Ford Global Technolgoies, LLC

Sanghyun Hong

Jianbo Lu

Smruti Panigrahi

Timothy Mark Feldkamp

Justin Miller, Jr

Justin Miller

Kurt Michael Lundeen

Dimitar Petrov Filev

Yifan Chen

Raj Sohmshetty

Pavithra Madhavan

Mitch McConnell

Ling Zhu

Jonathan Scott

Kevin Hwang

Zhengyu Dai

Jonathan Sullivan

Adam Wint

Zhijun Han

A docking system is provided. The docking system includes a docking station and an autonomous mobile robot (AMR). The AMR includes a processor, a camera electrically connected to the processor, a lidar system electrically connected to the processor, and a memory. The memory may have instructions that, when executed by the processor, cause the processor to perform operations including employ the camera and the lidar system to determine an offset position and a final position of the AMR with respect to the docking station, and dock the AMR at the docking station using the offset and final positions.

Docking system and associated method

Autonomous modular robots and methods of use are disclosed herein. An example system includes a first assembly includes a first controller having a first processor that causes a first assembly support to translate along a first axis and align with a container placed on the first assembly support which is connected to a third assembly. The third assembly is configured to release a lid when the container has been engaged with the lid. The lid and container form a second assembly that includes a second processor and a second sensor assembly located in the lid. A transfer assembly can transfer an object into and out of the container. The first processor can determine a position and orientation of a delivery platform, planned movement and position of the second assembly, and planned operations of the transfer assembly in order to deliver a package to the delivery platform.

Autonomous modular robots and methods of use

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 disclosure generally pertains to a robotic vehicle. An example robotic vehicle has a base platform that includes at least a chassis, an actuator, a sensor, and a controller. The chassis includes a first wheel that is attached to a first section of the chassis and further includes a second wheel attached to a second section of the chassis. The actuator has a proximal end attached to the first section of the chassis and a distal end attached to the second section of the chassis. The sensor is configured to obtain information associated with a stair structure located on a traversal path of the robotic vehicle. The controller evaluates the information obtained by the sensor and operates the actuator to vary a separation distance between the first section of the chassis and the second section of the chassis so as to enable the robotic vehicle to traverse the stair structure.

Stair climbing robotic vehicle

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 distributed control system for an autonomous modular robot (AMR) vehicle includes a top module processor disposed in communication with a lower module processor, and memory for storing executable instructions of the top module processor and the lower module processor. The instructions are executable to cause the top module processor and the lower module processor to navigate a bottom module, via the bottom module processor, the AMR vehicle to a target destination. The instructions are further executable to determine, via the bottom module processor, that the AMR vehicle is localized at a target destination, transmit a request for a cargo unloading instruction set, and receive, via a top module processor, a response to a cargo unloading instruction set sent from the bottom module processor. The instructions further cause the top module processor to unload the cargo to a target destination surface via an unloading mechanism associated with the top module.

Modular autonomous robot distributed control

The present disclosure is directed to multi-use mobile robots that cater to both goods delivery and people mobility. These robots allow for seamless and dynamic transition between a rideable personal transport vehicle that may be driven by a user sitting in a removable robot seat, driven by the user standing onboard the vehicle, or driven by a second individual walking behind the multi-use robot. The multi-use robot may also function as an autonomous delivery vehicle, and can provide an option to allow both people and goods to be transported simultaneously. The multi-use robots can also integrate with mobile warehouses and neighborhood storage lockers and mobile warehouses, and provide last-mile handling of goods and supplies.

Multi-use mobile robot and methods of use

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

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

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.

Growing community of inventors

Ann Arbor, MI, United States of America

Sanghyun Hong