A wireless system-on-chip with integrated antenna, power harvesting and biosensors is presented that is small enough, 200μm × 200μm × 100μm, to allow painless injection. Small device size is enabled by: a 13μm × 20μm 1nA current reference; optical clock recovery; low voltage inverting dc-dc to enable use of higher quantum efficiency diodes; on-chip resonant 2.4GHz antenna; and array scanning reader. In-vivo power and data transfer is demonstrated and linear glucose concentration recordings reported.

INTRODUCTION:Many older adults wish to age-in-place. Robot assistance at home may be beneficial for older adults who are experiencing limitations in performing home activities. In this study we investigate older Americans' robot acceptance before and after exposure to a domestic mobile manipulator, with an emphasis on understanding trialability (i.e., "trying out" a robot for a short time period) and result demonstrability (i.e., observing the results of the robot's functionality).METHOD:Older adult participants observed a mobile manipulator robot autonomously demonstrating three tasks: delivering medication, learning to turn off a light switch, and organizing home objects. We administered pre and post exposure questionnaires about participants' opinions and attitudes toward the robot, as well as a semi-structured interview about each demonstration.RESULTS:We found that demonstration of a mobile manipulator assistive robot did, in fact, influence older adults' acceptance. There was a significant increase, pre vs. post, in positive perceptions of robot usefulness and ease of use for 8 of the 12 Robot Opinions Questionnaire items. Furthermore, in the Assistance Preference Checklist, eighteen tasks significantly differed between pre and post exposure, with older adults showing a greater openness to robot assistance after exposure to the robot.CONCLUSION:Thus, demonstration of robot capability positively affected older adults' preferences for robot assistance for tasks in the home. Interview data suggest that the robot's capability and reliability influenced older adults' first impressions of the robot.

We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.

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 properties 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.

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 ifluenced their attitudes.

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.

In this paper we present the first fully passive (battery-free) wireless transmission of multiple digital audio channels and images via modulated backscatter. We leverage a previously reported single chip, passive transponder that can digitize and uplink up to 10 analog input channels sampled at a rate of 26.1 kHz. Given a base station transceiver operating at a frequency of 915 MHz and a transmit power of +36 dBm EIRP, the transponder has a demonstrated operating range of ≈1.4 m. The transponder data uplink uses binary phase-shift key (BPSK) modulated backscatter operating at a total link throughput rate of 5 Mbps, with an uplink energy consumption of only 3.7 pJ/bit. The transponder was initially designed for biomedical telemetry of neural and EMG signals. We present a new application of this tag for multichannel, high fidelity digital audio recording, as well as color image transfer using a slow-scan television (SSTV) modulation (PD290) with a resolution of 640 by 493 pixels. Additionally, we demonstrate fully-passive digital recording of ambient sound using a microphone powered by the chip's harvested energy at an operating range of 0.72 m. The passive, digital microphone is sensitive enough to record human speech within approximately 5 m of the device. We believe these results will serve as a first step toward media-rich battery-free (wirelessly powered) devices that take advantage of the high speed, low power nature of modulated backscatter communication links.

We propose to leverage UHF RFID techniques to yield a continuously wearable, battery-free wireless multichannel ECG telemetry device that is potentially disposable, low-cost and suitable for integration with multiple electrodes in a flexible circuit assembly. Such a device could have broad applicability, ranging from initial patient assessment by first responders, to continuous monitoring in various clinical settings. We employ a recently described single-chip data acquisition system including RF power harvesting to eliminate the need for a battery. The single-chip system includes 14 channels of integrated biopotential amplification, an 11-bit ADC, and a 5 Mbps digital backscatter telemetry link. We present an initial characterization of the telemetry chip in this application including battery-free, wireless 3 and 5 channel ECG recordings made from an ambulatory human subject at a range of ≈ 1 meter.

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 downward-facing 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 high-fidelity odometry estimates and is able to compensate for wheel slip on a four-wheeled omnidirectional mobile robot base.

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.

©2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). The original publication is available at : http://www.aaai.org/Papers/Symposia/Spring/2009/SS-09-03/SS09-03-003.pdf.

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.

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.

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.

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.

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 multi-path 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 rangefinder. 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 6.1deg mean bearing error and 0.69m mean range error over robot to tag distances of over 4m 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.