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Telekinesis, as commonly portrayed in science fiction literature and cinema, is a super power wherein users control and manipulate objects absent in physical interaction. In real world, enhancing human–robot interaction needs the synthesis of human intuitive processes with robotic arm. This paper introduces a robotic teleoperation system achieving the essence of telekinetic operations, combining the profound capabilities of augmented reality (AR) with the robotic arm operations. Utilizing AR, the proposed methodology offers operators with a visual feedback, facilitating a level of control surpassing the capacities of natural interfaces. By using AR-driven visual recognition, this system achieves operations in a virtual environment, subsequently actualized in the real world through the robotic arm. Through multiple experiments, we found that the system has a small margin of error in telekinesis operations, meeting the needs of remote operation. Furthermore, our system can operate on objects in the real world. These experiments underscore the capability of the remote control system to assist humans in accomplishing a wider range of tasks through the integration of AR and robotic arms, providing a natural human–robot interaction approach.

Audio‐visual scene classification (AVSC) poses a formidable challenge owing to the intricate spatial‐temporal relationships exhibited by audio‐visual signals, coupled with the complex spatial patterns of objects and textures found in visual images. The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures, inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio‐visual data. The authors present a feature pyramid attention network (FPANet) for audio‐visual scene understanding, which extracts semantically significant characteristics from audio‐visual data. The authors’ approach builds multi‐scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module (FPAM). A dimension alignment (DA) strategy is employed to align feature maps from multiple layers, a pyramid spatial attention (PSA) to spatially locate essential regions, and a pyramid channel attention (PCA) to pinpoint significant temporal frames. Experiments on visual scene classification (VSC), audio scene classification (ASC), and AVSC tasks demonstrate that FPANet achieves performance on par with state‐of‐the‐art (SOTA) approaches, with a 95.9 F1‐score on the ADVANCE dataset and a relative improvement of 28.8%. Visualisation results show that FPANet can prioritise semantically meaningful areas in audio‐visual signals.

Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

A thorough introduction to the development and applications of intelligent wearable interfaces As mobile computing, sensing technology, and artificial intelligence become more advanced and their applications more widespread, the area of intelligent wearable interfaces is growing in importance. This emerging form of human-machine interaction has infinite possibilities for enhancing humans' capabilities in communications, actions, monitoring, and control. Intelligent Wearable Interfaces is a collection of the efforts the authors have made in this area at The Chinese University of Hong Kong. They introduce methodologies to develop a variety of intelligent wearable interfaces and cover practical implementations of systems for real-life applications. A number of novel intelligent wearable interface systems are examined, including: Network architecture for wearable robots Wearable interface for automatic language translation Intelligent cap interface for wheelchair control Intelligent shoes for human-computer interface Fingertip human-computer interface Ubiquitous 3D digital writing instrument Intelligent mobile human airbag system This book is a valuable reference for researchers, designers, engineers, and upper-level undergraduate and graduate students in the fields of human-machine interactions,rehabilitation engineering, robotics, and artificial intelligence.

Copyright ©2015 Zhejiang University Press, Published by Elsevier Inc.Household Service Robotics is a collection of the latest technological advances in household service robotics in five main areas: robot systems, manipulation, navigation, object recognition, and human-robot interaction. The book enables readers to understand development s and apply them to their own working areas, including:Robotic technologies for assisted living and elderly careDomestic cleaning automationHousehold surveillanceGuiding systems for public spaces Service robotics is a highly multidisciplinary field, requiring a holistic approach. This handbook provides insights to the disciplines involved in the field as well as advanced methods and techniques that enable the scale-up of theory to actual systems. It includes coverage of functionalities such as vision systems, location control, and HCI, which are important in domestic settings.Provides a single source collection of the latest development in domestic robotic systems and controlCovers vision systems, location control, and HCI, important in domestic settingsFocuses on algorithms for object recognition, manipulation, human-robot interaction, and navigation for household robotics

In this paper, autonomous motion control approaches to generate the coordinated motion of a dual-arm space robot for target capturing are presented. Two typical cases are studied: (a) The coordinated dual-arm capturing of a moving target when the base is free-floating; (b) one arm is used for target capturing, and the other for keeping the base fixed inertially. Instead of solving all the variables in a unified differential equation, the solution equation of the first case is simplified into two sub-equations and practical methods are used to solve them. Therefore, the computation loads are largely reduced, and feasible trajectories can be determined. For the second case, we propose to deal with the linear and angular momentums of the system separately. The linear momentum conservation equation is used to design the configuration and the mounted pose of a balance arm to keep the inertial position of the base's center of mass, and the angular momentum conservation equation is used to estimate the desired momentum generated by the reaction wheels for maintaining the inertial attitude of the base. Finally, two typical tasks are simulated. Simulation results verify the corresponding approaches.

The energy of a space station is a precious resource, and the minimization of energy consumption of a space manipulator is crucial to maintain its normal functionalities. This paper first presents novel gaits for space manipulators by equipping a new gripping mechanism. With the use of wheels locomotion, lower energy demand gaits can be achieved. With the use of the proposed gaits, we further develop a global path planning algorithm for space manipulators which can plan a moving path on a space station with a minimum total energy demand. Different from existing approaches, we emphasize both the use of the proposed low energy demand gaits and the gaits composition during the path planning process. To evaluate the performance of the proposed gaits and path planning algorithm, numerous simulations are performed. Results show that the energy demand of both the proposed gaits and the resultant moving path is also minimum.

The technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms.

With the substantial increase of vehicles on road, driving safety and transportation efficiency have become increasingly concerned focus from drivers, passengers, and governments. Wireless networks constructed by vehicles and infrastructures provide abundant information to share for the sake of both enhanced safety and network efficiency. This paper presents the systematic research to enhance the vehicle safety by wireless communication, in the aspects of information acquisition through vehicle sensing, vehicle-to-vehicle (V2V) routing protocol for the highly dynamic vehicle network, vehicle-to-infrastructure (V2I) routing protocol for a tradeoff in real-time performance and load balance, and hardware implementation of V2V system with on-road test. Simulations and experimental result validate the feasibility of the algorithms and communication system.