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2008, 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Automatic grasp planning systems are very important for service robots, which compute what forces should be exerted onto the object and how those forces can be applied by robotic hands. In this paper, a highly integrated grasp planning system is introduced. Initial grasp is computed in the grasp simulator GraspIt! combining hand preshapes and automatically generated approach directions. With fixed relative position and orientation between the robotic hand and object as by the initial grasp, all the contact points between the fingers and the object are efficiently found. A search process tries to improve the grasp quality by moving the fingers to its neighbored joint positions, and uses the corresponding contact points to the joint position to evaluate the grasp quality, until local maximum grasp quality is reached. Optimal forces for the found grasp is computed as a linear inequalities matrix problem, which are exerted onto the object using torque based finger impedance control during execution. Experiments on Schunk Anthropomorphic Hand with 13 degrees of freedom show that, using the introduced grasp planning system, the object can be grasped solidly with shift errors of only some millimeters.
2009
Service robots need many different informations about the objects, to grasp and manipulate them. Besides the physical information such as the geometry and weight, semantic information of the objects is also needed. To model both of these informations, we have constructed a multimodal object modeling center. It can model the physical properties of the object, such as the textures and
Springer Tracts in Advanced Robotics, 2012
Grasping is a key function of service robots to help people in handling their household tasks. In order to grasp real world objects, automatic grasp planning systems are needed. In this article, a complete grasp planning system is introduced, which can plan feasible grasps and execute them with real robotic hands.
2008 SICE Annual Conference, 2008
Grasping and manipulation are the most important key functions for service robots to help people to handle the everyday tasks in household. After the object is grasped, the internal force between the hand and object is very important for fine manipulation. In this paper, we present the computation of internal force of grasped objects using joint torques. An automatic grasp planning system is used to generate grasps with high grasp quality for Schunk Anthropomorphic Hand with 13 degrees of freedom. Optimal grasp forces are computed as a linear matrix inequalities problem, and are exerted using embedded joint torque based finger impedance control. Computation of force at the fingertip, the internal force for grasps with multiple contact points and the external forces exerted onto the object after grasping are introduced. In case of multiple contact points, it is assumed that the external force is distributed equally among all contact points on one finger. Experimental results show the feasibility of our methods.
Nowadays, one important research area of robotics is the design of machines with capabilities to interact with humans or other robots. Service robots fall in this category of machines and they need a set of functions to accomplish their tasks. Future autonomous robots must be capable to work and take decisions in an automatic way. Such a system must be capable to handle most common objects in a dynamic human environment.
2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), 2012
This paper introduces a novel grasp planning algorithm which can find feasible grasps within a few milliseconds. The object surface is decomposed into plenar regions, which will be decomposed into smaller ones until a discrete grasp can be determined. We have extended the grasp wrench space formulation for the grasp regions and use ray-shooting method to evaluate the force-closure property of the grasp. Experiments in simulation show the efficiency of our algorithm.
2012
This work proposes an algorithm for designing a simple End Effector configuration for a robotic arm which is able to grasp a given set of objects. The algorithm searches for a common 3-finger grasp over a set of objects. The search algorithm maps all possible grasps for each object which satisfy a quality criterion and takes into account an external wrench (force and torque) applied to the object. The mapped grasps are represented by feature vectors in a high-dimensional space. This feature vector describes the shape of the gripper. We then generate a database of all possible grasps for each object represented as points in the feature vector space. Then we use another search algorithm for intersecting all points over the entire sets and finding common points suitable for all objects. Each point (feature vector) is the grasp configuration for a group of objects, which implies for the end-effector design. The final step classifies the grasps found to subsets of the objects, according to the common points found, this with preference to find one grasp to all the objects. The algorithm will be useful for assembly line robots in reducing end-effector design time, end-effector manufacturing time and final product cost.
