This letter investigates the optimal hovering altitude of a single UAV toprovide coverage over any convex quadrilateral region on the ground. The UAVemploys a directional antenna with a tiltable beam, producing an ellipticalcoverage pattern. Two scenarios are considered: (1) inscribing the largestellipse within the quadrilateral to cover its interior, and (2) circumscribingthe smallest ellipse about the quadrilateral to ensure full coverage. We derivethe optimal UAV altitude and antenna tilt conditions in both scenarios for asimplified yet widely accepted path loss model and present numerical resultsfor coverage efficiency. The work contributes to the development ofenergy-efficient UAV-based communication systems. <<<

In the last ten years, different concepts of electric vertical take-off and landing aircrafts (eVTOLs) have been tested. This article addresses the problem of the choice of the best configuration. VTOLs built since the fifties are presented and their advantages, disadvantages, and problems are discussed. Three representative eVTOLs, one for each main configuration, are compared on five main parameters and three reference missions. The parameters are disk loading, total hover time, cruise speed, practical range, and flight time. The performance of the eVTOLs on the urban, extra-urban, and long-range mission is evaluated computing the time and energy required. The results show that the best configuration depends on the mission. The multirotor is more efficient in hover. The vectored thrust jet is more efficient in cruise and has a higher range. The lift + cruise is a compromise. <<<

This paper presents a prototype of a low-cost two-phase axial-gap transverse flux generator, in which the magnetic and electric circuits have been made of reused materials, and the stator housing has been manufactured by 3D printing of plastic. Therefore, this work presents as a novelty the combination of the novel transverse flux topology and two challenging trends in electrical machines manufacturing, such as reusing of components and additive manufacturing. Axial-gap transverse flux machines potentially enable the combination of two of the main advantages of axial flux machines and transverse flux machines, i.e., short axial length and a high number of poles. The two-phase arrangement with shared air gap is of great interest in order to reduce further the axial length while avoiding the use of magnetic materials in the rotor, such as iron or soft magnetic composites. However, the equivalent air gap might be large, with significant leakage and fringing effects as the magnetic flux closes through the air. Therefore, in this paper the accuracy of the analytical equations and the magnetic equivalent circuit is firstly investigated. The two-phase axial-gap transverse flux machine is prone to misalignment between phases and rotor imbalances that alter the air gap length, so these effects have been included in the simulations with the finite element method. Experimental tests have been conducted throughout the investigation, from the prototype characterization to the steady-state operation, both with no load and with resistive loads. <<<

AbstractWith the further transformation of The Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the new generation of fiber positioner robot chooses a 4 mm hollow cup motor with minimum phase inductance. Because the load of the fiber positioner robot is constant and the inertia of the motor is very small, an open loop positioning control method based on Space Vector Pulse Width Modulation is proposed, and the specific open loop parameters are directly tuned by relevant experimental strategies. The critical factors of the open loop driving mode are discussed in detail from four aspects: subdivision, fundamental frequency, wave generation mode and peak current. Based on the actual fiber positioner robot, the hardware driver and assessment platform are built. The positioning tests show that the method proposed is practical and effective, and meets the precision positioning demand of the new generation optical fiber positioner robot. <<<

This paper introduces the Stoch BiRo, a cost-effective bipedal robot designedwith a modular mechanical structure having point feet to navigate uneven andunfamiliar terrains. The robot employs proprioceptive actuation in abduction,hips, and knees, leveraging a Raspberry Pi4 for control. Overcomingcomputational limitations, a Learning-based Linear Policy controller managesbalance and locomotion with only 3 degrees of freedom (DoF) per leg, distinctfrom the typical 5DoF in bipedal systems. Integrated within a modular controlarchitecture, these controllers enable autonomous handling of unforeseenterrain disturbances without external sensors or prior environment knowledge.The robot's policies are trained and simulated using MuJoCo, transferringlearned behaviors to the Stoch BiRo hardware for initial walking validations.This work highlights the Stoch BiRo's adaptability and cost-effectiveness inmechanical design, control strategies, and autonomous navigation, promisingdiverse applications in real-world robotics scenarios. <<<

This paper aims to generate materials for 3D meshes from text descriptions.Unlike existing methods that synthesize texture maps, we propose to generatesegment-wise procedural material graphs as the appearance representation, whichsupports high-quality rendering and provides substantial flexibility inediting. Instead of relying on extensive paired data, i.e., 3D meshes withmaterial graphs and corresponding text descriptions, to train a material graphgenerative model, we propose to leverage the pre-trained 2D diffusion model asa bridge to connect the text and material graphs. Specifically, our approachdecomposes a shape into a set of segments and designs a segment-controlleddiffusion model to synthesize 2D images that are aligned with mesh parts. Basedon generated images, we initialize parameters of material graphs and fine-tunethem through the differentiable rendering module to produce materials inaccordance with the textual description. Extensive experiments demonstrate thesuperior performance of our framework in photorealism, resolution, andeditability over existing methods. Project page: https://zju3dv.github.io/MaPa <<<

Developing anode materials with high specific capacity and cycling stability is vital for improving thin‐film lithium‐ion batteries. Thin‐film zinc oxide (ZnO) holds promise due to its high specific capacity, but it suffers from volume changes and structural stress during cycling, leading to poor battery performance. In this research, we ingeniously combined polytetrafluoroethylene (PTFE) with ZnO using a radio frequency (RF) magnetron co‐sputtering method, ensuring a strong bond in the thin‐film composite electrode. PTFE effectively reduced stress on the active material and mitigated volume change effects during Li+ ion intercalation and deintercalation. The composite thin films are thoroughly characterized using advanced techniques such as X‐ray diffraction, scanning electron microscopy, and X‐ray photoelectron spectroscopy for investigating correlations between material properties and electrochemical behaviors. Notably, the ZnO/PTFE thin‐film electrode demonstrated an impressive specific capacity of 1305 mAh g−1 (=7116 mAh cm−3) at a 0.5C rate and a remarkable capacity retention of 82% from the 1st to the 100th cycle, surpassing the bare ZnO thin film (50%). This study provides valuable insights into using binders to stabilize active materials in thin‐film batteries, enhancing battery performance. <<<

