A9.com, Inc.

Amazon Technologies, Inc.

Canvas Technology, Inc.

Nima Keivan

Jonathan Mcqueen

Karl McDowall

Jacob Blacksberg

Juan Falquez

Andrew E Hoffman

Phillip Walkemeyer

Joshua Bavari

Daniel Sekich

Matthew Ventimiglia

David Hastings Kerr

An autonomous robot moves materials in a warehouse or other industrial environment. It runs on an electric motor powered by a rechargeable battery. When its battery becomes depleted, it maneuvers to a nearby charger. It guides itself to the charger using visual cues, such as a target on or near the charger, until it establishes a good electrical connection with the charger. Proximity sensors on the charger and/or the autonomous robot determine if the autonomous robot is positioned properly; if so, the charger begins charging the autonomous robot's battery. While charging, the charger monitors the resistance of the electrical connection for open- or short-circuit conditions. It also monitors the status of the proximity sensors. If the charger detects an open-circuit or a short-circuit or that the autonomous robot has moved away from the charger, the charger stops charging.

Charging systems and methods for autonomous carts

Systems and methods are disclosed for holonomic drivetrain modules for mobile robots. In one embodiment, an example mobile robot may include a chassis, and a holonomic drivetrain module removably coupled to the chassis. The holonomic drivetrain module may include a first drive wheel having a caster angle of substantially zero, a first bearing block assembly vertically aligned with the first drive wheel, and a first steer motor coupled to the first bearing block assembly and vertically aligned with the first drive wheel.

Holonomic drivetrain modules for mobile robots

Systems, methods, and computer-readable media are disclosed for systems and methods to implement preferred pathways in mobile robots. Example methods may include obtaining, via at least one of a user interface and a corresponding Application Programming Interface API call, at least one preferred pathway for an autonomous mobile robot, transmitting the at least one preferred pathway to the autonomous mobile robot, generating a planned path for the autonomous mobile robot based at least in part on an influence function, the influence function being representative of an amount of bias towards the at least one preferred pathway on a motion planning decision of the autonomous mobile robot, the amount of bias being based at least in part on a metric associated with the at least one preferred pathway, and causing the autonomous mobile robot to move from a start point to an end point along the planned path.

Systems and methods to implement preferred pathways in mobile robots

An autonomous cart moves products and materials in an industrial environment. It is different from conventional carts because it can navigate autonomously indoors or outdoors in dynamic environments where things change frequently. This autonomous cart uses state-of-the-art 'dense' visual perception giving it unequalled and continuous awareness of its surroundings. With this it can operate at a cost, speed, level of safety and efficiency that has never been possible before. This robotic cart makes factories and warehouses more efficient and safer. It enables the movement of smaller batches of material more frequently, reduces the need for expensive conveyor systems, and helps eliminate dangerous and polluting fork trucks from indoor environments.

Autonomous cart for manufacturing and warehouse applications

The disclosure provides systems and methods of use pertaining to a visual mapping and transportation management system for determining a location of a user and directing a vehicle to the user's location. Embodiments include a navigation application installed upon a user's mobile computing device and configured to transmit a user image from the device to an image-matching server storing a map composed of keyframes, each having a stored image, a known geometric pose, and numerous extracted interest features. The server also includes a processor configured to extract interest features from the user image, compare the interest features between the user image and the stored images, identify common interest features between the two, and based on the common interest features and known geometric poses of the stored images, determine a global geometric pose of the user image before directing the vehicle to the user's location. Other embodiments are also disclosed.

Growing community of inventors

Boulder, CO, United States of America

Nima Keivan