Accurate measuring and modeling of dynamic robot manipulation (e.g., tossing and catching) is particularly challenging, due to the inherent nonlinearity, complexity, and uncertainty in high-speed robot motions and highly dynamic robot–object interactions happening in very short distances and times. Most studies leverage extrinsic sensors such as visual and tactile feedback toward task or object-centric modeling of manipulation dynamics, which, however, may hit bottleneck due to the significant cost and complexity, e.g., the environmental restrictions. In this work, we investigate whether using solely the on-board proprioceptive sensory modalities can effectively capture and characterize dynamic manipulation processes. In particular, we present an object-agnostic strategy to learn the robot toss dynamics of arbitrary unknown objects from the spatio-temporal variations of robot toss movements and wrist-force/torque (F/T) observations. We then propose TossNet, an end-to-end formulation that jointly measures the robot toss dynamics and predicts the resulting flying trajectories of the tossed objects. Experimental results in both simulation and real-world scenarios demonstrate that our methods can accurately model the robot toss dynamics of both seen and unseen objects, and predict their flying trajectories with superior prediction accuracy in nearly real-time. Ablative results are also presented to demonstrate the effectiveness of each proprioceptive modality and their correlations in modeling the toss dynamics. Case studies show that TossNet can be applied on various real robot platforms for challenging tossing-centric robot applications, such as blind juggling and high-precise robot pitching.

This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, separating, colliding, and grasping. Each manipulation primitive is formulate as a free-terminal optimal control problem (OCP), aimed at computing the optimal pose (position and orientation) trajectories of the object and the robot subject to the pose and force linkage constraints between them and the expected force maintenance at contact. A single-arm regrasping task and a dual-arm dynamic handover task are conducted to demonstrate the effectiveness of the proposed algorithm.

Collaboration between humans and robots can be challenging because robots may have difficulty understanding a specific person’s intentions, particularly in complicated tasks such as co-manipulation and assembly in computer, communication, and consumer electronics (3C) manufacturing. These tasks require different weights on accuracy and speed for various fabrication steps, making traditional physical interaction inadequate. In this paper, we introduce a fuzzy reinforcement learning-based admittance controller that can infer humans’ intentions not only through physical interaction but also through natural language. During training, the natural language is encoded into a reward term to help the robot reach the human-intended convergence point, allowing us to develop a “personalized” policy. During testing, the language serves as a tool to help the robot understand and obey humans’ intentions when physical interaction alone is insufficient. For example, if the user finds it difficult to push the robot and needs it to move faster, they can say “it’s really slow,” while a request for high-accuracy operation can be conveyed through “the damping is too small.” With this algorithm, the robot can comprehend the intentions and act accordingly in such situations. Further results and videos can be found at: https://sites.google.com/view/hri-nlp.

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Fog computing emerges as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By offloading partial computation-intensive tasks from fog node to cloud server, the computation experience of users can be further improved in fog computing system. In this paper, we develop an energy-efficient computation offloading scheme for IIoT in fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. It has a comprehensive consideration on the components of energy consumption at fog node, which includes the energy consumption of local computing, transmitting and waiting states. To address this energy minimization problem, an accelerated gradient algorithm is proposed, it can find the optimal offloading ratio with a fast speed that improves the convergence speed of traditional method. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing and full offloading schemes in terms of energy consumption and completion time, and further confirm the advantage of convergence rate.

The D2D (Device-to-Device) communication has been very popular as it is a promising and low-cost solution to reduce the burden on the cellular network. However, there are rare concerns about the distribution and pairing of DUEs(D2D user equipments), which have a significant impact on QoS (Quality of Service) of D2D communication. In this paper, we propose a novel algorithm based on the coalitional game to optimally adjust the distribution of DUEs. The proposed algorithm aims to form the optimal coalition structure, which achieves a balance between the throughput and power consumption of each coalition, obtaining the enhanced QoS of D2D. We show that our algorithm is superior to the benchmark models in terms of the throughput and energy efficiency of the DUE coalition. To further improve the QoS, we also propose a method to predict and maximize the pairing probability of DUEs. The proposed prediction method adopts the Logistic Regression to model the global pairing probability according to the communication parameters of DUEs. Experimental results show that the proposed prediction method is significantly superior to the benchmark methods in terms of prediction accuracy. In addition, the pairing probability maximization algorithm proposed also significantly improves the pairing probability.

