Ford Global Technolgoies, LLC

Ford Motor Company Limited

University of Michigan

Ford Global Technologies & Ford Motor Company

Volvo Car Corporation

Lu, Jianbo

Jacobson, Bengt

Rupp, Jeffrey D.

Johansson, Olle

Rhode, Douglas S.

Geurink, Mathijs

Meyers, Joseph C.

Jianbo Lu

Dimitar Petrov Filev

Todd Allen Brown

Kwaku O Prakah-Asante

Joseph Carr Meyers

Sanghyun Hong

Fling Finn Tseng

Davor Hrovat

Keith Glenn Mattson

Albert Chenouda Salib

David John Messih

Timothy Mark Feldkamp

Smruti Panigrahi

Eric Hongtei Tseng

Michael Edward Brewer

Finn Finn Tseng

Erik Christopher Chubb

Li Xu

Steven Joseph Szwabowski

Jeffrey Dan Rupp

Justin Miller, Jr

Justin Miller

Ryan Abraham McGee

Thomas Edward Pilutti

Hongtei Eric Tseng

Hai Yu

Mitch McConnell

Otto Hofmann

Gilberto Burgio

Perry Robinson Macneille

John Ottavio Michelini

Yifan Chen

Raj Sohmshetty

Zhengyu Dai

Douglas Scott Rhode

Todd Nelson Clark

Anthony Mario D'Amato

Uwe Hoffmann

Michael Seemann

Kurt Michael Lundeen

Hassen Hammoud

Simon Baales

Pavithra Madhavan

Jason Shomsky

Fazal Urrahman Syed

Imad Hassan Makki

Shane Elwart

Aaron Lewis Mills

Gerald H Engelman

Joseph F Stanek

Timothy Gerard Offerle

David Jeffeory Berels

Xiao Guang Yang

Peter W A Zegelaar

Mohsen Lakehal-Ayat

Ling Zhu

Ryan Lee Baker

Jonathan Scott

Liwen Xu

Jeffrey Allen Doering

Thomas George Leone

Ming Lang Kuang

Kenneth James Miller

Manoharprasad K Rao

Alex O'Connor Gibson

Gregory Michael Pietron

Yuji Fujii

Stefano Di Cairano

Gary Steven Strumolo

Ilya V Kolmanovsky

Hong Jiang

Felix Nedorezov

Wei Liang

Diana Yanakiev

Mathew Alan Boesch

Joseph F Kucharski

Hsin-Hsiang Yang

Pankaj Kumar

Roger Lyle Huffmaster

Gaurav Pandey

Levasseur Tellis

David Michael Russell

Jeffrey Allen Greenberg

Nimrod Kapas

Medville Jay Throop

Stephen William Rouhana

Meghna Menon

Christopher Scott Nave

Pratima V Addepalli

James Matthew Marcicki

Gregory Philip Brown

Jonathan Sullivan

Dexin Wang

David Scott Rohweder

Scott Mayberry

James McBride

Stephen Varghese Samuel

Devang Bhalchandra Dave

David M Bevly

Daniel Domek Eisele

A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to determine a perception zone of interest, via a trained perception model, based on at least one of a vehicle parameter or planning data, wherein the vehicle parameter comprises at least one of a vehicle direction or a vehicle speed and the planning data comprises a route and trajectory to be traversed by the vehicle; determine a vehicle route and trajectory based on a subset of sensor data, wherein the subset of sensor data corresponds to the perception zone of interest; and operate the vehicle to traverse the vehicle route and trajectory.

Adaptive perception by vehicle sensors

The present invention extends to methods, systems, and computer program products for path planning for autonomous moving devices. Aspects of the invention include planning a path for a mobile robot to move autonomously in an environment that includes other static and moving obstacles, such as, for example, other mobile devices and pedestrians, without reference to a prior map of the environment. A planned path for a mobile robot can be determined, adjusted, and adapted using diffusion maps to avoid collisions while making progress towards a global destination. Path planning can include using transition probabilities between grid points to find a feasible path through parts of the environment to make progress towards the global destination. In one aspect, diffusion maps are used in combination with a receding horizon approach, including computing diffusion maps at specified time intervals.

