Control Strategies for Whole Arm Grasping

Access to files

•   FULL-TEXT.PDF (pdf)

Abstract

Grasping is a useful ability that allows manipulators to restrain objects to a desired location or trajectory. Whole arm grasps are grasps that use the entire surface of the manipulator to apply contacts to an object. The problem of determining the shape of an object and planning a grasp for that object with a snake-like robot are considered in this work. Existing algorithms that attempt to allow robots to plan and perform whole arm grasps are lacking, they either use restrictive assumptions or have unrealistic demands in terms of required hardware. The work presented here allows even the most basic of robots to plan grasps on unknown objects whilst using a minimum of assumptions.The new developed Octograsp algorithm is a method of gaining information regarding the shape of the object to be grasped through tactile information alone. This 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 algorithms are examined using both simulations and experimental hardware, it is shown that accuracy errors as low as 3.1% can be obtained. The accuracy of the model depends upon factors such as the complexity of the object and the suitability of the robot. Manipulators consisting of a large number of small links with relaxed rotational constraints outperform other configurations. It is also shown that the accuracy can be improved by between 11% and 17% by contacting the object from multiple orientations, whilst also encircling from multiple positions can provide a very large improvement of between 56% to 86%. These methods allow even the coarse contact information provided by the experimental equipment to attain a model with an accuracy error of only 26%.A second novel algorithm is described that uses the information provided from the first algorithm to plan strong grasps over the desired object. The algorithm takes, on average, 25.1 seconds to plan the grasp. The mean strength of the planned grasps is 0.3816 using the wrench ball measure, this is firmly in the very good region. Several robotic configurations, as well as objects, are used to test the performance of the algorithm. The optimal parameters of the algorithm are investigated by using the results of 51030 different tests. It is again shown that robots that consist of a large number of small links and with high rotational ability perform the best.

Keyword(s)

Control; Grasp planning; Grasping; Object Identication; Whole arm grasping

Bibliographic metadata

Institutional metadata

Record metadata