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In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

Cooperative manipulation of a rigid object is challenging and represents an interesting and active research area, especially when these robots are subject to joint and task prioritization constraints. In cooperative manipulation, a primary task is to maintain the coordination of motions, to avoid severe damage caused by the violation of kinematic constraints imposed by the closed chain mechanism. This paper proposes a kinematic controller for dual-arm coopera

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based cameras sets a limitation on continuous visual feedback due to their low sampling rate, poor performance in low light conditions and redundant data in real-time image processing, especially in the case of high-speed tasks. Neuromorphic event-based vision is a recent technology that gives human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution (1µs) with low latency and wide dynamic range. In this paper, for the first time, we present a purely event-based visual servoing method using a neuromorphic camera in an eye-in-hand configuration for the grasping pipeline of a robotic manipulator. We devise three surface layers of active events to directly process the incoming stream of events from relative motion. A purely event-based approach is used to detect corner features, localize them robustly using heatmaps and generate virtual features for tracking and grasp alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in Spatiotemporal space. The controller switches its operation such that it explores the workspace, reaches the target object and achieves a stable grasp. The event-based visual servoing (EBVS) method is comprehensively studied and validated experimentally using a commercial robot manipulator in an eye-in-hand configuration for both static and dynamic targets. Experimental results show superior performance of the EBVS method over frame-based vision, especially in high-speed operations and poor lighting conditions. As such, EBVS overcomes the issues of motion blur, lighting and exposure timing that exist in conventional frame-based visual servoing methods.

Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (∆t = 500µs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.

This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

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