Path planning for autonomous moving devices

Scenario discriminative hybrid motion control for robots and methods of use are disclosed herein. A method may include determining a number of objects in a space, determining when a goal is within the space, and selectively switching between a plurality of control schemes based on the number of objects in the space and whether the goal is within the space. The plurality of control schemes including a model predictive control scheme, a simplified model predictive control scheme, and a proportional-integral-derivative scheme. Selectively switching between the plurality of control schemes reduces power consumption of an automated system compared to when the automated system utilizes only the model predictive control scheme.

Scenario discriminative hybrid motion control for mobile robots

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 battery expansion cradle is attachable to an electric scooter (eScooter) handlebar. The battery expansion cradle includes a battery connection terminal disposed on a face of the battery expansion cradle, where the battery connection terminal electrically connects with a power bus of the electric scooter. A battery module is removably attachable to the face of the battery expansion cradle and the back terminal of another battery. The battery module includes a connector electrically coupling the first rechargeable battery to the eScooter power bus, and includes a mobile device holder disposed on a face of the first battery module with holding means for securing a mobile device to the face of the battery module, which may be offset from the center of the external battery to allow for a clear forward view of the scooter using the smartphone's front camera, and the user's face using the rear camera.

Smartphone and battery integration module for an electric scooter

This disclosure is generally directed to systems and methods for generating a last mile travel route for a delivery robot. In an example embodiment, an individual captures an image of a frontage of a property. The image may include a package drop-off spot where a package can be dropped off by the delivery robot. The individual may also insert labels and/or illustrations into the image for conveying information such as object descriptors (front door, drop-off spot, etc.), traversable areas (driveway, walkway, etc.), and non-traversable areas (lawn, flower bed, etc.). The image is converted by a computer into a travel map that the delivery robot can use to identify a travel route through the frontage area of the property. The travel map may be provided in various forms such as a semantic map of the frontage area or a bird's eye view rendering of the frontage area.

Systems and methods to generate a last mile travel route for a delivery robot

A method for controlling a robotic vehicle in a delivery environment includes causing the robotic vehicle to deploy from an autonomous vehicle (AV) at a first AV position in the delivery environment. The method further includes localizing, via a robotic vehicle controller, an initial position within a global reference map using a robot vehicle perception system, receiving, from the AV, a 3-dimensional (3D) augmented map and localizing an updated position in the delivery environment based on the 3D augmented map and the global reference map. The robot vehicle perception system senses obstacle characteristics, and generates a unified 3D augmented map with robot-sensed obstacle characteristics. The method further includes generating a dynamic path plan to a package delivery destination using the unified 3D augmented map, and actuating the robot vehicle to the package delivery destination according to the dynamic path plan.

Autonomous vehicle and infrastructure aided robotic system for end-to-end package delivery

Systems and methods for fleet inspection and maintenance using a robot are provided. The robot may detect a maintenance issue of a vehicle of a fleet of vehicles via one or more sensors, generate a navigation route to a position proximal to the maintenance issue of the vehicle, traverse along the navigation route to the position, and execute a maintenance to rectify the maintenance issue of the vehicle. The robot may include a mobile base removably coupled to a modular platform for performing a maintenance task.

Fleet inspection and maintenance mobile robot

This disclosure is generally directed to generating travel route information that is interpretable by a delivery robot for traversing a last mile of a delivery. In an example embodiment, an individual captures a video clip while moving along a travel route that is preferred by the individual for use by the delivery robot. The video clip is converted into a digital route map that the delivery robot uses to reach a package drop-off location on the property. In another example embodiment, an individual captures an image of a portion of the property and appends oral instructions to reach the package drop-off location. The image and the oral instructions are converted into a digital route map for use by the delivery robot. In yet another example embodiment, markers affixed to a surface of a traversable area on the property are used by the delivery robot to reach the package drop-off location.

