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Institute for Robotics and Intelligent Machines (IRIM)

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Author: Deyle, Travis × End date: 2009 × Start date: 2000 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Healthcare Robotics Lab ×

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Now showing 1 - 7 of 7
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    RF vision: RFID receive signal strength indicator (RSSI) images for sensor fusion and mobile manipulation
    (Georgia Institute of Technology, 2009-10) Deyle, Travis ; Nguyen, Hai ; Reynolds, Matt S. ; Kemp, Charles C.
    In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed. Our primary contribution is a new mode of perception that produces images of the spatial distribution of received signal strength indication (RSSI) for each of the tagged objects in an environment. The intensity of each pixel in the 'RSSI image' is the measured RF signal strength for a particular tag in the corresponding direction. We construct these RSSI images by panning and tilting an RFID reader antenna while measuring the RSSI value at each bearing. Additionally, we present a framework for estimating a tagged object's 3D location using fused ID-specific features derived from an RSSI image, a camera image, and a laser range finder scan. We evaluate these methods using a robot with actuated, long-range RFID antennas and finger-mounted short-range antennas. The robot first scans its environment to discover which tagged objects are within range, creates a user interface, orients toward the user-selected object using RF signal strength, estimates the 3D location of the object using an RSSI image with sensor fusion, approaches and grasps the object, and uses its finger-mounted antennas to confirm that the desired object has been grasped. In our tests, the sensor fusion system with an RSSI image correctly located the requested object in 17 out of 18 trials (94.4%), an 11.1% improvement over the system's performance when not using an RSSI image. The robot correctly oriented to the requested object in 8 out of 9 trials (88.9%), and in 3 out of 3 trials the entire system successfully grasped the object selected by the user.
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    PPS-Tags: Physical, Perceptual and Semantic Tags for Autonomous Mobile Manipulation
    (Georgia Institute of Technology, 2009-10) Nguyen, Hai ; Deyle, Travis ; Reynolds, Matt S. ; Kemp, Charles C.
    For many promising application areas, autonomous mobile manipulators do not yet exhibit sufficiently robust performance. We propose the use of tags applied to task-relevant locations in human environments in order to help autonomous mobile manipulators physically interact with the location, perceive the location, and understand the location’s semantics. We call these tags physical, perceptual and semantic tags (PPS-tags). We present three examples of PPS-tags, each of which combines compliant and colorful material with a UHF RFID tag. The RFID tag provides a unique identifier that indexes into a semantic database that holds information such as the following: what actions can be performed at the location, how can these actions be performed, and what state changes should be observed upon task success? We also present performance results for our robot operating on a PPS-tagged light switch, rocker light switch, lamp, drawer, and trash can. We tested the robot performing the available actions from 4 distinct locations with each of these 5 tagged devices. For the light switch, rocker light switch, lamp, and trash can, the robot succeeded in all trials (24/24). The robot failed to open the drawer when starting from an oblique angle, and thus succeeded in 6 out of 8 trials. We also tested the ability of the robot to detect failure in unusual circumstances, such as the lamp being unplugged and the drawer being stuck.
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    1000 Trials: An empirically validated end effector that robustly grasps objects from the floor
    (Georgia Institute of Technology, 2009-05) Xu, Zhe ; Deyle, Travis ; Kemp, Charles C.
    Unstructured, human environments present great challenges and opportunities for robotic manipulation and grasping. Robots that reliably grasp household objects with unknown or uncertain properties would be especially useful, since these robots could better generalize their capabilities across the wide variety of objects found within domestic environments. Within this paper, we address the problem of picking up an object sitting on a plane in isolation, as can occur when someone drops an object on the floor - a common problem for motor- impaired individuals. We assume that the robot has the ability to coarsely position itself in front of the object, but otherwise grasps the object with an open-loop strategy that does not vary from object to object. We present a novel end effector that is capable of robustly picking up a diverse array of everyday handheld objects given these conditions. This straight-forward, inexpensive, nonpre- hensile end effector combines a compliant finger with a thin planar component with a leading wedge that slides underneath the object. We empirically validated the efficacy of this design through a set of 1096 trials over which we systematically varied the object location, object type, object configuration, and floor characteristics. Our implementation, which we mounted on a iRobot Create, had a success rate of 94.71 % on 680 trials, which used 4 floor types with 34 objects of particular relevance to assistive applications in 5 different poses each (4x34x5=680). The robot also had strong performance with objects that would be difficult to grasp using a traditional end effector, such as a dollar bill, a pill, a cloth, a credit card, a coin, keys, and a watch. Prior to this test, we performed 416 trials in order to assess the performance of the end effector with respect to variations in object position.
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    Human-Robot Interaction Studies for Autonomous Mobile Manipulation for the Motor Impaired
    (Georgia Institute of Technology, 2009-03) Choi, Young Sang ; Anderson, Cressel D. ; Deyle, Travis ; Kemp, Charles C.
