Abstract: An embodiment receives models of an object and an environment and an indication of position of a digital human model (DHM). An oriented bounding box (with a plurality of faces) surrounding the model of the object is determined and, for each of the plurality of faces, a candidate grasp location, a candidate grasp orientation, and a candidate grasp type is determined. From amongst the plurality of faces, one or more graspable faces is determined based on: the candidate grasp locations, the candidate grasp orientations, the environment model, and dimensions of each face. Then, an optimal graspable face is identified based on a hierarchy and the position of the DHM. An inverse kinematic solver determines position and orientation, i.e., grasp, of an end effector of the DHM grasping the object based on the candidate grasp location, candidate grasp orientation, and candidate grasp type of the optimal graspable face.

Abstract: Embodiments assess ergonomic risk of causing harm to a worker in a workplace. One such embodiment receives an indication of posture risk level for each of a plurality of digital human models performing a task. In turn, a weighted average of the received indications of posture risk level is determined. This determined weighted average is indicative of ergonomic risk to a real-world worker performing the task in a workplace. Embodiments consider consecutive risk through modifications to weights used in the determining the weighted average.

Abstract: Embodiments assess ergonomic risk in environments, such as factories and workstations. One such embodiment begins by receiving process planning data for an operator performing a task. In turn, the received process planning data is used to generate a posture for the operator to perform the task in a certain real-world environment. The generated posture is processed using a hierarchical decision tree to determine ergonomic risk of the posture in the certain real-world environment. Output includes an indication of the determined ergonomic risk.

Abstract: 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.

Abstract: 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.