Advertisement

Human Grasp Prediction and Analysis

  • Tim Marler
  • Ross Johnson
  • Faisal Goussous
  • Chris Murphy
  • Steve Beck
  • Karim Abdel-Malek
Chapter
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Abstract

Given that one of the critical motivations for using virtual humans is to simulate the interaction between humans and products, and given that using one’s hands are a primary means for interaction, then simulating human hands is arguably one of the most important elements of digital human modeling (DHM). Consequently, there is much research and development in this area, ranging from basic model development to detailed simulations of specific joints and tendons. However, when considering hand simulation and analysis within the context of a complete high-level DHM, the culmination of hand-related capabilities is grasping prediction. Thus, the focus of this chapter is on postural simulation and analysis capabilities of the overall hand as a component of a complete high-level DHM, with an eye toward grasping prediction. Within this context, the fundamental necessary elements one must consider when modeling the hand are highlighted. The intent is to provide general guidelines for creating computational models of hands and to present novel modeling and simulation techniques.

Keywords

Joint Angle Target Point Human Hand Revolute Joint Reach Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Goussous F, Marler T, Abdel-Malek K (2009) A new methodology for human grasp prediction. IEEE Trans Syst Man Cybern Part A Syst Humans 39(2):369–380CrossRefGoogle Scholar
  2. 2.
    Carenzi F, Gorce P, Burnod Y, Maier M (2005) Using generic neural networks in the control and prediction of grasp postures. The European symposium on artificial neural networks, BrugesGoogle Scholar
  3. 3.
    Sanso RM, Thalmann D (1994) A hand control and automatic grasping system for synthetic actors. Proceedings of EUROGRAPHICS’94, vol 13, pp C168–C177Google Scholar
  4. 4.
    Miller A, Knoop S, Christensen H, Allen P (2003) Automatic grasp planning using shape primitives. IEEE Int Conf Robot Autom, ICRA’03, vol 2, pp 1824–1829Google Scholar
  5. 5.
    Rijpkema H, Girard M (1991) Computer animation of knowledge-based grasping. Proc ACM SIGGRAPH’91 25(4):339–348Google Scholar
  6. 6.
    Tomovic R, Bekey G, Karplus W (1987) A strategy for grasp synthesis with multifingered robot hands. Proc IEEE Int Conf Robot Autom 4:83–89Google Scholar
  7. 7.
    Xue Z, Kasper A, Zoellner JM, Dillmann R (2009) An automatic grasp planning system for service robots. Proceedings of the ICAR 2009 14th international conference on advanced robotics, MunichGoogle Scholar
  8. 8.
    Gorce P, Rezzoug N (2005) Grasping posture learning with noisy sensing information for a large scale of multifingered robotic systems. J Robot Syst 12:711–724CrossRefGoogle Scholar
  9. 9.
    Jagannathan S, Galan G (2004) Adaptive critic neural network based object grasping control using a three finger gripper. IEEE Trans Neural Netw 15(2):395–407CrossRefGoogle Scholar
  10. 10.
    Moussa M (2004) Combining expert neural networks using reinforcement feedback for learning primitive grasping behavior. IEEE Trans Neural Netw 15(3):629–638MathSciNetCrossRefGoogle Scholar
  11. 11.
    Taha Z, Brown R, Wright D (1997) Modeling and simulation of the hand grasping using neural networks. Med Eng Phys 19(6):536–538CrossRefGoogle Scholar
  12. 12.
    Pelossof R, Miller A, Allen P, Jebara T (2004) An SVM learning approach to robotic grasping. Proc IEEE Int Conf Robot Autom 4:3512–3518Google Scholar
  13. 13.
    Ferrari C, Canny J (1992) Planning optimal grasps. Proceedings of the 1992 IEEE international conference on robotics and automation, NiceGoogle Scholar
  14. 14.