    We are developing an autonomous mobile assistive robot named El-E to help individuals with severe motor impairments by performing various object manipulation tasks such as fetching, transporting, placing, and delivering. El-E can autonomously approach a location specified by the user through an interface such as a standard laser pointer and pick up a nearby object. The initial target user population of the robot is individuals suffering from amyotrophic lateral sclerosis (ALS). ALS, also known as Lou Gehrig’s disease, is a progressive neuro-degenerative disease resulting in motor impairments throughout the entire body. Due to the severity and progressive nature of ALS, the results from developing robotic technologies to assist ALS patients could be applied to wider motor impaired populations. To accomplish successful development and real world application of assistive robot technology, we have to acquire familiarity with the needs and everyday living conditions of these individuals. We also believe the participation of prospective users throughout the design and development process is essential in improving the usability and accessibility of the robot for the target user population. To assess the needs of prospective users and to evaluate the technology being developed, we applied various methodologies of human studies including interviewing, photographing, and conducting controlled experiments. We present an overview of research from the Healthcare Robotics Lab related to patient needs assessment and human experiments with emphasis on the methods of human centered approach.
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    A foveated passive UHF RFID system for mobile manipulation
    (Georgia Institute of Technology, 2008-09) Deyle, Travis ; Anderson, Cressel D. ; Kemp, Charles C. ; Reynolds, Matt S.
    We present a novel antenna and system architecture for mobile manipulation based on passive RFID technology operating in the 850 MHz - 950 MHz ultra-high-frequency (UHF) spectrum. This system exploits the electromagnetic properties of UHF radio signals to present a mobile robot with both wide-angle dasiaperipheral visionpsila, sensing multiple tagged objects in the area in front of the robot, and focused, high-acuity dasiacentral visionpsila, sensing only tagged objects close to the end effector of the manipulator. These disparate tasks are performed using the same UHF RFID tag, coupled in two different electromagnetic modes. Wide-angle sensing is performed with an antenna designed for far-field electromagnetic wave propagation, while focused sensing is performed with a specially designed antenna mounted on the end effector that optimizes near-field magnetic coupling. We refer to this RFID system as dasiafoveatedpsila, by analogy with the anatomy of the human eye. We report a series of experiments on an untethered autonomous mobile manipulator in a 2.5D environment that demonstrate the features of this architecture using two novel behaviors, one in which data from the far-field antenna is used to determine if a specific tagged object is present in the robotpsilas working area and to navigate to that object, and a second using data from the near-field antenna to grasp a specified object from a collection of visually identical objects. The same UHF RFID tag is used to facilitate both the navigation and grasping tasks.
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    Probabilistic UHF RFID tag pose estimation with multiple antennas and a multipath RF propagation
    (Georgia Institute of Technology, 2008-09) Deyle, Travis ; Kemp, Charles C. ; Reynolds, Matt S.
    We present a novel particle filter implementation for estimating the pose of tags in the environment with respect to an RFID-equipped robot. This particle filter combines signals from a specially designed RFID antenna system with odometry and an RFID signal propagation model. Our model includes antenna characteristics, direct-path RF propagation, and multipath RF propagation. We first describe a novel 6-antenna RFID sensor system that provides the robot with a 360-degree view of the tags in its environment. We then present the results of real-world evaluation where RFID-inferred tag position is compared with ground truth data from a laser range-finder. In our experiments the system is shown to estimate the pose of UHF RFID tags in a real-world environment without requiring a priori training or map-building. The system exhibits 6.1 deg mean bearing error and 0.69 m mean range error over robot to tag distances of over 4 m in an environment with significant multipath. The RFID system provides the ability to uniquely identify specific tagged locations and objects, and to discriminate among multiple tagged objects in the field at the same time, which are important capabilities that a laser range-finder does not provide. We expect that this new type of multiple-antenna RFID system, including particle filters that incorporate RF signal propagation models, will prove to be a valuable sensor for mobile robots operating in semi-structured environments where RFID tags are present.
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    A List of Household Objects for Robotic Retrieval Prioritized by People with ALS (Version 092008)
    (Georgia Institute of Technology, 2008-09) Choi, Young Sang ; Deyle, Travis ; Kemp, Charles C.
    This technical report is designed to serve as a citable reference for the original prioritized object list that the Healthcare Robotics Lab at Georgia Tech released on its website in September of 2008. It is also expected to serve as the primary citable reference for the research associated with this list until the publication of a detailed, peer-reviewed paper. The original prioritized list of object classes resulted from a needs assessment involving 8 motor-impaired patients with amyotrophic lateral sclerosis (ALS) and targeted, in-person interviews of 15 motor-impaired ALS patients. All of these participants were drawn from the Emory ALS Center. The prioritized object list consists of 43 object classes ranked by how important the participants considered each class to be for retrieval by an assistive robot. We intend for this list to be used by researchers to inform the design and benchmarking of robotic systems, especially research related to autonomous mobile manipulation.