Table of Contents
ISRN Robotics
Volume 2013, Article ID 783083, 11 pages
Research Article

Biologically Inspired Perimeter Detection for Whole-Arm Grasping

1Department of Computer Science, University of Manchester, M13 9PL, UK
2School of Mechanical Engineering, University of Leeds, LS2 9JT, UK
3Institute for Simulation and Training, University of Central Florida, 32826, USA

Received 12 June 2013; Accepted 8 July 2013

Academic Editors: J. Archibald, A. Hamzaoui, and J.-S. Liu

Copyright © 2013 David Devereux et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.