Grasp Planning Of Unknown Object For Digital Human Model

Abstract: ObjectiveGrasp planning is a popular topic in the fields of robotic and Digital Human Model (DHM) (4, 6, 7, 9, 10, 11). So far, the proposed planners do not consider the final posture of the DHM has a criteria when determining potential grasps. In (4), a grasping algorithm has been developed to automatically grasp known tools. The present work introduces a grasp planner for single-hand grasp on an unknown object, further referred as “part”.MethodThe grasp planner gives has a result a grasp pose (position + orientation) for the posture solver (Smart Posturing Engine) to reach. The input necessary to the grasp planner are the 3D model of the object to grasp and of the surrounding environment, and an initial manikin position that is automatically determines by the posture solver algorithm.First the part is approximated by its oriented bounding box (OBB), limiting the grasp poses to 6 (one for each face of the OBB). Then precise grasp types (5) and apertures are chosen based on the face’s dimensions (i.e. width and depth), ranging from a small face (i.e. pinch) to larger ones (i.e. medium wrap or precision sphere).To determine what is the best face of the OBB to grasp, accessibility checks are performed by validating that the space around the face is free of collision. The faces are checked using a specific order (i.e. top, right or left, bottom, front, back) that is determined using the relative initial position of the manikin. As soon as a face is found to be graspable and accessible, the algorithm stops and choose that face as the best one to grasp.Using the selected face target, the hand is positioned using an inverse kinematic solver, free to rotate around the target using extra hand degrees-of-freedom inside a limited range (4). Giving the posture solver more possibilities to find a realistic posture.ResultsThe grasp planner described above leaded to believable grasps for the simulated tasks as well as a believable overall DHM posture. Examples of postures will be shown on assembly tasks performed on a gearbox assembly line.DiscussionThe proposed grasp planner seems really promising. In its current form, it is most suitable for small parts and bigger ones well represented by their OBB. More complex and bigger parts may require further segmentation into multiple smaller sub-parts (9, 10), allowing to perform the proposed checks at more specific and believable locations on the object. This would allow to obtain grasps on a wider range of objects. The object weight is also important and is currently being added to grasp type selection. The present planner is used by the Smart Posture Engine (SPE) framework (1, 2 and 3) inside Dassault Systèmes application “Ergonomic Workplace Design”. With the Ergo4All (12) technology the SPE allows to assess and minimize ergonomic risks involved in simulated workplaces.1. Lemieux, P.-O., Barré, A., Hagemeister, N., Aissaoui, R.: Degrees of freedom coupling adapted to the upper limb of a digital human model. Int. J. Hum. Factors Model. Simul. 5(4), 314–337 (2017)2. Lemieux, P., Cauffiez,M., Barré, A., Hagemeister, N., Aissaoui, R.: A visual acuity constraint for digital human modeling. In: 4th Conference proceedings (2016)3. Zeighami, A., Lemieux, P., Charland, J., Hagemeister, N., Aissaoui, A.: Stepping behavior for stability control of a digital human model. ISB/ASB (2019)4. Bourret, Q., Lemieux, P., Hagemeister, N., Aissaoui, R.: Flexible hand posture for tools grasping. DHM (2019)5. FEIX, Thomas, ROMERO, Javier, SCHMIEDMAYER, Heinz-Bodo, et al. The grasp taxonomy of human grasp types. IEEE Transactions on human-machine systems, 2015, vol. 46, no 1, p. 66-77.6. BEKEY, George A., LIU, Huan, TOMOVIC, Rajko, et al. Knowledge-based control of grasping in robot hands using heuristics from human motor skills. IEEE Transactions on Robotics and Automation, 1993, vol. 9, no 6, p. 709-722.7. Holleman, C.; Kavraki, L.E.; A framework for using the workspace medial axis in PRM planners, in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), IEEE, Vol 2, 2000, 1408-1413. https://doi.org/10.1109/ROBOT.2000.8447958. FEIX, Thomas, BULLOCK, Ian M., et DOLLAR, Aaron M. Analysis of human grasping behavior: Correlating tasks, objects and grasps. IEEE transactions on haptics, 2014, vol. 7, no 4, p. 430-4419. Díaz, C.; Puente, S.; Torres, F.; Grasping points for handle objects in a cooperative disassembly system, IFAC Proceedings Volumes, 40(2), 2007, 112-117. https://doi.org/10.3182/20070523-3-ES-4907.0002010. Miller, A.T., Knoop, S., Christensen, H.I. and Allen, P.K., Automatic grasp planning using shape primitives. in Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on, (2003), IEEE, 1824-1829.11. Goussous, Faisal Amer. Grasp planning for digital humans. The University of Iowa, 2007.12. Bourret, Quentin, et al. "Ergo4All: An Ergonomic Guid

Keywords: Digital Human Model, Grasp planner, Unknown object

DOI: 10.54941/ahfe1001908

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