Miracle-dz/UniFAR-FTUs huggingface dataset:
Fingertip Pulse-Echo Ultrasound and Optoacoustic Dual-Modal and Dual Sensing Mechanisms Near-Distance Sensor for Ranging and Material Sensing in Robotic Grasping*

To improve robotic grasping, we are interested in developing a new non-contact fingertip-mounted sensor for near-distance ranging and material sensing. Here we report new progress in combining direct pulse-echo ultrasound and optoacoustic effects in sensor design to deal with optically and/or acoustically challenging targets (OACTs). Our dual-modal and dual sensing mechanisms (DMDSM) sensor design is enabled by a novel wideband ultrasound transmitter embedded inside a piezoelectric (lead zirconate titanate - PZT) ring transducer. The new DMDSM sensor is capable of differentiating a variety of OACTs. To verify our design, both distance ranging tests and material sensing tests have been conducted. The ranging tests show the sensor can perform both optoacoustic ranging (for light-absorbing materials) and pulse-echo ultrasound ranging (for reflective or transparent materials). For material sensing, the dual-modal spectra from OACTs are collected to compare the new sensor with previous designs. The overall 100% accuracy from the confusion matrices indicates the initial success of our sensor design in differentiating conventional targets as well as the OACTs with the new DMDSM sensor.
Elevation control of a soft jumping robot

Jumping with controllable elevation is significant for insect-scale robots to improve terrain adaptability and to escape from risks. However, jumping robots based on soft materials with low stiffness cannot transmit displacement precisely, exhibiting poor control of jumping. Here, we propose a modified two-bars catapult mechanism combined with an asynchronous sequential releasing strategy to realize elevation controllable jumping. In this work, an 80 mg prototype robot, 56 (long) × 29 mm (wide) × 3 mm (high) mm in size, is designed with the controllable elevation range from 63° to 112°. The soft robot is mainly composed of a shape memory alloy actuator and four electrostatic pads acting as the lock/release structures. Elevation control is realized by asynchronously releasing the electrostatic pads in a small time interval (about 10 ms). A maximum jump height of 62 mm and a maximum half-distance of 41 mm are also achieved.
Leveraging Enhanced Virtual Reality Methods and Environments for Efficient, Intuitive, and Immersive Teleoperation of Robots

Many studies have focused on Virtual Reality (VR) frameworks for remotely controlling robotic systems. Although VR systems have been used to teleoperate robots in simple scenarios, their effectiveness in terms of accuracy, speed, and usability has not been rigorously evaluated for complex tasks that require accurate trajectories. In this work, an Enhanced Virtual Reality (EVR) framework for robotic teleoperation is evaluated to assess if it can be efficiently used in complex tasks that require accurate control of the robotic end-effector. The environment and the employed robot are captured using RGB-D cameras, while the remote user controls the motion of the robot with VR controllers. The captured data are transmitted and reconstructed in 3D so as to allow the remote user to monitor the task execution progress in real time, using a VR headset. The EVR system is compared with two other interface alternatives: i) teleoperation in pure VR (the model of the robot is rendered with respect to its real joint states), and ii) teleoperation in EVRR (the model of the robot is superimposed on the real robot). The results show that pure point cloud interfaces suffer from visualization issues, reducing the effectiveness of the robot teleoperation. However, the accuracy and user experience can be greatly improved by including the robot model.
Globally Optimal Online Redundancy Resolution for Serial 7-DOF Kinematics Along SE(3) Trajectories

Redundant robots offer the possibility of improving agility, compared to their non-redundant counterparts, by exploiting the additional kinematic DOFs to increase a measure called manipulability. While it is common to maximize the manipulability measure during redundancy resolution locally, global optimization of a full trajectory is usually computationally too expensive and thus only considered for offline procedures in current literature. However, local maximization is prone to be sub-optimal and at times even fails at preserving agility of a robot that ought to be reactive. In this work we build upon our previous contributions on online trajectory generation on SE(3) and closed-form task space manipulability of a 7-DOF serial robot, and combine it with graph search techniques for global optimization. This enables, for the first time, online trajectory generation with globally optimal redundancy resolution regarding manipulability, to maintain agility in reactive robot behavior.
Self-Sustainable Dynamic Tariff for Real Time Pricing-Based Demand Response: A Brazilian Case Study

