Dassault Systemes Americas Corp.

Pierre-Olivier Lemieux

Rachid Aissaoui

Nicola Hagemeister

Arnaud Barré

Ali Zeighami

Louis Rivest

Alexandre Macloud

Julie Charland

Matthieu Cauffiez

Quentin Bourret

A computer-implemented method and system determine an initial or starting position of a manikin for use in simulation. The method automatically analyzes environment data to determine a highest ranking type of data from among the environment data. In response, a guiding vector and a sweep mode are determined based upon the determined highest ranking type of data. The determined guiding vector and sweep mode are used to automatically analyze free space between a manikin and a target object in a simulated real-world environment to determine an initial position for and pre-position of the manikin in a simulation of the real-world environment.

Environment-aware prepositioning of digital models in an environment

Grasping remains a complex topic for simulation. Embodiments provide a method to automatically determine grasping cues for tools. An example embodiment scans a CAD model representing a real-world tool to generate a series of sections from the CAD model. In turn, properties of each section are extracted and one or more regions of the CAD model are identified based upon the extracted properties and a tool family to which the tool represented by the CAD model belongs. To continue, a respective classification for each of the one or more identified regions is identified and grasping cues for the CAD model are generated based upon the determined respective classification for each of the one or more regions.

Extracting grasping cues from tool geometry for digital human models

Embodiments determine positioning of a mannequin. One such embodiment begins by determining a frame of a grasping element of a mannequin represented by a computer-aided design (CAD) model and determining a frame of an object to be grasped, where is object is also represented by a CAD model. To continue, degrees of freedom of the mannequin are specified and limits on the specified degrees of freedom are set. In turn, using an inverse kinematic solver, positioning of the mannequin grasping the object is determined based upon: (i) the determined frame of the grasping element, (ii) the determined frame of the object, (iii) the specified degrees of freedom, and (iv) the set limits on the specified degrees of freedom.

Systems and methods for determining digital model positioning for grasping

The present disclosure is directed to a method and corresponding system that improves accuracy of a computer simulation of an original posture of a digital human model (DHM) relative to a target object. The method and system may obtain information associated with the original DHM posture. The obtained DHM posture information may include a position of a head of the DHM. The method and system may obtain information associated with the target object. The obtained target object information may include a size of the target object and an orientation of the target object. The method and system method may obtain a distance from the head of the DHM to the target object. In some embodiments, the system and method may generate a measure of vision (i.e., vision measure) of the DHM of the target object that the DHM is visually targeting. The system and method may generate the measure of vision based on one or more parameters which may include any of the obtained DHM posture information, the obtained target object information, and the obtained head-target (HT) distance. Based on the measure of vision, the system and method may generate a constraint of vision (i.e., vision constraint) of the DHM to the target object. Based on the vision constraint, the system and method may generate an updated DHM posture.

Method and system for vision measure for digital human models

An upper limb model of a virtual manikin includes a data conversion engine configured to produce converted data based on one or more data sets. Each data set represents dependencies between elements of the kinematic model. The upper limb model further includes a kinematic chain model configured to generate one or more constraints based on the converted data. The upper limb model also includes a posturing engine configured to determine, based on the one or more constraints, a trajectory from a first position to a second position. The kinematic model may further include a rendering engine configured to render a posture corresponding to the second posture. The elements of the kinematic model may include one or more of a clavicle, a scapula, a humerus, a forearm and a hand.

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Montreal, Canada

Pierre-Olivier Lemieux