Autodesk, Inc.

Hyunmin Cheong

Francesco Iorio

Wei Li

Adrian Butscher

Mehran Ebrahimi

George William Fitzmaurice

Tovi Samuel Grossman

Rubaiat Habib Kazi

Ali Baradaran Hashemi

Claude-Guy Quimper

Vincent Goulet

A computer-implemented method for controlling a robot, the method comprising: determining a first value for a first joint parameter associated with a first continuum joint included in the robot and a first value for a second joint parameter associated with the first continuum joint, wherein the first joint parameter indicates a bending radius of a flexible portion of the continuum joint, and the second joint parameter indicates a rotation of the flexible portion of the continuum joint with respect to a base portion of the first continuum joint; and positioning an end portion of the robot at a final target location based on the first value of the first joint parameter and the first value of the second joint parameter.

Singularity-free kinematic parameterization of soft robot manipulators

A design engine automates portions of a mechanical assembly design process. The design engine generates a user interface that exposes tools for capturing input data related to the design problem. Based on the input data, the design engine performs various operations to generate a formalized problem definition that can be processed by a goal-driven optimization algorithm. The goal-driven optimization algorithm generates a spectrum of potential design options. Each design option describes a mechanical assembly representing a potential solution to the design problem.

Techniques for applying generative design to the configuration of mechanical assemblies

A design engine systematically explores a design space associated with a design problem related to mechanical assemblies. The design engine implements a constraint programming approach to produce mechanical assembly configurations that adhere to a set of design constraints. For each feasible configuration, the design engine then optimizes various parameters to generate design options that meet a set of design objectives. With these techniques, the design space can be explored very quickly to generate significantly more feasible design options for the mechanical assembly than possible with conventional manual approaches. Accordingly, numerous design options can be generated that may otherwise never be produced using those conventional approaches.

Constraint-oriented programming approach to mechanical assembly design

A design engine implements a probabilistic approach to generating designs that exposes automatically-generated design knowledge to the user during operation. The design engine interactively generates successive populations of designs based on a problem definition associated with a design problem and/or a previously-generated population of designs. During the above design process, the design engine generates a design knowledge graphical user interface (GUI) that graphically exposes various types of design knowledge to the user. In particular, the design engine generates a design variable dependency GUI that visualizes various dependencies between designs variables. The design engine also generates a design evolution GUI that animates the evolution of designs across the successive design populations. Additionally, the design engine generates a design exploration GUI that facilitates the user exploring various statistical properties of automatically-generated designs.

Techniques for visualizing probabilistic data generated when designing mechanical assemblies

A design engine generates a configuration option that includes a specific arrangement of interconnected mechanical elements adhering to one or more design constraints. Each element within a given configuration option is defined by a set of design variables. The design engine implements a parametric optimizer to optimize the set of design variables associated with each configuration option. For a given configuration option, the parametric optimizer discretizes continuous equations governing the physical dynamics of the configuration. The parametric optimizer then determines the gradient of an objective function based on the discretized equations the gradient of objective and constraint functions based on discrete direct differentiation method or discrete adjoint variable method derived directly from the discretized motion equations. Then, the parametric optimizer traverses a design space where the configuration option resides to reduce improve the objective function, thereby optimizing the design variables.

Efficient sensitivity analysis for generative parametric design of dynamic mechanical assemblies

A design application is configured to perform a system-level optimization of a collection of system components. The design application iteratively executes a multi-objective solver to optimize structural and functional relationships between the system components in order to meet global design criteria and generate a system design. The design application initializes the design process by extracting from a knowledge base system templates having taxonomic, structural, or functional attributes relevant to the system design. The design application generates the knowledge base by mining taxonomic, structural, and functional relationships from a corpus of engineering texts.

System-level approach to goal-driven design

A design engine implements a probabilistic approach to generating designs for computer-aided design (CAD) assemblies. The design engine initially generates a population of designs based on a problem definition associated with a design problem. Each design includes a randomly-generated set of design values assigned to various design variables. The design engine repairs any infeasible designs in the population of designs and then executes a dynamic simulation with the population of designs. The design engine selects the most performant designs and identifies, based on those performant designs, design variables that are dependent on one another. The design engine generates a probability model indicating conditional probabilities between design values associated with dependent design variables. The design engine then iteratively samples the probability model to generate a subsequent population of designs. In this manner, the design engine can automatically generate designs for mechanical assemblies significantly faster than possible with conventional algorithmic design techniques.

Automatic design of mechanical assemblies using estimation of distribution algorithm

Embodiments of the invention disclosed herein provide techniques for generating an alternative design recommendation. The techniques include determining, via a processor, a first function associated with one or more first components included in a first design. The techniques further include analyzing, via the processor, a plurality of components included in a database to select one or more second components associated with a second function that corresponds to the first function. The techniques further include causing an alternative design recommendation that includes the one or more second components to be output for display.

Knowledge-based alternative design recommendations

Embodiments of the invention disclosed herein provide techniques for generating a design autocomplete recommendation. The techniques include determining one or more first components included in a first design. The techniques further include analyzing, via the processor, a plurality of designs included in a database to select a second design that includes one or more second components that correspond to the one or more first components. The techniques further include selecting, via a processor, one or more third components included in the second design that are not included in the first design. The techniques further include causing an autocomplete recommendation that includes the one or more third components to be output.

Knowledge-based design autocomplete recommendations

A computing device for receiving a design problem statement describing a design problem in a controlled natural language (CNL) that defines permitted lexicons and syntax structures. The design problem statement is processed using the CNL lexicons and syntax structures to produce a job description executable by a design application for generating a design solution for the design problem statement. An improved CNL user interface that assists users to produce valid design problem statements that are CNL-compliant. The CNL user interface receives user-selectable terms that are compliant with the CNL lexicons and generates candidate problem statements that are compliant with CNL syntax structures and receives a selection of a candidate problem statement that is added to the design problem statement. A graphical user interface may display a graphical representation of a design problem statement that can be directly modified. A dialogue-based design process to explore possible design intentions and design solutions.

Techniques for using controlled natural language to capture design intent for computer-aided design

One embodiment of the present invention provides a technique for generating a three-dimensional model from a two-dimensional sketch. The technique includes receiving input indicating a set of points defining a first sketch element and a second set of points defining a second sketch element included in a sketch. The technique further includes identifying one or more design relationships between the first sketch element and the second sketch element. The technique further includes generating a computer model of the sketch that represents a structure linking the first sketch element and the second sketch element according to the one or more design relationships. The technique further includes outputting the first sketch element, the second sketch element, and the structure for display.

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Toronto, Canada

Hyunmin Cheong