The operation of power systems under peak demand conditions results in higher operational costs, more instability risks and often forces an excessive use of fossil fuels. In this context, this paper proposes a self-sustainable real-time pricing (RTP) tariff that is adjusted proportionally to the aggregated demand of a group of consumers, and is based on a changing rate for each hour of the day. Therefore, demand peaks are expected to be naturally reduced by the group due to the higher tariff values imposed during these operating conditions. In addition, consumers benefit from the opportunity to reduce their bills according to the tariff and can carry out financial strategies by setting up their own consumption profile. The proposed dynamic tariff has a self-sustainable and revenue neutral nature which protects both consumers and distribution power companies from being economically affected by varying price elasticity scenarios. This is achieved by mathematically guaranteeing for such companies a revenue equal to the one they would expect with the traditional application of flat tariff. The advantages of the proposal are highlighted through numerical simulations by comparing it with the so-called Conventional Tariff and White Tariff, which are currently adopted by residential consumers in Brazil.
Learning Shape Control of Elastoplastic Deformable Linear Objects

Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need for large amounts of data that can only be satisfied in simulation. This paper proposes a new shape control task for Deformable Linear Objects (DLOs). More notably, we present the first study on the effects of elastoplastic properties on this type of problem. Objects with elastoplasticity such as metal wires, are found in various applications and are challenging to manipulate due to their nonlinear behavior. We first highlight the challenges of solving such a manipulation task from an RL perspective, particularly in defining the reward. Then, based on concepts from differential geometry, we propose an intrinsic shape representation using discrete curvature and torsion. Finally, we show through an empirical study that in order to successfully solve the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward needs to include intrinsic information about the shape of the DLO.
A Soil Yeast Count Monitor for Plant Growing Applications

A soil yeast count monitor that includes a soil yeast sensor and converter was developed for plant growing applications. The monitor can linearly transform soil yeast count into a frequency output. Moreover, the monitor’s adjustable sensitivity control circuits can increase sensitivity when low-nutrient soil is used. During measurements, the input voltage of the proposed converter was 0 to 2.2 V, and the sensitivity was controllably adjusted by 2.217, 2.952, 3.651, 4.388, and 5.118 MHz/V. The range of soil yeast count was 0 to <inline-formula> <tex-math notation="LaTeX">$7\times10$ </tex-math></inline-formula><sup>8</sup> colony forming units (CFU), and the adjustable sensitivities were 0.654, 0.871, 1.091, 1.311, and 1.529 MHz/10<sup>8</sup> CFU. The maximum linear error of the complete system was −0.66%, and the chip size was <inline-formula> <tex-math notation="LaTeX">$1.49\times1.49$ </tex-math></inline-formula> mm<sup>2</sup>. The system was experimentally tested on two types of soil: fluvisol and paddy soil. Through measurements and experiments, the soil yeast count monitor was proven functionally.
An Optimized Transition Structure for Solving Reference Ground Discontinuities in SIW Filter Integration

Substrate integrated waveguides (SIW) have been widely used in the design of high-performance narrowband filters and the integration of microwave circuits in recent years, owing to their high quality factor and easy planarization. In the design of single-board integration, in order to achieve high quality factor and narrow-band SIW filter, a low-cost and effective method is to increase the thickness of the substrate layer within a certain range to achieve lower insertion losses. However, with the switching of the reference ground planes, a discontinuous return path will be induced. Improper handling of the structure will give rise to additional losses and radiation which will lead to EMC issues. In this paper, a method is proposed to extend the inner ground at the discontinuous section to form a stepped transition structure to achieve good signal transmission performance. The simulation results show that at 10GHz, the center frequency of SIW in our design, the proposed transition structure has 1.8dB lower insertion loss as well as 40.14dB higher return loss than the structure of direct interconnection at the discontinuous section.
A Critical Assessment of Open-Loop Active Gate Drivers Under Variable Operating Conditions

Active gate drivers have been investigated in power circuits to reduce unwanted over-voltages and over-currents, whilst keeping the transients fast. Indeed, the use of such a kind of driver avoids the triggering of oscillations related to high frequency parasitic resonant circuits, which affect adversely the electro-magnetic interference delivered by power modules. However, in the case of fast power switches, the driver is working in an open-loop manner, and the modulation pattern is fixed. This paper assesses the effects of different operating conditions on the switching waveforms of an AGD-driven power transistor. More precisely, load current, input voltage and temperature variations were investigated on an open-loop active gate driver comprised in a Buck converter. Experimental results suggest that the AGD is no longer effective in damping the unwanted oscillations under a significant change of the operating conditions.
Lifelong Localization in Semi-Dynamic Environment

