Ford Global Technolgoies, LLC

Honeywell International Inc.

Anthony Mario D'Amato

Dimitar Petrov Filev

John Ottavio Michelini

Jonathan Thomas Mullen

Steven Joseph Szwabowski

Shankar Narayan Mohan

Jianbo Lu

John Jeffrey Pfeiffer

Kenneth Yesh

Fling Finn Tseng

Raj Sohmshetty

Mario Anthony Santillo

Jaroslav Pekar

Levasseur Tellis

David Jeffeory Berels

Meghna Menon

Ondrej Santin

Gregory P Linkowski

Harold Bernard Samuels

Imad Hassan Makki

Mathew Alan Boesch

Yan Wang

Pankaj Kumar

Joseph F Stanek

Timothy Mark Feldkamp

Karin Lovett

Colm Peter Boran

Medville Jay Throop

Mark Warren Muddiman

Ryan Lee Baker

Steven Schuchard

Sophia Dancel

Marty Smets

A method to manage a vehicle is disclosed. The method may include obtaining vehicle inputs that may include an image captured by a vehicle camera. The method may further include estimating a time delay in teleoperation communication with the vehicle, and generating an augmented reality image based on the time delay and the vehicle inputs. The method may further include rendering the augmented reality image on a user interface to manage the vehicle.

Augmented reality for teleoperation

A system for reconfiguring a factory having equipment at different workstations throughout the factory and a plurality of sensors disposed throughout the factory includes a factory configuration module configured to store a plurality of predetermined factory configurations and a plurality of mobile transporters configured to engage and transport the equipment to the different workstations throughout the factory based on the predetermined factory configurations and dynamic inputs, where the dynamic inputs include a status of the equipment, a status of the plurality of mobile transporters, sensor data output by the plurality of sensors, or a combination thereof.

Deployable mobile transporters for easy plant reconfiguration

A robotic system includes a robotic arm, a robotic end-effector detachably coupled to the robotic arm, and a control system. The robotic end-effector includes a locomotion device to move the robotic end-effector independent of the robotic arm, and the control system configured to control the robotic arm and the robotic end-effector to detach and attach the robotic arm and the robotic end-effector.

Robotic arm with a detachable and mobile end-effector

A system and method for reconfiguring a factory having equipment at different workstations throughout the factory includes a plurality of mobile transporters configured to engage and transport the equipment to the different workstations throughout the factory. Each mobile transporter includes a transmitter, a receiver, at least one proximity sensor, and an engagement mechanism for engaging the equipment. A factory configuration module includes a 3D model of the factory and a plurality of predetermined factory configurations. A supervisory control module is in communication with the plurality of mobile transporters, the equipment, and the factory configuration module. The plurality of mobile transporters are configured to receive instructions from the supervisory control module to engage and reposition the equipment throughout the factory based on the predetermined factory configurations and dynamic inputs.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to identify patterns in first high anticipation scenarios based on user identification, wherein high anticipation scenarios include video sequences wherein a first vehicle will be within a specified distance of a first object in a first environment around the first vehicle, wherein user identification is determined by viewing portions of a respective video sequence. A first model including a first deep neural network can be trained to determine second high anticipation scenarios based on the patterns identified in the first high anticipation scenarios and a second model including a second deep neural network can be trained to modify locations and velocities of second objects in the first high anticipation scenarios and output modified high anticipation scenarios. The computer can be further programmed to train a third model including a third deep neural network to operate a vehicle based on the modified high anticipation scenarios output by the second model.

Vehicle operation labeling

The present disclosure is directed toward a robotic system that includes a robotic arm, a robotic end-effector, a lock, and a control system. The robotic end-effector is detachably coupled to the robotic arm and includes a locomotion device to move the robotic end-effector independent of the robotic arm. The lock is disposed with the robotic arm and robotic end-effector, and is operable to detach and attach the robotic arm and the robotic end-effector. The control system is configured to control the robotic arm and the robotic end-effector, and to operate the lock to detach and attach the robotic arm and the robotic end-effector.

A system including a computer that includes a processor and a memory storing instructions executable by the processor to determine a location in a defined area according to coordinates on a map. The instructions include instructions to actuate a robot to move to the location. The instructions include instructions to identify an object specified in a digital model of the area, stored at a remote server, to be at the coordinates for the location. The instructions include instructions to actuate a sensor on the robot to collect data at the location. The instructions include instructions to update the object in the digital model based on the collected data.

Digital model rectification

The present disclosure is directed toward, a method that includes receiving a travel path for a robot in a facility, selecting one or more target projectors disposed along the travel path, and displaying, by the one or more target projectors, one or more visual markers along the travel path of the robot to communicate an intended movement of the robot. The one or more target projectors are selected from among a plurality of projectors disposed in the facility.

Intent communication for automated guided vehicles

Methods and systems are provided for managing torque arbitration for a hybrid powertrain. In one example, a method may include operating the hybrid powertrain over a predetermined route with a torque arbitration, and updating the torque arbitration based on a vehicle mass.

Method and system for torque management in hybrid vehicle

Various embodiments of the present disclosure provide a ride height sensing system that includes an encoded electromagnetic source as an input unit for a Hall Effect sensors and the signal from the electromagnet is encoded to enable the rejection of ambient magnetic field. In various embodiments, the ride height sensing system includes a self-calibration system including at least one Hall effect sensor mounted at a fixed distance and orientation with respect to the electromagnetic source to provide for continuous calibration of the system. In one embodiment, the enhanced ride height sensing system includes at least two inertial measurement units, such as accelerometers mounted to each of the body frame of the vehicle and the axle frame of the vehicle. This enables the system to optimize power usage by more accurately and efficiently measuring the ride height at high frequency rates with relatively lower power consumption.

Extensions and performance improvements for non-contact ride height sensing

Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range while preventing the unnecessary downshifts. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.

Method and system for controlling shifting of a vehicle in cruise control

Systems and methods for communicating data to a control system of a vehicle are described. A system may include a server, which generates a set of coefficients using an approximation function and based on path-preview data indexed to future values of a road attribute with respect to a road traveled by the vehicle. The system may also include a client, which utilizes an evaluation of the approximation function to direct the control system of the vehicle.

Compression algorithms for vehicle-bus-messaging of preview data

A vehicle system includes a processor having a memory. The processor is programmed to predict a vehicle key-on time based on a next destination of a vehicle. The processor is further programmed to request, at a predetermined amount of time before the key-on time, optimization data from a remote server.

Predictive vehicle task scheduling

Method and system are provided for vehicle route planning based on adaptive model predictive control. In one example, a method may include real-time online identification of the vehicle model base on the vehicle inputs and outputs; compression of the input space to increase the optimization efficiency; and optimization of the route planning based on the model parameter of the vehicle and the known road grade.

Adaptive model predictive control for vehicle route planning

Traffic incident information may be clustered to determine geographical locations in which a statistically-increased level of traffic incidents occur. Indices indicative of vehicle operating conditions within the locations may be generated according to crowd-sourced event information factors and an index function including coefficients defining contributions of the factors to the indices. The coefficients may be optimized to minimize error between the index-predicted statistically-increased level areas and the clustered traffic incident information.

Growing community of inventors

Canton, MI, United States of America

Anthony Mario D'Amato