Grasping is a useful ability that allows manipulators to constrain objects to a desired location or trajectory. Whole-arm grasping is a specific method of grasping an object that uses the entire surface of the manipulator to apply contact forces. Elephant trunks and snakes and octopus arms are illustrative of these methods. One of the greatest challenges of whole-arm grasping in poorly defined environments is accurately identifying the perimeter of an object. Existing algorithms for this task use restrictive assumptions or place unrealistic demands on the required hardware. Here, a new algorithm (termed Octograsp) has been developed as a method of gaining information on the shape of the grasped object through tactile information alone. The contact information is processed using an inverse convex hull algorithm to build a model of the object’s shape and position. The performance of the algorithm is examined using both simulated and experimental hardware. Methods of increasing the level of contact information through repeated contact attempts are presented. It is demonstrated that experimentally obtained, coarsely spaced, contact information can result in an accurate model of an object’s shape and position. 1. Introduction To grasp an object is to seize or hold it, thereby constraining it to a desired position or trajectory. Industrial robots implement finger-tip grasping (based around biological inspiration from humans) to perform tasks on objects that are normally well defined, often rigid, and always considerably smaller than the robotic manipulator. Robotic systems are increasingly being considered for new application areas where the operating requirements are more complex and less well defined; consider the use of robotic end-effectors for the assembly of complex machines with components of varying size and consistency. For applications such as these, an alternative approach to robotic grasping is required. Whole-arm grasping is an alternative to finger-tip grasping [1, 2] that is used by animals such as elephants, snakes and octopuses. Whole-arm grasping uses the entire surface of the manipulator to provide contacts between the object and manipulator. This allows the distribution of grasp forces and an increased surface area to aid the grasping of smooth objects. For example, consider the scenario of Figure 1 where a 10-link multiple section robot arm has encircled an object and is applying grasping forces. Each link has the capability of exerting forces at various points along the objects body. The grasping control aim is to exert the minimum
References
[1]
J. R. Napier, “The prehensile movements of the human hand,” The Journal of Bone & Joint Surgery—British Volume, vol. 38, no. 4, pp. 902–913, 1956.
[2]
K. Salisbury, “Whole arm manipulation,” in Proceedings of the 4th Symposium on Robotics Research, pp. 183–189, 1988.
[3]
D. Kirkpatrick, B. Mishra, and C.-K. Yap, “Quantitative Steinitz's theorems with applications to multifingered grasping,” Discrete & Computational Geometry, vol. 7, no. 1, pp. 295–318, 1992.
[4]
F. Asano, Z.-W. Luo, M. Yamakita, and S. Hosoe, “Dynamic modeling and control for whole body manipulation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '03), vol. 4, pp. 3162–3167, October 2003.
[5]
M. Shahinpoor, “Ionic polymer-conductor composites as biomimetic sensors, robotic actuators and artificial muscles—a review,” Electrochimica Acta, vol. 48, no. 14–16, pp. 2343–2353, 2003.
[6]
E. Biddiss and T. Chau, “Electroactive polymeric sensors in hand prostheses: bending response of an ionic polymer metal composite,” Medical Engineering & Physics, vol. 28, no. 6, pp. 568–578, 2006.
[7]
C. Busch, Whole-arm grasping with hyper-redundant planar manipulators using neural networks [Ph.D. dissertation], Vorarlberg University of Applied Sciences, 2002.
[8]
W. McMahan, B. Jones, I. Walker, V. Chitrakaran, A. Seshadri, and D. Dawson, “Robotic manipulators inspired by cephalopod limbs,” in Proceedings of the CDEN Design Conference, Montreal, Canada, July 2004.
[9]
I. D. Walker, D. M. Dawson, T. Flash et al., “Continuum robot arms inspired by cephalopods,” in Unmanned Ground Vehicle Technology VII, vol. 5804 of Proceedings of SPIE, pp. 303–314, March 2005.
[10]
R. Desai, C. Rosenberg, and J. Jones, “Kaa: an autonomous serpentine robot utilizes behavior control,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems: Human Robot Interaction and Cooperative Robots, vol. 3, pp. 250–255, August 1995.
[11]
D. Reznik and V. Lumelsky, “Multi-finger “hugging”: a robust approach to sensor-based grasp planning,” in Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 754–759, May 1994.
[12]
Y. Y. Yekutieli, S. G. German, F. T. Tamar, and H. B. Binyamin, “How to move with no rigid skeleton? The octopus has the answers,” Biologist, vol. 49, no. 6, pp. 250–254, 2002.
[13]
D. Devereux, P. Nutter, and R. Richardson, “Reactive and pre-planned encirclement for whole arm grasping,” in Proceedings of the 7th IEEE International Conference on Industrial Informatics (INDIN '09), pp. 668–673, June 2009.
[14]
P. J. McKerrow, Introduction To Robotics, Addison Wesley, 4th edition, 1998.
[15]
F. D. Snyder, D. D. Morris, P. H. Haley, R. T. Collins, and A. M. Okerholm, “Autonomous river navigation,” in Mobile Robots XVII, vol. 5609 of Proceedings of SPIE, pp. 221–232, October 2004.
[16]
P. Koprowski, “areaintersection.m,” http://www.mathworks.cn/matlabcentral/fileexchange/15557-areaintersection-m, 2007.
[17]
D. Devereux, Control strategies for whole arm grasping [Ph.D. thesis], The University of Manchester, Manchester, UK, 2010.
[18]
N. Hogan, “Impedance control: an approach to manipulation,” in Proceedings of IEEE American Control Conference, pp. 304–313, 1984.
[19]
J. K. Salisbury and B. Roth, “Kinematic and force analysis of articulated hands,” ASME Journal of Mechanisms, Transmissions, and Automation in Design, vol. 105, no. 1, pp. 33–41, 1982.
