Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Learning for Safe Highway Driving based on Two-Player Zero-Sum Game

In this paper, we set up a two-player-zero-sum Markov game (TZMG) framework to train a safe driving policy network so that the worst intentions of the neighbor vehicles can be considered. Compared to the conventional policy learning frameworks, the TZMG framework can embed the adversary from the neighbor vehicle throughout its training process. Furthermore, a novel TZMG Q-learning algorithm based on the Wolpertinger policy is proposed to be scalable to multiple adversarial neighbor vehicles. Finally, simulations and humansin-the-loop experiments are conducted to verify the effectiveness of the TZMG framework and novel algorithm. Compared to the benchmarking safety controllers in the literature, our proposed novel TZMG algorithm can achieve a much lower collision rate when dealing with adversarial neighbor vehicles.
Attacker-Resilient Adaptive Path Following of a Quadrotor with Dynamic Path-Dependent Constraints

For most works on constrained motion control in the literature, only constant or time-varying constraints are discussed, which are often conservative and cannot adapt to the dynamically changing operation environment. In this work, in the context of quadrotor operations, we propose a new adaptive path following architecture with dynamic path-dependent constraints, in which the desired path coordinate, desired path speed, and constraint requirements not only depend on a path parameter associated with the desired path, but also can adapt to the presence of an "attacker" nearby. A new concept of "composite barrier function" has been proposed to address both safety and performance constraints in a unified structure. Adaptive laws are introduced to estimate the upper bounds of system uncertainties and unknown "attacker" velocity. Exponential convergence into a small neighborhood around the equilibrium for position tracking error can be guaranteed. In the end, a simulation example further demonstrates the effectiveness of the proposed architecture.
RoSS: Rotation-induced Aliasing for Audio Source Separation

This paper considers the problem of audio source separation, where the goal is to isolate a target audio signal (say Alice's speech) from a mixture of multiple interfering signals (e.g., when many people are talking). This problem has gained renewed interest mainly due to the significant growth in voice-controlled devices, including robots in homes, offices, and other public facilities. Although a rich body of work exists on the core topic of source separation, we find that rotational motion of the microphones (e.g., a swiveling robot-head) offers complementary gains. We show that rotating the microphone array to the optimal orientation can produce desirable “delay aliasing” between two interferers, causing the two interferers to appear as one. In general, a mixture of K signals becomes a mixture of (K - 1) signals, a mathematically concrete gain. We show that the gain translates well to practice, provided two rotation-related challenges can be mitigated. This paper is focused on mitigating these challenges and demonstrating the end-to-end performance on a fully functional prototype. We believe that our Rotational Source Separation (RoSS) module could be plugged into actual robot heads or into other devices (like Amazon Show) that are also capable of rotation.
Sensor Localization by Few Distance Measurements via the Intersection of Implicit Manifolds

We present a general approach for determining the unknown (or uncertain) position and orientation of a sensor mounted on a robot in a known environment, using only a few distance measurements (between 2 to 6 typically), which is advantageous, among others, in sensor cost, and storage and information-communication resources. In-between the measurements, the robot can perform predetermined local motions in its workspace, which are useful for narrowing down the candidate poses of the sensor. We demonstrate our approach for planar workspaces, and show that, under mild transversality assumptions, already two measurements are sufficient to reduce the set of possible poses to a set of curves (one-dimensional objects) in the three-dimensional configuration space of the sensor $\mathbb{R}^{2}\times \mathbb{S}^{1},$ and three or more measurements reduce the set of possible poses to a finite collection of points. However, analytically computing these potential poses for non-trivial intermediate motions between measurements raises substantial hardships and thus we resort to numerical approximation. We reduce the localization problem to a carefully tailored procedure of intersecting two or more implicitly defined two-manifolds, which we carry out to any desired accuracy, proving guarantees on the quality of the approximation. We demonstrate the real-time effectiveness of our method even at high accuracy on various scenarios and different allowable intermediate motions. We also present experiments with a physical robot. Our open-source software and supplementary materials are available at https://bitbucket.org/taucgl/vb-fdml-public.
Groupes triangulaires lagrangiens en géométrie hyperbolique complexe

— There is still much to learn about discrete subgroups of PU(n,1), the group of holomorphic isometries of the complex hyperbolic n-space. Given a finitely generated group G, describing the discrete and faithful representation of G into PU(n,1) is a difficult task, which has been carried out in only few cases. In this note we expose some results about Lagrangian triangle groups in the frame of PU(2,1). These groups are generated by three antiholomorphic isometric involutions. They are connected to many of the known examples of discrete subgroups of PU(2,1). The main result stated here is the existence of a one parameter family of embeddings of the Teichmueller space of the once punctured torus into the PU(2,1)-representation variety of the free group of rank two. These embeddings are described using Lagrangian triangle groups.
DEdgeNet: Extrinsic Calibration of Camera and LiDAR with Depth-discontinuous Edges

