A fast and robust grasp planner for arbitrary 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 real world environments that are not specially tailored for robots. In order to grasp and manipulate real world objects, grasp planning systems are required. To be integrated in online planning systems for robots, they have to be very fast. This paper presents a method to compute a desirable grasp quality measure very fast and accurate - both aims haven't been reached simultaneously until now. Based on this measure an heuristic approach towards fast planning of precision grasps for arbitrarily shaped 3D objects is described. A number of feasible grasp candidates are generated heuristically. These grasp candidates are qualified using the described grasp quality measure and the best candidate is chosen. The planned grasps are robust in respect of grasp placement. 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.