Autonomous systems are soon to be ubiquitous, from manufacturing autonomy toagricultural field robots, and from health care assistants to the entertainmentindustry. The majority of these systems are developed with modularsub-components for decision-making, planning, and control that may behand-engineered or learning-based. While these existing approaches have beenshown to perform well under the situations they were specifically designed for,they can perform especially poorly in rare, out-of-distribution scenarios thatwill undoubtedly arise at test-time. The rise of foundation models trained onmultiple tasks with impressively large datasets from a variety of fields hasled researchers to believe that these models may provide common sense reasoningthat existing planners are missing. Researchers posit that this common sensereasoning will bridge the gap between algorithm development and deployment toout-of-distribution tasks, like how humans adapt to unexpected scenarios. Largelanguage models have already penetrated the robotics and autonomous systemsdomains as researchers are scrambling to showcase their potential use cases indeployment. While this application direction is very promising empirically,foundation models are known to hallucinate and generate decisions that maysound reasonable, but are in fact poor. We argue there is a need to step backand simultaneously design systems that can quantify the certainty of a model'sdecision, and detect when it may be hallucinating. In this work, we discuss thecurrent use cases of foundation models for decision-making tasks, provide ageneral definition for hallucinations with examples, discuss existingapproaches to hallucination detection and mitigation with a focus on decisionproblems, and explore areas for further research in this exciting field. <<<

AbstractAutomobile drive motor with flat wire winding has improved the slot fill factor and efficiency. However, under the current environment of drive motors development to high frequency and voltage, the increase in winding AC loss has weakened those advantages. Accurate analysis of AC loss and the design of high‐performance windings have become the key issues and difficulties in the design process of drive motors. Based on the winding AC loss generation mechanism, the authors propose a winding AC loss analysis method, which can effectively separate DC loss, eddy current loss, and circulating current loss. The method is used to analyse winding AC loss of 60 kW flat wire winding permanent magnet synchronous motor (PMSM). In addition, parallel multi‐strand flat wire windings are proposed to reduce the winding AC loss and eliminate the limitation of the magnetic load of the motor on the selection of winding layers. The AC loss of the parallel multi‐strand flat wire windings at different frequencies is calculated, which demonstrates the effectiveness of the proposed winding topology in reducing the winding AC loss. In addition, the correctness of the simulation model was verified through experiments, and the loss calculation method was verified through finite element analysis. <<<

Licheng Wen, Xuemeng Yang, Daocheng Fu, Xiaofeng Wang, Pinlong Cai, Xin Li, Tao Ma, Yingxuan Li, Linran Xu, Dengke Shang, Zheng Zhu, Shaoyan Sun, Yeqi Bai, Xinyu Cai, Min Dou, Shuanglu Hu, Botian Shi, Yu Qiao <<<

The pursuit of autonomous driving technology hinges on the sophisticatedintegration of perception, decision-making, and control systems. Traditionalapproaches, both data-driven and rule-based, have been hindered by theirinability to grasp the nuance of complex driving environments and theintentions of other road users. This has been a significant bottleneck,particularly in the development of common sense reasoning and nuanced sceneunderstanding necessary for safe and reliable autonomous driving. The advent ofVisual Language Models (VLM) represents a novel frontier in realizing fullyautonomous vehicle driving. This report provides an exhaustive evaluation ofthe latest state-of-the-art VLM, GPT-4V(ision), and its application inautonomous driving scenarios. We explore the model's abilities to understandand reason about driving scenes, make decisions, and ultimately act in thecapacity of a driver. Our comprehensive tests span from basic scene recognitionto complex causal reasoning and real-time decision-making under varyingconditions. Our findings reveal that GPT-4V demonstrates superior performancein scene understanding and causal reasoning compared to existing autonomoussystems. It showcases the potential to handle out-of-distribution scenarios,recognize intentions, and make informed decisions in real driving contexts.However, challenges remain, particularly in direction discernment, trafficlight recognition, vision grounding, and spatial reasoning tasks. Theselimitations underscore the need for further research and development. Projectis now available on GitHub for interested parties to access and utilize:\url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration} <<<

Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality. The code and datasets are available at this https URL. <<<

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a visual-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Project website: this https URL <<<

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications. The advancement of Artificial General Intelligence (AGI) that transcends task and application boundaries is critical for enhancing IDM. Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks, including computer vision, natural language processing, and reinforcement learning. We propose that a Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture, offering a promising solution for expanding IDM applications in complex real-world situations. In this paper, we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI, production scheduling, and robotics tasks. Lastly, we present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks, such as text generation, image captioning, video game playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 represents an initial step toward more autonomous and efficient real-world IDM applications. <<<

A high-throughput lithographic method with 25-nanometer resolution and smooth vertical sidewalls is proposed and demonstrated. The technique uses compression molding to create a thickness contrast pattern in a thin resist film carried on a substrate, followed by anisotropic etching to transfer the pattern through the entire resist thickness. Metal patterns with a feature size of 25 nanometers and a period of 70 nanometers were fabricated with the use of resist templates created by imprint lithography in combination with a lift-off process. With further development, imprint lithography should allow fabrication of sub-10-nanometer structures and may become a commercially viable technique for manufacturing integrated circuits and other nanodevices. <<<