This paper addresses the problem of calibrating extrinsic parameter matrix between an RGB camera and a LiDAR. Multimodal sensing systems are essential for fully autonomous navigation platforms. A key pre-requisite for such a system is calibration between different sensors. As the two most widely equipped sensors, calibration between RGB cameras and LiDARs remains challenging. Existing methods address this problem without using explicit geometric priors. In this paper, we propose a novel real-time network that utilizes depth-discontinuous edges extracted from a single image to calibrate cameras and LiDARs. Our network consists of two key components: (1) a self-supervised edge extraction network named DEdgeNet, which detects depth-discontinuous edges from a single image and extracts corresponding features; (2) prediction of the extrinsic parameter matrix between the camera and the LiDAR by matching fixed features in RGB images and updating depth features in a coarse-to-fine frame. Specifically, considering that edges are rich and common in natural scenes, DEdgeNet simplifies RGB image encoding and extracts fixed edges for feature matching. We conducted extensive experiments on the KITTI-odometry dataset. The results show that our method achieves an average rotation error of 0.028° and an average translation error of 0.247 cm, which demonstrates the superiority of our method.
Yaw-Augmented Control for Wind Farm Power Tracking

This paper presents a wind farm control strategy to use wake steering (yaw control) to augment pitch control for tracking a power reference signal. The outer loop controller employs a recently proposed dynamic yaw model with a time-varying graph structure that accounts for changes in the farm power output due to the propagation of wakes and wake interactions from yawing turbines. The inner pitch control loop uses a PI controller combined with a novel power-sharing arrangement that reduces the needed derate at each turbine. A compensation scheme accounts for the slow timescale effects of the yaw control actions within the faster timescale pitch control. The controller is applied to track two power trajectories (typical of secondary frequency regulation signals) using a large-eddy simulation wind farm plant. The results demonstrate that the additional control authority from yaw provides some added benefit in reducing the required turbine derates needed for wind farms to track transient power increases in the proposed setting. However, the benefit decreases and pitch control alone is sufficient when the turbines are derated beyond a certain level. These findings suggest that augmenting pitch control with yaw may provide financial incentives in terms of allowing wind farms to maximize power supply to the bulk power market while still providing regulation services. Further work is needed to analyze the costs and benefits of the additional control complexity versus bandwidth in augmenting pitch control with wake steering in these applications.
Learning to Predict Action Feasibility for Task and Motion Planning in 3D Environments

In Task and motion planning (TAMP), symbolic search is combined with continuous geometric planning. A task planner finds an action sequence while a motion planner checks its feasibility and plans the corresponding sequence of motions. However, due to the high combinatorial complexity of discrete search, the number of calls to the geometric planner can be very large. Previous works [1] [2] leverage learning methods to efficiently predict the feasibility of actions, much like humans do, on tabletop scenarios. This way, the time spent on motion planning can be greatly reduced. In this work, we generalize these methods to 3D environments, thus covering the whole workspace of the robot. We propose an efficient method for 3D scene representation, along with a deep neural network capable of predicting the probability of feasibility of an action. We develop a simple TAMP algorithm that integrates the trained classifier, and demonstrate the performance gain of using our approach on multiple problem domains. On complex problems, our method can reduce the time spent on geometric planning by up to 90%.
Contact Based Turning Gait of a Novel Legged-Wheeled Quadruped

How does a wheeled robot move and turn? The answer is straightforward for a conventional wheeled robot, but it is not so easy for a robot with a discrete wheel design. Regular wheeled robots always have four contact points, resulting in static stability during locomotion. However, QuadRunner's novel leg mechanism provides only a semi-circular wheel shape, and proper gait planning is needed to go straight or turn. Therefore, this paper presents a dual frequency gait planning method which controls the robot's gait cycle's duty factor and generates unique turning gait patterns for wheel locomotion. Describing requirements and limitations, we found sets of solutions that can achieve turning. Results show that the smallest turning radius QuadRunner achieved is 1.05m, and the biggest is 1.86m. In addition, detailed experiments were made to observe the performance and stability of straight and turning wheel behaviors. Finally, a gait verification is made using high-speed cameras.
CA-YOLO: Model Optimization for Remote Sensing Image Object Detection

