Parallel methods for synthesizing whole-hand grasps from generalized prototypes

Robotic hands are very exible mechanisms. Because of this exibility, it has been di cult to automate the process of acquiring objects using these robotic hands. Each new grasping problem is too complex to analyze without the use of good heuristics. Constraints due to target object geometry, environment geometry, hand kinematics and geometry, and a task description must all be considered when forming a solution. One tempting approach to this problem is to contain the exibility of the robot hand by developing standard routines for grasping common objects. Standard grasping routines greatly reduce the complexity of the grasp synthesis problem, but these routines tend to go too far, becoming in exible to variation in the target object and environment geometries. If the range of application of any given grasping routine is small, a reasonably sized library of routines will not cover an acceptable space of problem situations. This report describes one way in which standard grasping routines can be made more e ective. A technique is developed for generalizing standard grasps and applying a generalized grasp description to new problem situations. The grasp generalization technique expands the range of application of a standard grasp to a wide variety of target object and environment geometries, while ensuring that the resulting grasps are appropriate for the intended task. The ability to generalize a standard grasp in this way greatly reduces the number of standard grasping routines that must be stored in any comprehensive grasp library. A generalized grasp description is applied to a new problem situation using a parallel search through hand con guration space. The generalized grasp description allows the placement of each contact of a grasp to be independently optimized. This means that the process of assembling good hand con gurations can be transformed into a dynamic programming algorithm. The many-dimensional space of hand con gurations ( fteen dimensions in the examples of the report) is searched for optimal solutions by performing a small number of steps in a six-dimensional workspace. The result of applying a generalized grasp description to a new problem situation is a space of wrist con gurations from which high quality, collision-free grasps can be achieved. It is useful to have such a global solution, because this solution can be used to estimate the robustness of a grasp to errors in wrist placement, to measure the e ect of obstacles on the size and shape of the space of good grasps, and to indicate whether any good grasps are possible at all. A global solution also provides a exible starting point for a post-processing step that adds the constraint of robot arm kinematics to the problem and nds an approach path into one of the high quality, collision-free grasps that have been identi ed. The techniques presented in this report have been implemented. Many simulated examples are shown throughout the report, and the results are veri ed using the Salisbury threenger robotic hand. c Massachusetts Institute of Technology 1994