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The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. [ABSTRACT FROM AUTHOR]

Abstract: The interference effect on an array of electrospray emitters is analytically and experimentally investigated. An analytical model is presented to predict the behavior of the operating voltage with respect to emitter spacing in an array of emitters. The basic idea of these models is to superimpose the electric potential of individual emitters together in an array of emitters. If only one of the emitters operates and no liquid is supplied through the neighboring emitters, the potential required to form a stable cone-jet generally increases as the emitters move closer to each other due to electrical shielding. However, at very close spacing the required potential decreases. If all the emitters operate simultaneously, the operating voltage required for cone-jet spraying increases as the emitter spacing decreases; furthermore, there is no decrease in potential when the spacing is very close. The results of the analytical electrostatic interference model agree well with the experimental data. [Copyright &y& Elsevier]

Abstract: To learn biped walking dynamics accurately, and then compensate time-varying external disturbances timely, a time-sequence-based fuzzy SVM (TSF-SVM) learning control system considering time properties of biped walking samples is proposed. For the first time, time-sequence-based triangular and Gaussian fuzzy membership functions have been proposed for the single support phase (SSP) and the double support phase (DSP), respectively, according to time properties of different biped phases, which provides an effective way to formulate time properties of biped walking samples in the context of time-varying external disturbances. In addition, a time-sequence-based moving learning window (TS-MLW) is proposed for online training of the proposed TSF-SVM. The performance of the proposed TSF-SVM is compared with other typical intelligent methods; simulation results demonstrate that the proposed method is more sensitive to occasional external disturbances, which increases the stability margin and prevents the robot from falling down. [Copyright &y& Elsevier]

Abstract: In this paper, we present visibility-based spatial reasoning techniques for real-time object manipulation in cluttered environments. When a robot is requested to manipulate an object, a collision-free path should be determined to access, grasp, and move the target object. This often requires processing of time-consuming motion planning routines, making real-time object manipulation difficult or infeasible, especially in a robot with a high DOF and/or in a highly cluttered environment. This paper places special emphasis on developing real-time motion planning, in particular, for accessing and removing an object in a cluttered workspace, as a local planner that can be integrated with a general motion planner for improved overall efficiency. In the proposed approach, the access direction of the object to grasp is determined through visibility query, and the removal direction to retrieve the object grasped by the gripper is computed using an environment map. The experimental results demonstrate that the proposed approach, when implemented by graphics hardware, is fast and robust enough to manipulate 3D objects in real-time applications. [Copyright &y& Elsevier]