Tdx, 2013
Several aspects have to be addressed before realizing the dream of a robotic handarm system with human-like capabilities, ranging from the consolidation of a proper mechatronic design, to the development of precise, lightweight sensors and actuators, to the efficient planning and control of the articular forces and motions required for interaction with the environment. This thesis provides solution algorithms for a main problem within the latter aspect, known as the grasp planning problem: Given a robotic system formed by a multifinger hand attached to an arm, and an object to be grasped, both with a known geometry and location in 3-space, determine how the hand-arm system should be moved without colliding with itself or with the environment, in order to firmly grasp the object in a suitable way. Central to our algorithms is the explicit enforcement of a pre-speceified set of handobject contact constraints in the grasp configuration, imposed by the particular manipulation task to be performed with the object. This is a distinguishing feature from other grasp planning algorithms given in the literature, where a means of ensuring precise hand-object contact locations in the resulting grasp is usually not provided. These conventional algorithms are fast, and nicely suited for planning grasps for pickan-place operations with the object, but not for planning grasps required for a specific manipulation of the object, like those necessary for holding a pen, a pair of scissors, or a jeweler's screwdriver, for instance, when writing, cutting a paper, or turning a screw, respectively. To be able to generate such highly-selective grasps, we assume that a number of surface regions on the hand are to be placed in contact with a number of corresponding regions on the object, and enforce the fulfilment of such constraints on the obtained solutions from the very beginning, in addition to the usual constraints of grasp restrainability, manipulability and collision avoidance. The proposed algorithms can be applied to robotic hands of arbitrary structure, possibly considering compliance in the joints and the contacts if desired, and they can accommodate general patch-patch contact constraints, instead of more restrictive contact types occasionally considered in the literature. It is worth noting, also, that while common force-closure or manipulability indices are used to assess the quality I would like to express my gratitude to my advisors Lluís Ros and Raúl Suárez for their invaluable guidance. Also, to Josep M. Porta and Jan Rosell for their unconditional support, and for leading the projects of such wonderful frameworks, the CUIK and Kautham suites, in which this thesis relies on. I would like to thank Federico Thomas who, together with Raúl, came with the idea of joining the worlds of grasping and kinematics that resulted in this PhD research. I am thankful to Antonio Bicchi and Marco Gabiccini for letting me accomplish a fruitful research stay in Pisa; Chapter 5 is a joint work with them. I'd like to highlight the work of Alexander Perez, a friend, a coauthor, and coder of a large part of the Kautham suite; Montserrat Manubens, Léonard Jaillet, Oriol Bohigas, friends and contributors to the CUIK suite; Patrick Grosch, a friend who provided some of the 3D models used here as well as great ideas; and Leopold Palomo, a friend who introduced me to the open source world and provided technical support. Thus, I would like to acknowledge the work of the open source and free software communities since most of the content of this document has been generated using freely-available tools, such as Ubuntu, Debian, LaTeX, Inkscape, GIMP, Geomview, Blender, among many others, freely-available 3D models, such as the SketchUp handarm model used in Figure 1.3 shared by Daniel Murray, and freely-available LaTex templates, such as the PhD thesis style used for this document shared by Adolfo Rodriguez, all of them free as in free beer, and some as in freedom. I would like to acknowledge the reviewing work of numerous researchers who have improved this work with their valuable comments on publications. I'd like to thank my relatives for their support. And last, but not least for sure, I am more than pleased to have met very nice friends and colleagues at the IOC and IRI institutes, as well as countless friends aside who collabarated in different ways and means to the development of this work.
2018 IEEE Conference on Decision and Control (CDC), 2018
For successful object manipulation with robotic hands, it is important to ensure that the object remains in the grasp at all times. In addition to grasp constraints associated with slipping and singular hand configurations, excessive rolling is an important grasp concern where the contact points roll off of the fingertip surface. Related literature focus only on a subset of grasp constraints, or assume grasp constraint satisfaction without providing guarantees of such a claim. In this paper, we propose a control approach that systematically handles all grasp constraints. The proposed controller ensures that the object does not slip, joints do not exceed joint angle constraints (e.g. reach singular configurations), and the contact points remain in the fingertip workspace. The proposed controller accepts a nominal manipulation control, and ensures the grasping constraints are satisfied to support the assumptions made in the literature. Simulation results validate the proposed approach.
