https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1065&context=usafresearch

A review of space robotics technologies for on-orbit servicing

The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. The quality measures are classified into two groups according to the main aspect they evaluate: location of contact points on the object and hand configuration. The approaches that combine different measures from the two previous groups to obtain a global quality measure are also reviewed, as well as some measures related to human hand studies and grasp performance. Several examples are presented to illustrate and compare the performance of the reviewed measures.

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Grasp quality measures: review and performance

This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.

/pdf/sampling-based-roadmap-of-trees-for-parallel-motion-planning-5ur1dydtyh.pdf

Sampling-based roadmap of trees for parallel motion planning

The correct grasp of objects is a key aspect for the right fulfillment of a given task. In robotics, the development of grippers more and more complex and versatile, such as mechanical hands, augments the necessity for algorithms to automatically determine grasps, and in a parallel way, arises the need to quantify the quality of the generated grasps in order to optimize them. This work reviews the quality measures proposed in the grasp literature to quantify the grasp quality. We classify the quality measures into two groups according to the main aspect evaluated by the measure: the location of the contact points on the object or the hand configuration. We also review the approaches that combine different quality measures from the two previous groups to obtain a global quality measure.

Grasp quality measures

Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of information about the objects in the environment as well as the actions of the robot during the interactions. In this article, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The article also includes an overview of different types of robot applications and the types of tactile information that they employ. Finally we end the article with a discussion for future tactile applications which are still beyond the current capabilities of robots.

/pdf/a-review-of-tactile-information-perception-and-action-1uw4hkx24d.pdf

A Review of Tactile Information: Perception and Action Through Touch

This paper is concerned with parameter identification methods for inertial parameters of the unknown object handled by manipulators on a free-flying space robot. The parameter identification is necessary for precise control because the payload changes the kinematics of the system together with the dynamics. Two methods are proposed under the condition that the robot is free to translate and rotate. One method is based on the conservation principle of linear and angular momentum and the other on Newton-Euler equations of motion. Only the linear/ angular velocities and accelerations of the satellite base are used in the identification methods with no information about the force and torque utilized. The feasibility of the methods is demonstrated by a hardware experiment on the ground as well as numerical simulation.

Parameter identification of unknown object handled by free-flying space robot

A space robot consisting of a satellite main body that flies freely and manipulators that have structural flexibility is considered. The position and orientation of the satellite main body may change when the manipulators move. Motions of the space robots also induce vibrating motions of the structurally flexible manipulators. The control schemes used are the virtual rigid manipulator, pseudo-resolved acceleration control, and composite control. A singular perturbation strategy is applied, and the stability of the controls is examined. Numerical examples are given. >

Trajectory control of flexible manipulators on a free-flying space robot

In many cases of manipulating an object stably and accurately by robot, it is required to know the mass and center of mass of the object. For the case when the weight or shape of an object is over the grasp capacity of a robot hand, the paper proposes a technique that can estimate the mass and center of mass of the graspless and shape-unknown object. A plane called gravity equi-effect plane is first defined, which contains the center of mass and a contact line where the object is in line-contact with an environment. If three or over three orientation-different gravity equi-effect planes of an object are obtained, the center of mass of the object can be estimated by the intersect point of the planes. In order to estimate the gravity equi-effect plane, tip operation by robot finger, which tips the object repeatedly, is proposed. Then an algorithm to estimate the gravity equi-effect plane and an algorithm to estimate the mass and center of mass of the object are addressed by using the fingertip position and force information measured from tip operations.

Estimation of mass and center of mass of graspless and shape-unknown object

/sup F/or dexterously performing object grasping and manipulation with multifingered hand of robot, sensing the finger joint torque is required. In general, the size of finger joint is quite small so that it is hard to realize the torque sensing. This paper proposes a novel mechanism, called the strain-deformation expansion mechanism, to sense the joint torque, which is small enough and fitted in the finger joint. By the torque sensing mechanism, the small joint strain-deformation used for torque sensing can be expanded without reducing the joint stiffness. In this paper, the torque sensing principle is addressed by analyzing the deformation of the sensing mechanism and the torques acting on the joint shaft theoretically. Then, the sensitivity of the sensing mechanism and its expansion rate of sensitivity are defined, and a design method for realizing the sensing mechanism with high sensitivity is discussed. Finally, some experiments with robot finger are performed to show the basic characteristics and the effectiveness of the proposed torque sensing mechanism.

Torque sensing of finger joint using strain-deformation expansion mechanism

This paper proposes a technique that can estimate the inertia parameters of a graspless unknown object, which is pushed by robot fingers. Using the fingertip different accelerations (or angular accelerations), velocities (or angular velocities) and forces information measured in pushing operations, the algorithms to estimate the object mass (or moment of inertia) are described. Then, a line called C.M. Line, is defined in this paper. The line contains the center of mass and is between two fingertips which are in point-contact with an object side. By using two or more than two orientation-different C.M. lines, an algorithm to estimate the center of mass of the object is given. Lastly, experimental verification on the proposed approach is performed and its results are outlined.

Showzow Tsujio

Papers

Parameter identification of unknown object handled by free-flying space robot

Trajectory control of flexible manipulators on a free-flying space robot

Estimation of mass and center of mass of graspless and shape-unknown object

Torque sensing of finger joint using strain-deformation expansion mechanism

Estimation of Object Inertia Parameters on Robot Pushing Operation