Bae Systems Information and Electronic Systems Integration Inc.

Amazon Technologies, Inc.

Diane Grieselhuber Mills

John Gregory Longtine

Andrew Stubbs

Matthew David Verminski

Parris S Wellman

Thomas P McElwain

Dianne E Egnor

Apurva N Mody

Joseph W Durham

Steven Augustine Wilson

Stephen Robert Blatt

Scott Dresser

Geoffrey S Edelson

Ned B Thammakhoune

Rachel E Learned

Robert B MacLeod

David L Herrick

Kari M Karwedsky

Michael J Kotrlik

Cory S Myers

Geoff S Edelson

A transfer rack on one floor of an inventory system can receive multiple containers each carrying an inventory item designated for an operation on a different floor of the inventory system. A vertical lift can move the transfer rack carrying the multiple containers from the one floor to the different floor. On the different floor, the multiple containers may be swapped for different multiple containers each carrying an inventory item designated for an operation on the one floor. The vertical lift may return the transfer rack carrying the different multiple containers to the one floor.

Amassing pick and/or storage task density for inter-floor transfer

Item information such as an identity or arrangement of a selected inventory item of an inventory system can be determined. A classification of the selected inventory item can be obtained based at least in part on the item information. The selected inventory item can be routed based at least in part on the classification of the selected inventory item. For example, the selected inventory item may be routed for manual handling if the classification of the selected inventory item indicates that the selected inventory item is designated for an operation to be performed relative to the selected inventory item by a human operator. Alternatively, the selected inventory item may be routed for automated handling if the classification of the selected inventory item indicates that the selected inventory item is designated for an operation to be performed relative to the selected inventory item by a robotic manipulator.

Routing based on automation capabilities in inventory system

Robotic manipulators may be used to manipulate objects. Manipulation data about manipulations performed on objects may be generated and accessed. This data may be analyzed to generate a profile indicating how an object may be manipulated. A portion of the profile may be transmitted to a particular robotic manipulator. For example, the portion may be based on a manipulation capability of the robotic manipulator. In turn, the robotic manipulator may use the portion of the profile to manipulate the object.

Networked robotic manipulators

Embodiments are described for implementing a mobile inventory transfer station within a workspace of an inventory management system. In embodiments, inventory transfer instructions for moving inventory stored within container holders in a workspace may be received. A portion of container holders within the workspace may be identified based on characteristics associated with the items of the inventory transfer instructions and first information identifying the items stored in the portion of container holders. A mobile container unit and a mobile manipulator unit may be selected based on distances between the units and the identified portion of container holders within the workspace. Instructions may be transmitted to the mobile container unit and mobile manipulator unit for navigating to the identified portion of container holders in the workspace and transferring inventory between a particular container holder and a container associated with the mobile container unit by the mobile manipulator unit.

Mobile robot manipulator

A method and system of cognitive communication for generating non-interfering transmission, includes conducting radio scene analysis to find grey spaces using external signal parameters for incoming signal analysis without having to decode incoming signals. The disclosed cognitive communications system combines the areas of communications, signal processing, pattern classification and machine learning to detect the signals in the given spectrum of interests, extracts their features, classifies the signals in types, learns the salient characteristics and patterns of the signal and predicts their future behaviors. In the process of signal analysis, a classifier is employed for classifying the signals. The designing of such a classifier is initially performed based on selection of features of a signal detected and by selecting a model of the classifier.

Cognitive radio methodology, physical layer policies and machine learning

In a method of cognitive communication for non-interfering transmission, wherein the improvement comprises the step of conducting radio scene analysis to find not just the spectrum holes or White spaces; but also to use the signal classification, machine learning and prediction information to learn more things about the existing signals and its underlying protocols, to find the Gray space, hence utilizing the signal space, consisting of space, time, frequency (spectrum), code and location more efficiently.

Interference cancellation techniques for use in a wireless multicarrier communications system where signals from multiple wireless networks might be present and interfering with the detection and demodulation processes are disclosed. Interference cancellation is applied to the interfering network before removing same-system interference. By removing the contribution of all interfering systems' from a receiver's aggregate signal, the receiver is capable of detection and demodulation. Performance can be further improved by including an additional level of interference cancellation within the system of interest to separate the individual user of interest.

