Person:

Kemp, Charles C.

Permanent Link

https://hdl.handle.net/1853/72201

Associated Organization(s)

Organizational Unit

Wallace H. Coulter Department of Biomedical Engineering

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Publication Search Results

Now showing 1 - 10 of 13

Finding and Navigating to Household Objects with UHF RFID Tags by Optimizing RF Signal Strength

(Georgia Institute of Technology, 2014-09) Deyle, Travis ; Reynolds, Matthew S. ; Kemp, Charles C.

We address the challenge of finding and navigating to an object with an attached ultra-high frequency radio- frequency identification (UHF RFID) tag. With current off-the- shelf technology, one can affix inexpensive self-adhesive UHF RFID tags to hundreds of objects, thereby enabling a robot to sense the RF signal strength it receives from each uniquely identified object. The received signal strength indicator (RSSI) associated with a tagged object varies widely and depends on many factors, including the object’s pose, material prop- erties and surroundings. This complexity creates challenges for methods that attempt to explicitly estimate the object’s pose. We present an alternative approach that formulates finding and navigating to a tagged object as an optimization problem where the robot must find a pose of a directional antenna that maximizes the RSSI associated with the target tag. We then present three autonomous robot behaviors that together perform this optimization by combining global and local search. The first behavior uses sparse sampling of RSSI across the entire environment to move the robot to a location near the tag; the second samples RSSI over orientation to point the robot toward the tag; and the third samples RSSI from two antennas pointing in different directions to enable the robot to approach the tag. We justify our formulation using the radar equation and associated literature. We also demonstrate that it has good performance in practice via tests with a PR2 robot from Willow Garage in a house with a variety of tagged household objects.
In-Hand Radio Frequency Identification (RFID) for Robotic Manipulation

(Georgia Institute of Technology, 2013-05) Deyle, Travis ; Tralie, Christopher J. ; Reynolds, Matthew S. ; Kemp, Charles C.

We present a unique multi-antenna RFID reader (a sensor) embedded in a robot's manipulator that is designed to operate with ordinary UHF RFID tags in a short-range, near-field electromagnetic regime. Using specially designed near-field antennas enables our sensor to obtain spatial information from tags at ranges of less than 1 meter. In this work, we characterize the near-field sensor's ability to detect tagged objects in the robots manipulator, present robot behaviors to determine the identity of a grasped object, and investigate how additional RF signal properties can be used for “pre-touch” capabilities such as servoing to grasp an object. The future combination of long-range (far-field) and short-range (near-field) UHF RFID sensing has the potential to enable roboticists to jump-start applications by obviating or supplementing false-positive-prone visual object recognition. These techniques may be especially useful in the healthcare and service sectors, where mis-identification of an object (for example, a medication bottle) could have catastrophic consequences.
Older Adults Medication Management in the Home: How can Robots Help?

(Georgia Institute of Technology, 2013-03) Prakash, Akanksha ; Beer, Jenay M. ; Deyle, Travis ; Smarr, Cory-Ann ; Chen, Tiffany L. ; Mitzner, Tracy L. ; Kemp, Charles C. ; Rogers, Wendy A.

Successful management of medications is critical to maintaining healthy and independent living for older adults. However, medication non-adherence is a common problem with a high risk for severe consequences [5], which can jeopardize older adults’ chances to age in place [1]. Well-designed robots assisting with medication management tasks could support older adults’ independence. Design of successful robots will be enhanced through understanding concerns, attitudes, and preferences for medication assistance tasks. We assessed older adults’ reactions to medication hand-off from a mobile manipulator with 12 participants (68-79 years). We identified factors that affected their attitudes toward a mobile manipulator for supporting general medication management tasks in the home. The older adults were open to robot assistance; however, their preferences varied depending on the nature of the medication management task. For instance, they preferred a robot (over a human) to remind them to take medications, but preferred human assistance for deciding what medication to take and for administering the medication. Factors such as perceptions of one’s own capability and robot reliability influenced their attitudes.
Visual Odometry and Control for an Omnidirectional Mobile Robot with a Downward-Facing Camera

(Georgia Institute of Technology, 2010-10) Killpack, Marc D. ; Deyle, Travis ; Anderson, Cressel D. ; Kemp, Charles C.

An omnidirectional Mecanum base allows for more flexible mobile manipulation. However, slipping of the Mecanum wheels results in poor dead-reckoning estimates from wheel encoders, limiting the accuracy and overall utility of this type of base. We present a system with a downwardfacing camera and light ring to provide robust visual odometry estimates. We mounted the system under the robot which allows it to operate in conditions such as large crowds or low ambient lighting. We demonstrate that the visual odometry estimates are sufficient to generate closed-loop PID (Proportional Integral Derivative) and LQR (Linear Quadratic Regulator) controllers for motion control in three different scenarios: waypoint tracking, small disturbance rejection, and sideways motion. We report quantitative measurements that demonstrate superior control performance when using visual odometry compared to wheel encoders. Finally, we show that this system provides highfidelity odometry estimates and is able to compensate for wheel slip on a four-wheeled omnidirectional mobile robot base.
RFID-Guided Robots for Pervasive Automation

(Georgia Institute of Technology, 2010-01-15) Deyle, Travis ; Nguyen, Hai ; Reynolds, Matt S. ; Kemp, Charles C.

Passive UHF RFID tags are well matched to robots' needs. Unlike lowfrequency (LF) and high-frequency (HF) RFID tags, passive UHF RFID tags are readable from across a room, enabling a mobile robot to efficiently discover and locate them. Using tags' unique IDs, a semantic database, and RF perception via actuated antennas, this paper shows how a robot can reliably interact with people and manipulate labeled objects.
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.
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.
A List of Household Objects for Robotic Retrieval Prioritized by People with ALS

(Georgia Institute of Technology, 2009-06) Choi, Young Sang ; Deyle, Travis ; Chen, Tiffany L. ; Glass, Jonathan D. ; Kemp, Charles C.

Studies have consistently shown that object retrieval would be a valuable task for assistive robots to perform, yet detailed information about the needs of patients with respect to this task has been lacking. In this paper, we present our efforts to better understand the needs of motor impaired patients with amyotrophic lateral sclerosis (ALS) with the goal of informing the design and evaluation of assistive mobile robots. We first describe our results from a needs assessment involving 8 patients from the Emory ALS Center. We provided patients and caregivers with cameras and notepads to document when objects were dropped or were otherwise unreachable in daily life. This study confirmed the importance of robotic retrieval and resulted in documented cases of objects being dropped and out of reach for 1 to 120 minutes. Based on this initial study, we created a questionnaire to assess the importance of various objects for robotic retrieval using the Likert scale. We administered this survey to 25 patients through in-person interviews. These studies culminated in a prioritized list of 43 object classes for robotic retrieval. Using the Friedman test we show that the rankings from the patients are statistically consistent. We present this list and discuss its implications for designing and benchmarking assistive robots.
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.
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.