IEEE Transactions Automation Science and Engineering, 2004
Planning a proper set of contact points on a given object/workpiece so as to satisfy a certain optimality criterion is a common problem in grasp synthesis for multifingered robotic hands and in fixture planning for manufacturing automation. In this paper, we formulate the grasp planning problem as optimization problems with respect to three grasp quality functions. The physical significance and properties of each quality function are explained, and computation of the corresponding gradient flows is provided. One noticeable property of some of these quality functions is that the optimal solutions are also forceclosure grasps if they do exist for the given object. Furthermore, when specialized to two-fingered or three-fingered grasps on a spherical object, the optimal solutions become the familiar antipodal grasp, or the symmetric grasp, respectively. Thus, by following the gradient flows with arbitrary initial conditions, the optimal grasp synthesis problem is solved for objects with smooth geometries manipulated by hands with any number of fingers. Also, note that our solutions do not involve linearization of the friction cones. We discuss two simplified versions of these problems when real-time solutions are needed, e.g., coordinated manipulation of a robotic hand with contact points servoing. We give simulation and experimental results illustrating validity of the proposed approach for optimal grasp planning.
Proceedings of the 12th Annual Conference on Computers and Industrial Engineering, 1990
Abstract: In this paper, a new Multiple Aspect Grasp (MAG) performance index is presented for evaluating grasp quality for object manipulation tasks. The position of contact points, the configuration of cooperative manipulators, and the kinetics aspects of manipulators and the manipulated object are taken into account by the proposed MAG index. The MAG index is used to evaluate the candidate grasp points, selecting the effective branch of inverse kinematics solution, and cooperation of two manipulators. Simulation results reveal capabilities of MAG index in grasp planning for both individual and cooperative object manipulation tasks.
2019
This paper develops model-based grasp planning algorithms for assembly tasks. It focuses on industrial endeffectors like grippers and suction cups, and plans grasp configurations considering CAD models of target objects. The developed algorithms are able to stably plan a large number of high-quality grasps, with high precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which pre-processes a mesh model by peeling it into facets. The algorithms use superimposed segments to locate contact points and parallel facets, and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters were prepared to adapt the algorithms to meet various requirements. The experimental section demonstrates the advantages of the algorithms by analyzing the cost and stability of the algorithms, the precision of the planned grasps, and the tunable parameters with both simulations and real-world experiments. Also, some examples of robotic assembly systems using the proposed algorithms are presented to demonstrate the efficacy.
Robotics and Autonomous Systems, 2012
This paper presents a simple grasp planning method for a multifingered hand. Its purpose is to compute a context-independent and dense set or list of grasps, instead of just a small set of grasps regarded as optimal with respect to a given criterion. By context-independent, we mean that only the robot hand and the object to grasp are considered. The environment and the position of the robot base with respect to the object are considered in a further stage. Such a dense set can be computed offline and then used to let the robot quickly choose a grasp adapted to a specific situation. This can be useful for manipulation planning of pick-and-place tasks. Another application is human-robot interaction when the human and robot have to hand over objects to each other. If human and robot have to work together with a predefined set of objects, grasp lists can be employed to allow a fast interaction. The proposed method uses a uniform sampling of the possible hand approaches. As this leads to many finger inverse kinematics tests, hierarchical data structures are employed to reduce the computation times. The data structures allow a fast determination of the points where the fingers can realize a contact with the object surface. The grasps are ranked according to a grasp quality criterion so that the robot will first parse the list from best to worse quality grasps, until it finds a grasp that is valid for a particular situation.