Cross-system interference cancellation for multicarrier CDMA and OFDM

Techniques for multiple access differential M-ary coding applications that can be optionally aided by multiuser detection (MUD) are disclosed. An initial hopped feature decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation can then be performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.

Multiuser detection aided multiple access differential M-ary coding applications

A system is presented that provides real-time performance for iterative multi-user detectors, such as Turbo MUDs, which are used to separate simultaneous transmissions on the same frequency, by permitting the MUD to use a less computationally intense, fast-processing algorithm and to correct for errors caused by the fast processing. In order to reduce the errors, a voting system is coupled to the output of the multi-user detector within the iterative system. The voting system provides confidence values on a bit-by-bit basis for the estimates made by the multi-user detector, with the confidence values then being utilized as soft inputs to a bank of conventional single-user decoders.

Voting system for improving the performance of single-user decoders within an iterative multi-user detection system

An error correction decoding (ECC) processing scheme is disclosed that reduces computational complexity normally associated with multiuser detection (e.g., TurboMUD) solutions, without causing degradation in quality of service or decreasing the total throughput. Error correction decoding algorithms are applied only to portions of the estimates that were affected by the immediately previous MUD update process. Even though the MUD and/or ECC updating is targeted so as to reduce complexity of each iteration, all of the estimates are maintained and remain candidates for future updates. As such, there is no negative impact real-time or future performance. This targeting approach can be used in conjunction with many variations of MUD, including full-complexity or reduced complexity, and may include MUD with confidence ordering or voting, and other techniques for facilitating efficient and effective MUD processing.

Multiuser detection with targeted error correction coding

Techniques for multiple access differential frequency-hopped spread spectrum (MA-DFHSS) aided by multiuser detection (MUD) are disclosed. An initial DFH decoding is performed to provide data estimates for each user represented in a received co-channel signal. Interference cancellation is then performed using MUD, thereby providing an interference-cancelled signal. Data estimates remaining are then re-decoded. Iteration on the interference cancellation and re-decoding can be carried out to satisfy a particular rule of iteration, although iteration is not always necessary. The final decoded signal can then be provided to its destination.

Multiuser detection aided multiple access differential frequency-hopped spread spectrum

A multi-user turbo decoder combining multi-user detection and forward error correction decoding is disclosed in which randomly ordered indices are assigned to interfering users before a decoding tree is constructed in the multi-user decoder for each symbol interval for every iteration and for each new block of data. By building the decoding tree in this manner for each symbol interval, a reduced complexity search is more likely to include paths (and nodes) in the tree containing the correct value for the channel symbols. All users thus share in the benefit of root level placement in the decoding tree. In an alternative embodiment of the invention only one decoding pass is accomplished and there is no re-construction of the decoding tree based on further random index ordering for iterative decoding. No modification to the transmitted signaling method is needed.

Method and apparatus for random shuffled turbo multiuser detector

A reduced complexity Turbo multi-user detector (MUD) processing system in multiple access communications channels that decreases the likelihood of improper decoding of the final values of interest and decreases the computation complexity for each iteration, thereby allowing for a reduction in the number of iterations performed and lowers the overall complexity without negatively impacting performance. In one form the present invention comprises a multi-user detector coupled to two or more decoder sections, two ore more recoders, and a compare and adjust section in such a manner that data flows iteratively to correct for errors in a computationally efficient manner.

Reduced complexity multi-turbo multi-user detector

A method and system begin at a random node of a trellis model having a set of at least two axes, wherein one axis corresponds to time and a second axis corresponds to frequency, a set of M states, corresponding to a set of all possible frequencies that may be transmitted by the system, and branches leaving each state, that terminate at allowable transmit frequencies for a next frequency hop. The number of branches leaving each state is dependent on the number of encoded bits per hop. Next, B bits of data are passed to a buffer, where B is the number of bits that will be transmitted per frequency hop. The method and system then combine the B bits of data with information for previously selected frequencies to select a current frequency, transmit the selected frequency, and feedback the currently selected frequency information to be used as previously selected frequency information when selecting a next frequency.

Growing community of inventors

Wilmington, MA, United States of America

Diane Grieselhuber Mills