The CA-YOLO (Coordinate Attention-YOLO) model has been optimized for object detection in complex remote sensing images, addressing key issues faced by algorithms that detect multiple objects. These issues include weak multi-scale feature learning capabilities and the challenging trade-off between detection accuracy and model parameter complexity. The CA-YOLO model, built on the framework of YOLOv5, incorporates a lightweight coordinate attention module in the shallow layer to improve detailed feature extraction and suppress redundant information interference. Additionally, a spatial pyramid pooling-fast with a tandem construction module is implemented in the deeper layer. The model employs a stochastic pooling strategy to fuse multi-scale key feature information from low-level to high-level layers, reducing the number of model parameters while improving inference speed. We optimized the anchor box mechanism and modified loss function to improve the ability of the model to detect objects of different sizes and scales. Results show that the CA-YOLO model outperforms the original YOLO in terms of multi-object detection accuracy, with an average mAP@0.5 accuracy improvement of 4.8% and mAP@0.5:0.95 accuracy improvement of 3.8%. Additionally, the CA-YOLO model demonstrates exceptional inference speed, averaging 125 fps, which reinforces its superiority in detection accuracy, generalization ability, and overall efficiency. Notably, these improvements were achieved while maintaining the same number of parameters and complexity as other models, making the CA-YOLO model an exceptional choice for various applications.
Mental Health Prediction from Social Media Text Using Mixture of Experts

Predicting mental health statuses from social media text is a well-known Natural Language Processing (NLP) task. In this work, we focus on the issue of depression and anxiety disorder prediction from Twitter by comparing a more conventional approach based on engineered features with a data-oriented alternative based on mixture of specialists with transformer language models. Results from a large corpus of depression/anxiety self-disclosed diagnoses in the Portuguese language are reported, and a feature importance analysis is carried out to provide further insights into these tasks.
Validity and Reliability of Extended Technology Acceptance Model for Digital Signage Augmented Roadshow (DiSAR)

The purpose of this study was to examine the validity and reliability of the extended Technology Acceptance Model (extended TAM) questionnaire in assessing the user acceptance of DiSAR (Digital Signage Augmented Roadshow). The validity and reliability of all 26 items in the questionnaire are assessed through the use of PLS-SEM analysis. This study also serves as an exploratory study on applying PLS-SEM on the pilot study data. The result shows that the scale exhibits adequate reliability and convergent validity but having diminished discriminant validity.
Towards Safe Remote Manipulation: User Command Adjustment based on Risk Prediction for Dynamic Obstacles

Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control ability or unexpected situations. In this paper, we propose a risk-aware user command adjustment method to avoid potential collision with dynamic obstacles. Our method consists of a network that predicts the risk of dynamic obstacles and another network that synthesizes commands to avoid obstacles. Based on the predicted risk, our method decides an adjusted command between a user command and a command to avoid collisions. We evaluate our method in problems that face collisions with dynamic obstacles when following given commands and in problems with static obstacles. We show that our method improves safety against the risk of dynamic obstacles or follows user commands when there is no risk. We also demonstrate the feasibility of our method using the real fetch manipulator with seven-degrees-of-freedom.
Fish Growth Tracking and Mortality Monitoring: Control Design and Comparisons

Monitoring the water quality and controlling the feeding are essential functions in balancing fish productivity and shaping fish’s life history in the fish growth process. Currently, most fish-feeding processes are conducted manually in different phases and are not optimized. The feeding technique influences fish growth through the feed conversion rate. In addition, the high concentration level of ammonia affects the water quality, resulting in fish survival and mass death. Therefore, there is a crucial need to develop control strategies to determine optimal, efficient, and reliable feeding processes and monitor water quality simultaneously. In this paper, we revisit the representative fish growth model describing the total biomass change by incorporating the fish population density and mortality. We specifically focus on relative feeding as a manipulated variable to design traditional and optimal control to track the desired weight reference within the suboptimal temperature and Dissolved Oxygen (DO) profiles under different levels of unionized ammonia (UIA) exposures. Then, we propose an optimal algorithm that optimizes the feeding and water quality of the dynamic fish population growth process. We also show that the model predictive control decreases fish mortality and reduces food consumption in all different cases by an average of 26.9% compared to the bang-bang controller, 22.6% compared to the PID controller.
Contact Force Control with Continuously Compliant Robotic Legs