IEEE Transactions on Robotics, 2013
This paper presents a procedure to synthesize highquality grasps for objects that need to be held and manipulated in a specific way, characterized by a pre-specified set of contact constraints to be satisfied. Due to the multi-modal nature of typical grasp quality measures, approaches that resort to local optimization methods are likely to get trapped into local extrema on such problem. An additional difficulty of the problem is that the set of feasible grasps is a highly-dimensional manifold, implicitly defined by a system of non-linear equations. The proposed procedure finds a way around these issues by focusing the exploration on a relevant subset of grasps of lower dimension, and tracing this subset exhaustively using a higher-dimensional continuation technique. A detailed atlas of the subset is obtained as a result, on which the highest-quality grasp according to any desired criterion, or a combination of criteria, can be readily identified. Examples are included that illustrate the application of the method to a three-fingered planar hand and to the Schunk anthropomorphic hand grasping several objects, using several quality indices.
2009 International Conference on Mechatronics and Automation, 2009
Grasp planning for multifingered robotic hand is still time consuming. The crucial part is to find the contact points with collision detection techniques to evaluate the grasp quality and to guarantee that the hand does not collide with other objects. Our methods to accelerate the collision detection for grasp planning are presented in this paper. Grasping is performed in two steps: hand moving and finger closing. Finger links are a-priori known for both steps. We use precomputed bounding boxes to bound the extent of the finger links' motion to cull the objects that are far from robotic hand. A state-of-the-art continuous collision detection with conservative advancement is integrated to detect collisions between moving robotic hand and objects. For pick-and-place operation the environments by grasping and by placing are merged to one environment for grasp planning to find collision-free grasps for both pick and place. Ray intersections are further used to find out hidden grasping directions. We have tested our approach with three experiments: grasping a standalone object with one hand, two hands and grasping in complex environment. Results with fourfingered SAHands in simulation show the efficiency of the introduced methods.
Lecture Notes in Mechanical Engineering, 2020
This work presents grasp planning on everyday objects using vision. The hand considered is a one degree-of-freedom parallel jaw gripper of Mitsubishi Movemaster robot. Candidate grasping points are chosen on the object and a grasp matrix is computed for the grasp. The grasp matrix can be used to computationally determine a force-closure grasp feasibility. For selecting the candidate grasping points, image of the object is used. Three quality metrics based on different physical notions of quality of grasp are computed. The first quality measure tells how far a grasp is from violating the friction limits, the second gives the worst case performance of the force-closure for all external wrenches, and the third tells how well the object is enclosed from all directions. The main contribution of the paper is to compare grasps based on different quality measures and understand their physical interpretation.
Robotics and Computer-integrated Manufacturing, 1988
) is an offiine robot grasp planner for three-fingered grippers. The planner is based on a generate and test paradigm. Candidate triples of faces (or grasp configurations) are generated using heuristics. The precise coordinates of grip points are selected for each configuration by numerical solution of optimization problems. The constraints imposed by stability considerations are built into the optimization formulations. The accessibility of each candidate grasp is checked based on swept volume computation. Other criteria are used to rank the feasible grasps. This paper describes the modular design and the implementation of PERCE's GRIPES.
2010
This paper presents grasp planning for a multifingered hand with a humanoid robot. Our planner selects different ways of grasping even for the same object according to object position/orientation. If the planner cannot find a feasible grasp with arm/hand kinematics, it switches to full body motion planning. These functions are necessary for realizing the robust grasp planning. Our planner defines convex models on both the object and each grasp type. In considering geometrical relationships among these convex models, we determine the parameters required to define the final grasping configuration. We demonstrate effectiveness of grasp planning through simulation and experimental results.
Applied Sciences
In the grasping and manipulation of 3D deformable objects by robotic hands, the physical contact constraints between the fingers and the object have to be considered in order to validate the robustness of the task. Nevertheless, previous works rarely establish contact interaction models based on these constraints that enable the precise control of forces and deformations during the grasping process. This paper considers all steps of the grasping process of deformable objects in order to implement a complete grasp planning pipeline by computing the initial contact points (pregrasp strategy), and later, the contact forces and local deformations of the contact regions while the fingers close over the grasped object (grasp strategy). The deformable object behavior is modeled using a nonlinear isotropic mass-spring system, which is able to produce potential deformation. By combining both models (the contact interaction and the object deformation) in a simulation process, a new grasp plan...
2010 IEEE International Conference on Robotics and Automation, 2010
In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.
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