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.

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.

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.

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.

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.

We introduce an inexpensive, low complexity power surface system capable of simultaneously providing wireless power and bidirectional communication from a surface to multiple mobile robots. This system enables continuous operation of a swarm-sized population of battery-less robots. Our first prototype consists of a 60 cm times 60 cm power surface that provides power and bidirectional communication to an initial evaluation group of five test robots, each one consuming 200 mW. Unlike typical non-resonant inductive (transformer) coupling, power transmission in this system is achieved through magnetic flux coupling between a high Q L-C resonator placed beneath the operating surface and a non-resonant pickup coil on each robot. We explore the design of the pickup coil and conditioning circuitry, and we characterize the position-dependent power density of a static load representative of a small autonomous robot operating on the surface. We demonstrate a continuous power density averaging 4.1 mW/cm2for a static load, and develop much greater peak power for dynamic loads via capacitor storage and power conditioning circuitry. We also demonstrate simultaneous broadcast communication between the surface and all robots via amplitude modulation of the magnetic field, and communication between individual robots and the surface via load modulation.

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.

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.