This paper presents a novel robotic leg design and an associated control approach, which aims at providing an extension to the classical series elastic actuation concept. We propose to directly integrate the series compliance into the structure of the robotic leg itself, as opposed to co-locating spring and motor as done in traditional series elastic actuators. Our approach will eliminate mechanical design complexity and lead to a reduction of mass in the legs. This will, as a secondary benefit, improve the energy efficiency of locomotion. The primary contribution of this work is a model-based controller that can stably and precisely regulate the ground contact forces during stance. This control approach is demonstrated in a set of test-bench experiments, in which we control the contact forces of a modified version of the robotic leg ScarlETH. Here, the rigid shank is replaced by a continuously compliant element made of spring steel. This work presents the first step towards a new generation of robotic legs with structural compliance.
Switched Combination Synchronization of Nonidentical Fractional-Order Chaotic Systems using Neuro-Fuzzy Sliding Mode Control

This work presents a novel chaos synchronization scheme for non-identical fractional-order chaotic systems. The new scheme, called switched combination synchronization, integrates the concepts of switched synchronization and combination synchronization in a unified framework where two drive systems, called base systems, are swapped using a switching factor and are combined with a third drive system, called the masking system. A response system is synchronized with the resulting multi-drive systems using a neuro-fuzzy sliding mode control (NFSMC) approach. A soft switching algorithm using the neuro-fuzzy switch is designed to ensure the stability of the system during the transition of drive systems. Simulation results confirm the higher performance of the proposed control approach, compared to a regular sliding mode control (SMC) and a proportional-integral-derivative (PID) controller, in switched combination synchronization of chaotic systems.
Fast Extrinsic Calibration for Multiple Inertial Measurement Units in Visual-Inertial System

In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the number of inertial sensors. Based on the assumption that extrinsic parameters between inertial sensors are perfectly calibrated, the fusion algorithm provides better localization accuracy with more IMUs, while neglecting the effect of extrinsic calibration error. Our method builds two non-linear least-squares problems to estimate the MIMU relative position and orientation separately, independent of external sensors and inertial noises online estimation. Then we give the general form of the virtual IMU (VIMU) method and propose its propagation on manifold. We perform our method on datasets, our self-made sensor board, and board with different IMUs, validating the superiority of our method over competing methods concerning speed, accuracy, and robustness. In the simulation experiment, we show that only fusing two IMUs with our calibration method to predict motion can rival nine IMUs. Real-world experiments demonstrate better localization accuracy of the VIO integrated with our calibration method and VIMU propagation on manifold.
On Private Server Implementations and Data Visualization for LoRaWAN

The paper titled “On Private Server Implementations and Data Visualization for LoRaWAN” explores the implementation of private LoRaWAN servers for secure and scalable data transmission in Internet of Things (IoT) applications. The authors present a detailed analysis of the existing server implementations and propose a novel architecture for private server deployment. The paper also addresses the challenges associated with data visualization in LoRaWAN networks and presents a comprehensive solution for efficient data representation using advanced visualization techniques. The proposed implementation is evaluated through experiments and simulations, and the results demonstrate the feasibility and effectiveness of the proposed architecture for secure and scalable IoT applications. The paper concludes with a discussion on the future directions for research in this area, highlighting the need for further investigation on the scalability and security of LoRaWAN networks.
Hyperspectral Target Detection via Spectral Aggregation and Separation Network With Target Band Random Mask

Hyperspectral target detection (HTD) is a pixel-wise detection method based on limited prior targets and spectral differences, which has been widely studied and applied in many fields. Recently, deep learning (DL) plays an important role in hyperspectral imagery (HSI) processing. However, for HTD, the severe lack of class-balanced training sets is an enormous challenge. Meanwhile, it is difficult to suppress backgrounds while highlighting targets through the deep network. To address these issues, we propose a spectral aggregation and separation network (SASN) with a target band random mask (TBRM) for HTD in this article. For the training sets of SASN, a multifarious representative background selection strategy (MRBS) is first proposed to obtain a multifarious and representative background training set. Next, aiming at the notorious class imbalance, a data augmentation (DA) method, TBRM, is proposed to generate adequate target training set by repeating randomly zero-masking the spectral bands of a prior target. Subsequently, in the training of SASN, residual connection and squeeze-and-excitation (SE) channel attention mechanism are applied to fully extract high discriminative features and nonlinear ones in the spectra. Besides, to better separate the targets and backgrounds, a triplet-soft loss function is presented, which makes the training in the direction of spectral separation of background samples from both the prior target and target samples. During testing, the trained SASN distinguishes the spectral similarities and differences simultaneously for highlighting targets and suppressing backgrounds. Moreover, extensive experimental results validate that the proposed method has superior detection performances, background suppression capacity, and separability compared with ten cutting-edge HTD algorithms on six benchmark HSI datasets.