Fast planning of precision grasps for 3D objects

In the near future, more and more robots will be used for servicing tasks, tasks in hazardous environments or space applications. Dextrous hands are a powerful and flexible tool to interact with these real world environments that are not specially tailored for robots. In order to grasp and manipulate real world objects, grasp planning systems are required. Grasp planning for general 3D objects is quite a complex problem requiring a large amount of computing time. Fast algorithms are required to integrate grasp planners in online planning systems for robots. This paper presents an heuristic approach towards fast planning of precision grasps for realistic, arbitrarily shaped 3D objects. In this approach a number of feasible grasp candidates are generated heuristically. These grasp candidates are qualified using an efficiently computable grasp quality measure and the best candidate is chosen. It is shown that only a relatively small number of grasp candidates has to be generated in order to obtain a good-although not optimal-grasp