    Katada Y, Svinin M, Matsumura Y, Ohkura K, Ueda K (2001) Optimization of stable grasps by evolutionary programming. Proceedings of the 32nd international symposium on robotics, pp 1503–1508Google Scholar
  15. 15.
    Kim B, Yi B, Oh S, Suh I (2004) Non-dimensionalized performance indices based optimal grasping for multi-fingered robot hands. Mechatronics 14(3):255–280CrossRefGoogle Scholar
  16. 16.
    Li Y, Pollard N (2005) A shape matching algorithm for synthesizing humanlike enveloping grasps. IEEE-RAS international conference on humanoid robots (Humanoids 2005)Google Scholar
  17. 17.
    Liu G, Xu J, Wang X, Li Z (2004) On quality functions for grasp synthesis, fixture planning and coordinated manipulation. IEEE Trans Autom Sci Eng 1(2):146–162CrossRefGoogle Scholar
  18. 18.
    Borst C, Fischer M, Hirzinger G (1999) A fast and robust grasp planner for arbitrary 3-D objects. Proceedings of the IEEE international conference on robotics and automation, Detroit, MayGoogle Scholar
  19. 19.
    Hester, R., Cetin, M., Kapoor, C., and Tesar, D. (1999), “A criteria-based approach to grasp synthesis”, Proceedings of the 1999 IEEE International Conference on Robotics and Automation, Detroit, MichiganGoogle Scholar
  20. 20.
    Toth E (1999) Stable object grasping with dexterous hand in three dimensions. Periodica Polytechnica SER EL. ENG 43(3):207–214Google Scholar
  21. 21.
    Berenson D, Kuffner J, Choset H (2008) An optimization approach to planning for mobile manipulation. Proceedings of the IEEE international conference on robotics and automation, Pasadena, MayGoogle Scholar
  22. 22.
    Fernandez J, Walker I (1998) Biologically inspired robot grasping using genetic programming. Proceedings of the 1998 IEEE international conference on robotics and automation, LeuvenGoogle Scholar
  23. 23.
    Globisch R (2005) Automated grasping for articulated structures using evolutionary learning algorithms. Master’s thesis, University of Johannesburg, South Africa, April 2005Google Scholar
  24. 24.
    ElKoura G, Singh K (2003) Handrix: animating the human hand. In: ACM SIGGRAPH/Eurographics symposium on computer animation, 2003Google Scholar
  25. 25.
    Ehrenmann M, Rogalla O, Zollner R, Dillmann R (2001) Teaching service robots complex tasks: programming by demonstration for workshop and household environments. Proceedings of the 2001 international conference on field and service robots, vol 1, Helsinki, pp 397–402Google Scholar
  26. 26.
    Bohg J, Kragic D (2009) Learning grasping points with shape context. Robot Auton Syst 58(4):362–377CrossRefGoogle Scholar
  27. 27.
    Miyata N, Kouchi M, Mochimaru M (2006) Posture estimation for screening design alternatives by DhaibaHand—cell phone operation. Proceedings of the SAE 2006 digital human modeling for design and engineering conference, 2006-01-2327Google Scholar
  28. 28.
    Aleotti J, Caselli S (2006) Grasp recognition in virtual reality for robot pregrasp planning by demonstration. Proceedings of the 2006 IEEE international conference on robotics and automation, OrlandoGoogle Scholar
  29. 29.
    Abdel-Malek K, Arora J, Yang J, Marler T, Beck S, Kim J, Swan C, Frey-Law L, Kim J, Bhatt R, Mathai A, Murphy C, Rahmatalla S, Patrick A, Obusek J (2009) A physics-based digital human model. Int J Veh Des 51(3/4):324–340CrossRefGoogle Scholar
  30. 30.
    Marler T, Arora J, Beck S, Lu J, Mathai A, Patrick A, Swan C (2008) Computational approaches in DHM. In: Duffy VG (ed) Handbook of digital human modeling for human factors, ergonomics. Taylor and Francis Press, LondonGoogle Scholar
  31. 31.
    Yang J, Kim JH, Abdel-Malek K, Marler T, Beck S, Kopp GR (2007) A new digital human environment and assessment of vehicle interior design. Comput-Aided Des 39:548–558CrossRefGoogle Scholar
  32. 32.