Mapping and localization in non-static environments are fundamental problems in robotics. Most of previous methods mainly focus on static and highly dynamic objects in the environment, which may suffer from localization failure in semi-dynamic scenarios without considering objects with lower dynamics, such as parked cars and stopped pedestrians. In this paper, we introduce semantic mapping and lifelong localization approaches to recognize semi-dynamic objects in non-static environments. We also propose a generic framework that can integrate mainstream object detection algorithms with mapping and localization algorithms. The mapping method combines an object detection algorithm and a SLAM algorithm to detect semi-dynamic objects and constructs a semantic map that only contains semi-dynamic objects in the environment. During navigation, the localization method can classify observation corresponding to static and non-static objects respectively and evaluate whether those semi-dynamic objects have moved, to reduce the weight of invalid observation and localization fluctuation. Real-world experiments show that the proposed method can improve the localization accuracy of mobile robots in non-static scenarios.
THz s-SNOM Imaging Of Logarithmic Spiral Antennas

Probing the near-field of photonic components at sub-wavelength scale is of high interest to improve the design of future THz devices. We report on the THz near-field imaging of logarithmic spiral antennas by scattering-type scanning near-field optical microscopy (s-SNOM). Our results demonstrate that the s-SNOM technique allows to access the electric field distribution on the arms of an antenna and reveals non-trivial features at the center of the component.
Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.
Kinetostatics for variable cross-section continuum manipulators

Continuum manipulators have shown a wide range of applications due to their inherent compliance and dexterity. At present, the cross-sectional dimension of these manipulators is often kept constant, which facilitates the design, fabrication and modeling processes. The famous piecewise-constant-curvature (PCC) assumption is widely used in the kinematics and motion control. By contrast, most natural creatures that have continuum structures usually exhibit variable cross sections, such as the elephant trunk and the octopus tentacle. Inspired by these creatures, variable cross-section continuum manipulators have been proposed in literature. For variable cross-section, the PCC assumption is no longer valid. In fact, the manipulator’s kinematics and statics are coupled, which should be considered and solved together. This paper establishes the general kinetostatic model of variable cross-section continuum manipulators, and performs experimental validations under various actuating and loading situations. Results showed that the proposed model has satisfactory accuracy in predicting the manipulator profile under various actuating and loading situations. The average root-mean-square error of the proposed model was 1.67 mm, and even the maximum error did not exceed 2.6 mm, according to all the 51 groups of tests on three 100 mm long prototypes. Furthermore, results also indicated that Type-1 manipulator (only varying the spacer dimension) has larger deflections and tends to be "softer" than Type-2 manipulator (only varying the backbone cross-sectional area). Results of this paper could be helpful for the design and analysis of variable cross-section continuum manipulators.
Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

SAR images contain a large amount of noise, and related algorithms will cause high complexity when increasing the accuracy. To overcome this problem, a neural network model based on the attention mechanism was proposed in this paper. The model extracted information in two stages. It gradually extracts high-level features by reducing noise first and then adding hybrid attention. First, use dual-channel one-dimensional convolution to reconstruct the residual shrinkage network to construct a lightweight and efficient feature module, which improved the information extraction of the module with the consumption of a small amount of computing resources. Then, it was used as the backbone for model construction. Subsequently, mixed adaptive pooling was adopted to improve the maximum pooling. After that, dimensionality was reduced by pooling and linear interpolation was used to increase dimensionality, so as to generate feature weights of mixed dimension. Tests were performed on MSTAR dataset. The results showed that compared with the advanced algorithms, the proposed model in this paper can greatly reduce the amount of parameters and complexity while ensuring accuracy. The robustness test demonstrated that the model can effectively identify images with noise being added.
Model based evaluation of human and lower-limb exoskeleton interaction during sit to stand motion

