Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A vehicle comprising an electric motor configured to drive power to rear wheels of the vehicle. The vehicle also includes a mechanical disconnect configured to engage, when closed, a plurality of half shafts corresponding to the rear wheels and a drive shaft coupled to a differential, and to engage, when open, the drive shaft but not the plurality of half shafts.

Abstract: 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.

Abstract: 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.