    Denavit J, Hartenberg RS (1955) A kinematic notation for lower-pair mechanisms based on matrices. J Appl Mech 22:215–221MathSciNetMATHGoogle Scholar
  33. 33.
    Pena-Pitarch E, Yang J, Abdel-Malek K (2005) SANTOS™ Hand: a 25 degree-of-freedom model. SAE International, Iowa City, June 14–16, 2005-01-2727 DHMGoogle Scholar
  34. 34.
    Liu Q, Marler T, Yang J, Kim J, Harrison C (2009) Posture prediction with external loads—a pilot study. SAE Int J Passeng Cars Mech Syst 2(1):1014–1023Google Scholar
  35. 35.
    Marler RT, Arora JS, Yang J, Kim H–J, Abdel-Malek K (2009) Use of multi-objective optimization for digital human posture prediction. Eng Optim 41(10):295–943MathSciNetCrossRefGoogle Scholar
  36. 36.
    Marler T, Yang J, Rahmatalla S, Abdel-Malek K, Harrison C (2007) Validation methodology development for predicted posture. SAE digital human modeling conference, June, Seattle, Society of Automotive Engineers, WarrendaleGoogle Scholar
  37. 37.
    Gill P, Murray W, Saunders A (2002) SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM J Optim 12(4):979–1006MathSciNetMATHCrossRefGoogle Scholar
  38. 38.
    Rmstrong TJ, Chaffin DB (1978) An investigation of the relationship between displacements of the finger and wrist joints and the extrinsic finger flexor tendons. Biomechanics 11:119–128CrossRefGoogle Scholar
  39. 39.
    Johnson R, Smith BL, Penmatsa R, Marler T, Abdel-Malek K (2009) Real-time obstacle avoidance for posture prediction. SAE digital human modeling conference, June, Goteborg, Society of Automotive Engineers, WarrendaleGoogle Scholar
  40. 40.
    Johnson R, Fruehan C, Schikore M, Marler T, Abdel-Malek K (2010) New developments with collision avoidance for posture prediction. 3rd international conference on applied human factors and ergonomics, July, MiamiGoogle Scholar
  41. 41.
    Yang J, Verma U, Penmatsa R, Marler T, Beck S, Rahmatalla S, Abdel-Malek K, Harrison C (2008) Development of a zone differentiation tool for visualization of postural comfort. SAE 2008 World Congress, April, Detroit, Society of Automotive Engineers, WarrendaleGoogle Scholar
  42. 42.
    Yang J, Verma U, Marler T, Beck S, Rahmatalla S, Harrison C (2009) Workspace zone differentiation tool for visualization of seated postural comfort. Int J Ind Ergon 39:267–276CrossRefGoogle Scholar
  43. 43.
    Lorensen W, Cline H (1987) Marching cubes: a high resolution 3-D surface construction algorithm. Comput Graph (SIGRAPH 87 Proc) 21(4):163–170CrossRefGoogle Scholar
  44. 44.
    Napier J (1956) The prehensile movements of the human hand. J Bone Joint Surg 38B(4):902–913Google Scholar
  45. 45.
    Cutkosky MR (1989) On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans Robot Autom 5(3):269–279MathSciNetCrossRefGoogle Scholar
  46. 46.
    Feix T, Pawlik R, Schmiedmayer H, Romero J, Kragic D (2009) The generation of a comprehensive grasp taxonomy. In: Robotics, science and systems conference: workshop on understanding the human hand for advancing robotic manipulation, JuneGoogle Scholar
  47. 47.
    Osada R, Funkhouser T, Chazelle B, Dobkin D (2002) Shape distributions. ACM Trans Graph 21(4):807–832CrossRefGoogle Scholar
  48. 48.
    Chen C, Hung Y, Cheng J (1997) RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images. Technical Report, Institute of Information Science, Academia Sinica, TR-IIS-97-019Google Scholar
  49. 49.
    Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and cartography. Commun ACM 24(6):381–395MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Tim Marler
    • 1
  • Ross Johnson
    • 1
  • Faisal Goussous
    • 1
  • Chris Murphy
    • 1
  • Steve Beck
    • 1
  • Karim Abdel-Malek
    • 1
  1. 1.Center for Computer Aided DesignUniversity of IowaIowa CityUSA

Personalised recommendations