Intel Corporation

Leobardo E Campos Macias

David Gomez Gutierrez

Rafael De La Guardia Gonzalez

Jose I Parra Vilchis

Rodrigo Aldana Lopez

Julio Cesar Zamora Esquivel

Anthony Kyung Guzman Leguel

Javier Felip Leon

Hector A Cordourier Maruri

Jonathan J Huang

David Israel Gonzalez Aguirre

Jose Rodrigo Camacho Perez

Paulo Lopez Meyer

Edgar Macias Garcia

Kshitij Arun Doshi

Carl S Marshall

Dave A Cavalcanti

David Arditti Ilitzky

Omesh Tickoo

Daniel Pohl

Javier Sebastian Turek

Nilesh Amar Ahuja

Jesus Cruz Vargas

Julio Zamora Esquivel

David Gómez Gutiérrez

Jan Willem Vervoorst

Linda Patricia Osuna Ibarra

Andreas Hippelein

Humberto Caballero Barragan

Technologies for generative AI for warehouse picking are disclosed. In an illustrative embodiment, a robot can partially or fully autonomously perform object picking in a warehouse. A description of an object to be picked can be sent to a compute device controlling the robot. The compute device can direct the robot to move to where the object is located. The robot can then take a picture that can be analyzed. A text description as well as the image is encoded and provided to a transformer. The transformer generates an output vector indicating, e.g., where an end effector should grab hold of an object. A goal generator can then determine a pose for the end effector based on the output latent vector. A planner can move the end effector to the determined pose. A new picture can be taken, and the cycle can repeat until the item is picked.

Technologies for generative AI for warehouse picking

Various systems and methods for implementing an enhanced occupant collision safety system in a vehicle are described herein. A system for controlling deployment of vehicle safety mechanisms of a host vehicle, includes a sensor array interface to receive acceleration data from an impact sensor; an image processor to receive events from an event camera, the event camera configured to monitor a scene of an interior of the host vehicle that includes an occupant; and an occupant safety system controller to: cluster the events to discriminate between events associated with a background of the scene and events associated with a body part of the occupant; detect a collision based on the acceleration data; verify the collision based on the events associated with the background of the scene; and in response to verifying the collision, cause a collision safety mechanism of the host vehicle to deploy.

Enhanced occupant collision safety system

A localization system includes a first sensor, configured to detect a vibration and generate a first electrical signal in response to the detection of the vibration; a second sensor, configured to detect a vibration and generate a second electrical signal in response to the detection of the vibration; a third sensor, configured to detect a vibration and generate a third electrical signal in response to the detection of the vibration; and

Localization system and method

A method for calculating a time to contact of an autonomous vehicle, the method comprising: obtaining a plurality of event data an image, wherein the event data is associated with a pixel associated with a change in light intensity; determining a reference signal frequency associated with a transmitted light; identifying a select event data from the plurality of event data, wherein the light frequency associated with the select event data is substantially the same as the reference signal frequency; determining an object based on the select event data, wherein the object is fully enclosed by a bounding box comprising coordinates of a rectangular border; calculating a distance between a set of coordinates of the bounding box closest to the autonomous vehicle and the autonomous vehicle; and calculating the time to contact between the set of coordinates of the bounding box and the autonomous vehicle.

Methods and devices for detecting objects and calculating a time to contact in autonomous driving systems

Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Robust object detection and classification using static-based cameras and events-based cameras

An apparatus comprising a memory to store an observed trajectory of a pedestrian, the observed trajectory comprising a plurality of observed locations of the pedestrian over a first plurality of timesteps; and a processor to generate a predicted trajectory of the pedestrian, the predicted trajectory comprising a plurality of predicted locations of the pedestrian over the first plurality of timesteps and over a second plurality of timesteps occurring after the first plurality of timesteps; determine a likelihood of the predicted trajectory based on a comparison of the plurality of predicted locations of the pedestrian over the first plurality of timesteps and the plurality of observed locations of the pedestrian over the first plurality of timesteps; and responsive to the determined likelihood of the predicted trajectory, provide information associated with the predicted trajectory to a vehicle to warn the vehicle of a potential collision with the pedestrian.

Potential collision warning system based on road user intent prediction

Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.

