Task Accuracy Measure based on Dynamic Process for Cooperating Manipulation System

Abstract-The present paper introduces an evaluation of the manipulation performance of a cooperating robotic manipulator with respect to task accuracy, taking into consideration the effects of the dynamic process between inputs and outputs in the manipulation system. A measure based on the output controllability of the manipulation system is proposed, which shows the relationship between the object's position and orientation and the joint driving force. Computer simulations show the validity of the task accuracy measure and the difference between the proposed measure and the conventional manipulability measure

Randomized Planning and Control Strategy for Whole-Arm Manipulation of a Slippery Polygonal Object

The present paper introduces a planning and control strategy for whole-arm manipulation of a slippery polygonal object. Randomized planning methods are first proposed in order to generate contact state transitions, which help not only to reduce the amount of calculation required, but also to handle a hybrid system composed of a continuous system and a discrete system, which has a large search space and complicated constraints. Second, a novel control strategy, which can switch manipulation modes among quasi-static, dynamic, and caging manipulation depending on the situation, is proposed. This strategy not only can cope with changes in the mechanics of the system caused by contact state transitions, but also can increase the manipulation feasibility and reliability. The validity of the proposed methods is verified through simulations and experiments

Control for throwing manipulation by one joint robot

Abstract-This paper proposes a throwing manipulation strategy for a robot with one revolute joint. The throwing manipulation enables the robot not only to manipulate the object to outside of the movable range of the robot, but also to control the position of the object arbitrarily in the vertical plane even though the robot has only one degree of freedom. In the throwing manipulation, the robot motion is dynamic and quick, and the contact state between the robot and the object changes. These make it difficult to obtain the exact model and solve its inverse problem. In addition, since the throwing manipulation requires more powerful actuators than the static manipulation, we should set the control input by taking consideration of the performance limits of the actuators. The present paper proposes the control strategy based on the iteration optimization learning to overcome the above problems and verifies its effectiveness experimentally

Markedly Different Pathogenicity of Four Immunoglobulin G Isotype-Switch Variants of an Antierythrocyte Autoantibody Is Based on Their Capacity to Interact in Vivo with the Low-Affinity Fcγ Receptor III

Using three different Fcγ receptor (FcγR)-deficient mouse strains, we examined the induction of autoimmune hemolytic anemia by each of the four immunoglobulin (Ig)G isotype-switch variants of a 4C8 IgM antierythrocyte autoantibody and its relation to the contributions of the two FcγR, FcγRI, and FcγRIII, operative in the phagocytosis of opsonized particles. We found that the four IgG isotypes of this antibody displayed striking differences in pathogenicity, which were related to their respective capacity to interact in vivo with the two phagocytic FcγRs, defined as follows: IgG2a > IgG2b > IgG3/IgG1 for FcγRI, and IgG2a > IgG1 > IgG2b > IgG3 for FcγRIII. Accordingly, the IgG2a autoantibody exhibited the highest pathogenicity, ∼20–100-fold more potent than its IgG1 and IgG2b variants, respectively, while the IgG3 variant, which displays little interaction with these FcγRs, was not pathogenic at all. An unexpected critical role of the low-affinity FcγRIII was revealed by the use of two different IgG2a anti–red blood cell autoantibodies, which displayed a striking preferential utilization of FcγRIII, compared with the high-affinity FcγRI. This demonstration of the respective roles in vivo of four different IgG isotypes, and of two phagocytic FcγRs, in autoimmune hemolytic anemia highlights the major importance of the regulation of IgG isotype responses in autoantibody-mediated pathology and humoral immunity

Robust hitting with dynamics shaping

Abstract—The present paper proposes the motion planning based on “the dynamics shaping ” for a robotic arm to hit the target robustly toward the desired direction, of which the concept is to shape the robot dynamics appropriately in order to accomplish the desired motion. According to the linear system theory, the positional error of the end-point converges onto near the singular vector corresponding to its maximum singular value of the output controllability matrix of the robotic arm. Therefore, if we can control the direction of the singular vector by applying the dynamics shaping, we will be able to control the direction of the positional error of the end-effector caused by the disturbance. We propose a novel motion planning based on the dynamics shaping and verify numerically and experimentally that the robotic arm can robustly hit the target toward the desired direction with a simple open-loop control system even though the disturbance is applied. I

Admittance learning strategy using generalized simplex gradient methods for human–robot collaboration

Human-Robot Collaboration (HRC) systems are becoming widespread in industrial applications owing to the advantages of enhancing work productivity and reducing production expenses. In this study, we focus on HRC systems that involve physical interactions between humans and robots. The relationship between force and position at the robot’s end-effector is generally modulated using impedance or admittance control techniques to implement these systems. Furthermore, varying the target impedance of robots has been shown to enhance their performance in HRC tasks. This report presents a novel approach to admittance learning strategy aimed at minimizing human effort during physical human-robot collaboration tasks. A damping generation scheme based on Gaussian basis functions is introduced, enabling the generation of a diverse range of smooth damping profiles via the modulation of the weights of these functions. The Gaussian design is based on a frequency analysis of human movement, with weights adjusted via gradient descent to minimize the interaction force. A learning algorithm based on the generalized simplex gradient approximation technique is proposed to accommodate the noisy evaluation function, utilizing data from previous iterations to enhance estimation accuracy. The effectiveness of the proposed method is experimentally demonstrated through comparison to conventional methods, as well as trials involving a complex task