According to efficient performance requirement of big sensory data compression and collection, this paper proposes a fog computing based optimized compressive data collection scheme to enhance recovery quality of original data. In this scheme, mutual correlations of big sensory data are exploited fully owing to the designed data collection architecture. The data processing of fog node urges the computation capability of edge device can be utilized effectively, and which reduces the amount of data transmission significantly. At the same time the constructed encoding and decoding methods among sensory, fog and cloud nodes guarantee successful performing of conventional compressed sensing (CS) reconstruction algorithm with overwhelming probability. In addition, since recovery error is proportional to the mutual coherence among measurement matrix, network coding (NC) transformation matrix and sparsifying basis, a measurement matrix optimization algorithm is constructed to minimize the mutual coherence for stabilizing and enhancing data recovery quality. The desired solution of mutual coherence can be achieved by integrating the alternating minimization and low-pass filtering methods. Simulation results illustrate that the constructed optimization algorithm can obtain the optimal value of mutual coherence, and reconstruction quality of our developed scheme is higher as compared with other compressive data collection schemes.

Device-to-device (D2D) communication is a promising technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem in D2D underlay communication with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D user equipment (DUE) and cellular user equipment (CUE) are taken into consideration under the control of proposed efficiency functions. Problems are formulated as a series of utility functions built on the unit price of jamming power and the amount of jamming service. Extracting system model into a price negotiation under Bargaining Game (PNBG) that a buyer and a seller both desiring maximum its profits, we solve the problems by reaching an agreement of the two sides. The step number of bargain process is also a restriction under consideration. For the Non-Step scheme, an Evaluation Function (EF) and a Comprehensive Utility Function (CUF) are demonstrated to analyze the negotiation process. For Step-Contained scheme, the step number of iteration is involved and an Attenuation Function (AF) is introduced to modify the Bargaining Game. Algorithms of two schemes are designed to derive the equilibrium point for reaching an agreement. Finally, simulations are illustrated for verifying proposed approach.

Heterogeneous collaborative networks have been ever-increasingly concerned due to the constant development of wireless networks. However, before employed in commercial applications, the secure charging should be solved; say, the charging systems with the guarantee of security are vital to support this architecture. Meanwhile, nodes’ non-cooperation behavior should be under control as well. Hence, a secure incentive-based charging solution for heterogeneous collaborative networks integrating cellular and MANET (Ad Hoc) is proposed, utilizing charging receipt to thwart non-reputation attacks. Theoretical verification shows that proposed scheme not only grants the existence of selfish nodes to meet their rational demand, but is robust enough to circumvent various active attacks. Finally, simulation analysis reveals the influence of the parameters in proposed scheme on routing stability and node cooperation in low overhead.

ECG (electrocardiogram) recognition and monitor are inevitable to trace and determine heart diseases. As self-health being focused on and social medical grade being progressed, ECG monitors with features such as portable/wearable, wireless, use-friendly, low-cost and convenient at home, are more and more necessary. Unfortunately, such kind of equipments couldn't be got currently. Thus, wearable ECG recognition and monitor instrument is developed. Palm, mobile phone and PC could be acting as display and relay terminals, where ECG signals would be transmitted to service center (e.g. hospital) through GSM/GPRS/CDMA and Internet. After introducing system architecture, the paper describes software design, direct ECG recognition method with morphology parameters based on specialists' experiences. The first generation product includes wearable monitor and palm is ready now, which has huge market.