Doctor of Philosophy (Ph.D.)
Degree Granting Department
Computer Science and Engineering
Yu Sun, Ph.D.
Lawrence O. Hall, Ph.D.
Luther Palmer III, Ph.D.
Rajiv V. Dubey, Ph.D.
Gangaram S. Ladde, Ph.D.
Disturbance Wrench, Grasp Optimization, Grasp Quality Measure, Manipulative Motion, Task Modeling
Grasp should be selected intelligently to fulfill different stability properties and manipulative requirements. Currently, most grasping approaches consider only pick-and-place tasks without any physical interaction with other objects or the environment, which are common in an industry setting with limited uncertainty. When robots move to our daily-living environment and perform a broad range of tasks in an unstructured environment, all sorts of physical interactions will occur, which will result in random physical interactive wrenches: forces and torques on the tool.
In addition, for a tool to perform a required task, certain motions need to occur. We call it "functional tool motion," which represents the innate function of the tool and the nature of the task. Grasping with a robotic hand gives flexibility in "mounting" the tool onto the robotic arm - a different grasp will connect the tool to the robotic arm with a different hand posture, then the inverse kinematics approach will result in a different joint motion of the arm in order to achieve the same functional tool motion. Thus, the grasp and the functional tool motion decide the manipulator's motion, as well as the effort to achieve the motion.
Therefore, we propose to establish two objectives to serve the purpose of a grasp: the grasp should maintain a firm grip and withstand interactive wrenches on the tool during the task; and the grasp should enable the manipulator to carry out the task most efficiently with little motion effort, and then search for a grasp to optimize both objectives. For this purpose, two grasp criteria are presented to evaluate the grasp: the task wrench coverage criterion and the task motion effort criterion. The two grasp criteria are used as objective functions to search for the optimal grasp for grasp planning.
To reduce the computational complexity of the search in high-dimensional robotic hand configuration space, we propose a novel grasp synthesis approach that integrates two human grasp strategies - grasp type, and thumb placement (position and direction) - into grasp planning. The grasping strategies abstracted from humans should meet two important criteria: they should reflect the demonstrator's intention, and they should be general enough to be used by various robotic hand models. Different abstractions of human grasp constrain the grasp synthesis and narrow down the solutions of grasp generation to different levels. If a strict constraint is imposed, such as defining all contact points of the fingers on the object, the strategy loses flexibility and becomes rarely achievable for a robotic hand with a different kinematic model. Thus, the choice of grasp strategies should balance the learned constraints and required flexibility to accommodate the difference between a human hand and a robotic hand. The human strategies of grasp type and thumb placement have such a balance while conveying important human intents to the robotic grasping.
The proposed approach has been thoroughly evaluated both in simulation and on a real robotic system for multiple objects that would be encountered in daily living.
Scholar Commons Citation
Lin, Yun, "Task-based Robotic Grasp Planning" (2014). USF Tampa Graduate Theses and Dissertations.