The interaction between an exoskeleton and its human user is complex, and needs to conform to various requirements related to safety, comfort and adaptability. It is however impractical to test a large number of prototype variations against a large number of user variations, especially in the initial design and testing phases. Model based methods can help at this design stage by providing a virtual testbed. In this study, we develop a MATLAB-based toolbox that can simulate the interaction between a human model and a lower limb exoskeleton during the sit to stand motion. We present results for different scales of human users as well as variation in levels of exoskeleton assistance. Our results show that large reductions in human joint torques upto 57Nm are possible, while also transmitting large forces upto 300N to the human model. Additionally, by varying the human body sizes by 15% we found that the interaction forces also changed by as much as 29.2%. Therefore, careful consideration of the human user and its limitation should be made in the exoskeleton design and concept phases.
DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise

Exploration in environments with sparse rewards remains a challenging problem in Deep Reinforcement Learning (DRL). For the off-policy method, it usually needs a large number of training samples. With the growing dimensions of state and action space, this method becomes more and more sample-inefficient. In this paper, we propose a novel fast exploration method for off-policy reinforcement learning, called Density-based Intrinsic Motivation and Self-adaptive Action Noise (DIMSAN). Our main contribution is twofold: (1) We propose a Density-based Intrinsic Motivation (DIM) method. It introduces a new intrinsic-reward generation mechanism based on samples’ density estimation during experience replay and encourages the agent to seek novel and unfamiliar states. (2) We propose a Self-adaptive Action Noise (SAN) to deal with the exploration-exploitation tradeoffs, which could automatically change the exploration step through adding adaptive action space noise. The synergy between DIM and SAN could guide the agent to search the state and action space with high efficiency. We evaluate our method on the benchmark manipulation tasks and the designed challenging ones. Empirical results show that our method outperforms the existing methods in terms of convergence speed and sample efficiency, especially in challenging tasks.
Super-Resolution Power of Deep Unfolding Networks to TomoSAR 3D Imaging

Deep unfolding networks have been proved to be able to achieve super-resolution (SR). Different from sparsity regularization methods, deep unfolding networks can speed up the imaging process and own good robustness against noise. However, the optimal network design and maximum SR ability of deep unfolding networks have not been quantified. In this paper, the optimal number of layers and the SR extremity of the network are confirmed by simulation. And some sparsity regularization methods are compared with the proposed method. Simulation results validate the superiority of the proposed method in terms of SR imaging and accuracy.
Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments

Obstacle avoidance for robotic manipulators can be challenging when they operate in unstructured environments. This problem is probed with the sim-to-real (sim2real) deep reinforcement learning, such that a moving policy of the robotic arm is learnt in a simulator and then adapted to the real world. However, the problem of sim2real adaptation is notoriously difficult. To this end, this work proposes (1) a unified representation of obstacles and targets to capture the underlying dynamics of the environment while allowing generalization to unseen goals and (2) a flexible end-to-end model combining the unified representation with the deep reinforcement learning control module that can be trained by interacting with the environment. Such a representation is agnostic to the shape and appearance of the underlying objects, which simplifies and unifies the scene representation in both simulated and real worlds. We implement this idea with a vision-based actor-critic framework by devising a bounding box predictor module. The predictor estimates the 3D bounding boxes of obstacles and targets from the RGB-D input. The features extracted by the predictor are fed into the policy network, and all the modules are jointly trained. This makes the policy learn object-aware scene representation, which leads to a data-efficient learning of the obstacle avoidance policy. Our experiments in simulated environment and the real-world show that the end-to-end model of the unified representation achieves better sim2real adaption and scene generalization than state-of-the-art techniques.
Top management support in the implementation of Industry 4.0 and business digitization: the case of companies in the main European stock indices

Top management support is one of the key aspects in the implementation of Industry 4.0 and business digitization, whereas communication is essential to aligning and driving an organization in this task. However, to date, no research has addressed this support from this perspective. To fill this gap, we take a sample of companies (N=159) listed on the 5 main stock indices in the European Union, and we qualitatively examine the letters issued by chief executive officers and other top managers in their annual reports. We assess whether, to which extent and under which motivations and barriers, the senior management of these companies are supporting this implementation through their discourse. Among other relevant findings, our results show that barely half of the managers make explicit their strong support for this implementation, as a kind of widespread spatial myopia that serves as a meta-barrier in this implementation. In addition, the support given by top executives is mostly driven by business and customer motivations; in turn, they generally fail to present sustainability within their main motivations. Overall, this paper offers a series of relevant contributions and insights with managerial implications taken from a novel approach in this field.