Autodesk, Inc.

Nicholas Cote

Evan Patrick Atherton

Heather Kerrick

David Thomasson

Maurice Ugo Conti

Hui Li

Erin Marie Bradner

A robot system is configured to fabricate three-dimensional (3D) objects using closed-loop, computer vision-based control. The robot system initiates fabrication based on a set of fabrication paths along which material is to be deposited. During deposition of material, the robot system captures video data and processes that data to determine the specific locations where the material is deposited. Based on these locations, the robot system adjusts future deposition locations to compensate for deviations from the fabrication paths. Additionally, because the robot system includes a 6-axis robotic arm, the robot system can deposit material at any locations, along any pathway, or across any surface. Accordingly, the robot system is capable of fabricating a 3D object with multiple non-parallel, non-horizontal, and/or non-planar layers.

Closed-loop robotic deposition of material

An agent engine allocates a collection of agents to scan the surface of an object model. Each agent operates autonomously and implements particular behaviors based on the actions of nearby agents. Accordingly, the collection of agents exhibits swarm-like behavior. Over a sequence of time steps, the agents traverse the surface of the object model. Each agent acts to avoid other agents, thereby maintaining a relatively consistent distribution of agents across the surface of the object model over all time steps. At a given time step, the agent engine generates a slice through the object model that intersects each agent in a group of agents. The slice associated with a given time step represents a set of locations where material should be deposited to fabricate a 3D object. Based on a set of such slices, a robot engine causes a robot to fabricate the 3D object.

Agent-based slicing for 3D object models

A robotic assembly cell is configured to generate a physical mesh of physical polygons based on a simulated mesh of simulated triangles. A control application configured to operate the assembly cell selects a simulated polygon in the simulated mesh and then causes a positioning robot in the cell to obtain a physical polygon that is similar to the simulated polygon. The positioning robot positions the polygon on the physical mesh, and a welding robot in the cell then welds the polygon to the mesh. The control application captures data that reflects how the physical polygon is actually positioned on the physical mesh, and then updates the simulated mesh to be geometrically consistent with the physical mesh. In doing so, the control application may execute a multi-objective solver to generate an updated simulated mesh that meets specific design criteria.

Robotic assembly of a mesh surface

An assembly engine is configured to generate, based on a computer-aided design (CAD) assembly, a set of motion commands that causes the robot to manufacture a physical assembly corresponding to the CAD assembly. The assembly engine analyzes the CAD assembly to determine an assembly sequence for various physical components to be included in the physical assembly. The assembly sequence indicates the order in which each physical component should be incorporated into the physical assembly and how those physical components should be physically coupled together. The assembly engine further analyzes the CAD assembly to determine different component paths that each physical component should follow when being incorporated into the physical assembly. Based on the assembly sequence and the component paths, the assembly engine generates a set of motion commands that the robot executes to assemble the physical components into the physical assembly.

Techniques for CAD-informed robotic assembly

One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

Adapting simulation data to real-world conditions encountered by physical processes

One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.

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San Francisco, CA, United States of America

Nicholas Cote