Systems and methods to provide last mile assistance to a delivery robot

Present embodiments describe a method for controlling a robotic vehicle that can include causing a processor to determine a localized position of a dynamic object, determining, via the processor, a predicted trajectory of the robotic vehicle based on the localized position of the dynamic object, and determining an optimum trajectory based on the predicted trajectory, the optimum trajectory chosen based a travel velocity and longitudinal velocity. Determining the optimum trajectory can include computing a cost value comprising a plurality of cost terms associated with the optimum trajectory, and determining a control command that modifies a travel velocity and a travel vector of the robotic vehicle based on the cost value. The method further includes causing, via the processor, the robotic vehicle to follow the optimal trajectory based on the control command.

Predictive time horizon robotic motion control

Systems and methods for administering a hazard condition warning during a package delivery operation are provided. The system includes one or more sensors, e.g., radar sensors or cameras, configured to detect a hazard condition in a vicinity of a delivery vehicle and to generate data indicative of the hazard condition. The system further includes an operator sensor configured to detect a location of an operator relative to the delivery vehicle and one or more indicators, e.g., lights, a mobile application installed on a mobile phone, or an HMI of the vehicle, configured to generate an alert corresponding to the hazard condition. A processor of the system may determine whether the hazard condition is present based on the data indicative of the hazard condition, and cause, based on a presence of the hazard condition and the detected location of the operator, the one or more indicators to generate the alert.

Hazard condition warning for package delivery operation

Defrost system for smart locker in package delivery are disclosed herein. An example method can include receiving a delivery message from a delivery unit that a package is to be delivered to a locker unit, the delivery message including an expected delivery time, determining a frozen condition for the locker unit, and activating a defroster element associated with the locker unit before delivery of the package by the delivery unit according to expected delivery time.

Defrost system for smart locker in package delivery

A nighttime delivery system is configured to provide delivery of packages during night hours, in low or no-light conditions using a multi-mode autonomous and remotely controllable delivery robot. The system involves the use of a baseline lighting system onboard the robot chassis, with variable light intensity from single flash and floodlighting, to thermal imaging using infrared cameras. The lighting system changes according to various environmental factors, and may utilize multi-mode lighting techniques to capture single-shot images with a flash light source to aid in the robot's perception when the vehicle is unable to navigate using infrared (IR) images.

Systems and methods for nighttime delivery mobile robot with hybrid infrared and flash lighting

The disclosure generally pertains to travel planning for a multifunction robot that can travel on a ground surface and can fly over obstacles. In an example embodiment, a controller of the multifunction robot receives an Occupancy Grid map that provides information about a travel area to be traversed by the multifunctional robot. The controller may determine a first cost associated with a first travel route that involves the multifunctional robot driving around a 3D object when moving along the ground from an origination spot to a destination spot in the travel area. The controller may further determine a second cost associated with a second travel route that involves the multifunctional robot flying over the 3D object when moving from the origination spot to the destination spot. The controller may select either the first travel route or the second travel route based on comparing the first cost to the second cost.

Systems and methods for planning a travel route of a multifunctional robot

Systems and methods that allow a smartphone to be used as an imaging device for undercarriage inspection of a moving vehicle are provided. The method may include locating the smartphone on the ground via one or more sensors of the vehicle. The vehicle may generate a path for the vehicle to drive over the smartphone based on the location of the smartphone, and optionally display the path to facilitate manual driving of the vehicle by the driver over the smartphone. Alternatively, the vehicle may self-drive to follow the path. The smartphone may capture image data indicative of the undercarriage of the vehicle, inspect and analyze the image data to identify one or more issues of the undercarriage of the vehicle, and transmit the analyzed image data to the vehicle for display. The driver may confirm the one or more issues and transmit the data to an inspection professional for additional assistance if needed.

Systems and methods for underbody inspection of a moving vehicle with a smartphone

Growing community of inventors

Livonia, MI, United States of America

Jianbo Lu