Methods and apparatus to facilitate autonomous navigation of robotic devices

Systems and techniques for an architecture for contextual memories in map representation for 3D reconstruction and navigation are described herein. In an example, a system for contextual memory mapping is adapted to receive a data set of physical world sensor readings. The system may be further adapted to generate voxel data from the data set, the voxel data includes voxel coordinates and a physical world occupancy indicator. The system may be further adapted to select a block of addresses in the memory to store the voxel data. The system may be further adapted to generate a hash map to map voxel coordinates to memory locations in the block of addresses, the voxel coordinates having a contextual relationship that is maintained by the hash map. The system may be further adapted to store the voxel data at memory addresses based on the hash map.

Architecture for contextual memories in map representation for 3D reconstruction and navigation

An apparatus of an autonomous device comprises one or more state estimators to estimate one or more states of the autonomous device, wherein the one or more state estimators are to generate one or more derivatives of translational measurements, orientation measurements, reference translational values, and reference orientation values, and one or more controllers to receive an output from the one or more state estimators to provide control signals to control the autonomous device. The one or more state estimators include a hardware differentiator to generate the one or more derivatives.

Hardware accelerator for online estimation

Techniques are disclosed to facilitate cooperative mapping for safe and efficient trajectory planning and collision avoidance by allowing nearby agents to share contextual information. The described techniques also function to extend the mapping range of a single agent by leveraging observations made by multiple agents. Furthermore, the techniques as described herein function to reduce uncertainty in trajectory planning by allowing agents to 'see' behind occlusions, thus taking advantage of observations made by neighboring agents from different points of view. An efficient hardware implementation of the system is also presented that leverages the methodologies as discussed herein.

Cooperative mapping for autonomous vehicles, robots or multi-agent systems

Methods and apparatus for preventing collisions between drones based on drone-to-drone acoustic communications are disclosed. A control system of a first drone includes a route manager to command the first drone to track a first route. The control system further includes a plurality of acoustic sensors arranged on the first drone to detect acoustic signals. The control system further includes a collision prevention engine to determine a second route based on acoustic signals detected by the acoustic sensors, the acoustic signals being received from a second drone while the first drone is tracking the first route. The second route differs from the first route to prevent a drone-to-drone collision between the first drone and the second drone. The collision prevention engine is to cause the route manager to command the first drone to track the second route instead of the first route.

Methods and apparatus for preventing collisions between drones based on drone-to-drone acoustic communications

System and techniques for drone obstacle avoidance using real-time wind estimation are described herein. A wind metric is measures at a first drone and communicated to a second drone. In response to receiving the wind metric, a flight plan of the second drone is modified based on the wind metric.

Drone obstacle avoidance using real-time wind estimation

Various systems and methods for drone path planning are described herein. A system for drone path planning for a drone, the system to perform the operations: storing a current location of the drone as a current waypoint in a set of waypoints, the set of waypoints to include the current waypoint and a previous waypoint; detecting a current set of frontiers from the current location; storing the current set of frontiers in a list of active goals, the current set of frontiers associated with the current location; determining whether a target is discovered in the current set of frontiers; exploring the current set of frontiers to attempt to find the target; and exploring a previous set of frontiers from the list of active goals, when exploring the current set of frontiers does not, discover the target, the previous set of frontiers associated with the previous waypoint.

Drone path planning

In one embodiment, a distributed oscillator computes a first error value based on a first state value for the distributed oscillator and first state values for other distributed oscillators, and computes a second error value based on a second state value for the distributed oscillator and second state values for the other distributed oscillators. The distributed oscillator computes a new first state value for the distributed oscillator based on the first error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator, and computes a new second state value for the distributed oscillator based on the second error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator. The distributed oscillator transmits the new first state value and the new second state value to the other distributed oscillators.

Decentralized synchronization of multiple agents

Methods and apparatus for drone collision avoidance. Processing circuitry of a drone extracts information from an encoded image captured by a detection device with a field view overlapping the encoded image. Based on the extracted information a determination is made whether a collision will occur on the flight trajectory of the drone with an external source. The flight trajectory of the drone is then altered to avoid a collision.

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Jalisco, Mexico

Leobardo E Campos Macias