuator constraints are satisfied. We improve the reliability of the latter step. However, the decomposition approach can neither deal with non-zero start or goal velocities nor provides an optimal solution. We demonstrate that sampling-based algorithms can be extended to consider actuator limitations in the form of acceleration limits while retaining the same advantageous properties as in geometric domains. We present an asymptotically optimal planner by combining a steering method with the RRT* algorithm. In addition, we present hierarchical rejection sampling to improve the efficiency of informed kinodynamic planning in high-dimensional spaces.
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ItemAutonomous environment manipulation to facilitate task completion(Georgia Institute of Technology, 2015-03-31) Levihn, MartinA robot should be able to autonomously modify and utilize its environment to assist its task completion. While mobile manipulators and humanoid robots have both locomotion and manipulation capabilities, planning systems typically just consider one or the other. In traditional motion planning the planner attempts to find a collision free path from the robot's current configuration to some goal configuration. In general, this process entirely ignores the fact that the robot has manipulation capabilities. This is in contrast to how humans naturally act - utilizing their manipulation capabilities to modify the environment to assist locomotion. If necessary, humans do not hesitate to move objects, such as chairs, out of their way or even place an object, such as a board, on the ground to reach an otherwise unreachable goal. We argue that robots should demonstrate similar behavior. Robots should use their manipulation capabilities to move or even use environment objects. This thesis aims at bringing robots closer to such capabilities. There are two primary challenges in developing practical systems that allow a real robotic system to tightly couple its manipulation and locomotion capabilities: the inevitable inaccuracies in perception as well as actuation that occur on physical systems, and the exponential size of the search space. To address these challenges, this thesis first extends the previously introduced domain of Navigation Among Movable Obstacles (NAMO), which allows a robot to move obstacles out of its way. We extend the NAMO domain to handle the underlying issue of uncertainty. In fact, this thesis introduces the first NAMO framework that allows a real robotic systems to consider sensing and action uncertainties while reasoning about moving objects out of the way. However, the NAMO domain itself has the shortcoming that it only considers a robot's manipulation capabilities in the context of clearing a path. This thesis therefore also generalizes the NAMO domain itself to the Navigation Using Manipulable Obstacles (NUMO) domain. The NUMO domain enables a robot to more generally consider the coupling between manipulation and locomotion capabilities and supports reasoning about using objects in the environment. This thesis shows the relationship between the NAMO and NUMO domain, both in terms of complexity as well as solution approaches, and presents multiple realizations of the NUMO domain. The first NUMO realization enables a robot to use its manipulation capabilities to assist its locomotion by changing the geometry of the environment for scenarios in which obstructions can be overcome through the usage of a single object. The system led a real humanoid robot to autonomously build itself a bridge to cross a gap and a stair step to get on a platform. A second NUMO realization then introduces reasoning about force constraints using knowledge about the mechanical advantages of a lever and battering ram. The discussed system allows a robot to consider increasing its effective force though the use of objects, such as utilizing a rod as a lever. Finally this thesis extends the NUMO framework for geometric constraints to scenarios in which the robot is faced with a substantial lack of initial state information and only has access to onboard sensing. In summary, this thesis enables robots to autonomously modify their environment to achieve task completion in the presence of lack of support for mobility, the need to increase force capabilities and partial knowledge.
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ItemA linguistic method for robot verification programming and control(Georgia Institute of Technology, 2014-10-29) Dantam, Neil ThomasThere are many competing techniques for specifying robot policies, each having advantages in different circumstances. To unify these techniques in a single framework, we use formal language as an intermediate representation for robot behavior. This links previously disparate techniques such as temporal logics and learning from demonstration, and it links data driven approaches such as semantic mapping with formal discrete event and hybrid systems models. These formal models enable system verification -- a crucial point for physical robots. We introduce a set of rewrite rules for hybrid systems and apply it automatically build a hybrid model for mobile manipulation from a semantic map. In the manipulation domain, we develop a new workspace interpolation methods which provides direct, non-stop motion through multiple waypoints, and we introduce a filtering technique for online camera registration to avoid static calibration and handle changing camera positions. To handle concurrent communication with embedded robot hardware, we develop a new real-time interprocess communication system which offers lower latency than Linux sockets. Finally, we consider how time constraints affect the execution of systems modeled hierarchically using context-free grammars. Based on these constraints, we modify the LL(1) parser generation algorithm to operate in real-time with bounded memory use.
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ItemVisual object perception in unstructured environments(Georgia Institute of Technology, 2014-08-25) Choi, ChanghyunAs robotic systems move from well-controlled settings to increasingly unstructured environments, they are required to operate in highly dynamic and cluttered scenarios. Finding an object, estimating its pose, and tracking its pose over time within such scenarios are challenging problems. Although various approaches have been developed to tackle these problems, the scope of objects addressed and the robustness of solutions remain limited. In this thesis, we target a robust object perception using visual sensory information, which spans from the traditional monocular camera to the more recently emerged RGB-D sensor, in unstructured environments. Toward this goal, we address four critical challenges to robust 6-DOF object pose estimation and tracking that current state-of-the-art approaches have, as yet, failed to solve. The first challenge is how to increase the scope of objects by allowing visual perception to handle both textured and textureless objects. A large number of 3D object models are widely available in online object model databases, and these object models provide significant prior information including geometric shapes and photometric appearances. We note that using both geometric and photometric attributes available from these models enables us to handle both textured and textureless objects. This thesis presents our efforts to broaden the spectrum of objects to be handled by combining geometric and photometric features. The second challenge is how to dependably estimate and track the pose of an object despite the clutter in backgrounds. Difficulties in object perception rise with the degree of clutter. Background clutter is likely to lead to false measurements, and false measurements tend to result in inaccurate pose estimates. To tackle significant clutter in backgrounds, we present two multiple pose hypotheses frameworks: a particle filtering framework for tracking and a voting framework for pose estimation. Handling of object discontinuities during tracking, such as severe occlusions, disappearances, and blurring, presents another important challenge. In an ideal scenario, a tracked object is visible throughout the entirety of tracking. However, when an object happens to be occluded by other objects or disappears due to the motions of the object or the camera, difficulties ensue. Because the continuous tracking of an object is critical to robotic manipulation, we propose to devise a method to measure tracking quality and to re-initialize tracking as necessary. The final challenge we address is performing these tasks within real-time constraints. Our particle filtering and voting frameworks, while time-consuming, are composed of repetitive, simple and independent computations. Inspired by that observation, we propose to run massively parallelized frameworks on a GPU for those robotic perception tasks which must operate within strict time constraints.