Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Localization for Closed-Loop Control of Assistive Furniture

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligent coordination of this system, a key component is the localization of each mobile furniture. The challenge is to assess the state of an arbitrary living scenario so that the estimation can be used as a real-time feedback signal for autonomous closed-loop control of mobile furniture. We propose a perception pipeline that addresses these challenges. A machine learning model is designed and trained to jointly achieve multi-object semantic keypoint detection and classification in camera images. The synthetic data generation is employed to augment the training set and boost the model performance. A robust point cloud registration uses the detected semantic keypoints and depth information to estimate poses of the furniture. Tracking is applied to achieve smooth estimation. A high-performance accelerator that optimizes the efficiency of using heterogeneous devices is applied to achieve real-time performance. This visual perception pipeline is used in closed-loop control to steer the mobile furniture from initial to a desired location demonstrated in experiments on real hardware.
Distributed Model Predictive Formation Control with Gait Synchronization for Multiple Quadruped Robots

In this paper, we present a fully distributed framework for multiple quadruped robots in environments with obstacles. Our approach utilizes Model Predictive Control (MPC) and multi-robot consensus protocol to obtain the distributed control law. It ensures that all the robots are able to avoid obstacles, navigate to the desired positions, and meanwhile synchronize the gaits. In particular, via MPC and consensus, the robots compute the optimal trajectory and the contact profile of the legs. Then an MPC-based locomotion controller is implemented to achieve the gait, stabilize the locomotion and track the desired trajectory. We present experiments in simulation and with three real quadruped robots in an environment with a static obstacle.
Swarm Robotics Search and Rescue: A Bee-Inspired Swarm Cooperation Approach without Information Exchange

Swarm robotics plays a non-negligible role in actual practice because of its scalability and robustness. Besides some specific studies, there is still a lack of overall approaches to solving the search and rescue problem in a communication-denied environment. This paper presents a bee-inspired swarm cooperation approach without information exchange, including a target grouping method suitable for multi-objective and multi-robot, a finite behavior state machine, and the corresponding control law. Finally, the effectiveness of the proposed approach is shown via simulation. The overall approach proposed in this paper does not require two-way information exchange, and it is robust against relative and own position errors, making swarm robotics search and rescue in a communication-denied environment possible.
KRIS: A Novel Device for Kinesthetic Corrective Feedback during Robot Motion

This paper presents a novel device that can be used to perform kinesthetic corrective feedback for robotic systems. KRIS (Kinesthetic Robotic Interaction System) is a device that can be mounted on the end-effector of an articulated robot. From here it can be manipulated by a human to give corrective feedback to the robot system during execution and in an intuitive way. The device can provide feedback in six degrees of freedom while giving passive haptic feedback to the user about both the position, rotation, and movement of the robot. We evaluated KRIS in a user study with respect to a baseline based on keyboard feedback in the areas of usability, intuitiveness, accuracy of corrections, and user task load. KRIS outperformed our baseline on the first three metrics and performed similar on task load. We believe that KRIS can enable a wide variety of robots to be taught interactively by non-expert humans in diverse collaborative settings.
Integrating Multimedia Learning Principle into the Design of an Interactive Video for Low Achievers in Introductory Programming

This paper discusses the design and development of an interactive video for programming fundamentals for low-achieving students. The aim is to design an interactive multimedia learning tool that is more impactful toward the low achievers’ learning requirements. Two main phases are involved in this study, where the first phase includes the selection of multimedia learning principles and usability recommendations based on Nielsen’s design guidelines. The segmenting principle is then selected and integrated into the design of the interactive video. The next phase requires an iterative design phase, which involves the design, development of the prototype, and review activities. A storyboard was designed based on the selected principle, then developed into a high-fidelity prototype. A qualitative approach through focus group discussion involving a user-centered design (UCD) session was conducted with 12 first-year students from the Diploma of Computer Science program who are also low achievers in programming to review and rate the design of the interactive video. The scales of the usability recommendations are related to visual design, content design, navigation, interaction, and multimedia design. Results from the UCD session show that all participants agreed with the usability recommendations and segmenting principle integrated into the interactive video.
Demand Response Strategy Considering User Satisfaction Based on NILM Technology

Aiming at the problems of the diversity of power grid residents' loads and the satisfaction of the user side in the process of demand response (DR), combining the DR model and the idea of data mining, proposes a DR model based on Non-Intrusive Load Monitoring (NILM) technology, which considers user satisfaction with electricity. The simulation results show that compared with the traditional DR model, this model can minimize the impact of DR on residential users' power consumption comfort, reduce the cost of power consumption for users while enhancing their satisfaction with power consumption, and make the load power curve more stable, which is conducive to the safe and stable operation of the power grid. Through the in-depth mining of historical power consumption data of users using NILM technology, the basis for the improvement of DR is provided, which is conducive to continuously improving the implementation effect of DR and giving full play to the important role of DR in the safe and stable operation of the power grid.
Improved ADMM Algorithm with Variable Fixing for the Hydrothermal Unit Commitment Problem

This study proposes an improved ADMM algorithm with variable fixing for the hydrothermal unit commitment problem. The efficiency of this method and two other ADMM-based methods were tested on three datasets. The empirical results demonstrate that ADMM-based algorithms outperform traditional methods in terms of problem-solving time. Moreover, the improved ADMM algorithm proposed in this study also ensures the quality of the solution.
RoboSC: a domain-specific language for supervisory controller synthesis of ROS applications

The paper presents a novel domain-specific language, RoboSC, for developing supervisory controllers for robotic applications. RoboSC supports concepts of ROS/ROS2 and supervisory control theory. It enables users to focus on the modeling and the synthesis process of supervisory controllers for ROS applications only because it generates all artifacts needed to connect such controllers to ROS applications and deploy them. Validation tests with actual and simulated robots show the approach's feasibility and indicate reduced coding effort.
An Optimization Method for Satellite Data Structure Design Based on Improved Ant Colony Algorithm

The telemetry data structure is the embodiment of satellite telemetry format, the rationality and correctness of which determine the satellite telemetry capacity as well as transmission capability. Conventional satellite telemetry data structure designs excessively depend on manual experience, easily leading to problems such as waste of satellite resources, low telemetry transmission efficiency and unintuitive ground decoding. In this paper, a novel method was proposed to optimize the design of satellite telemetry data structure, based on rasterized modeling to visualize the design constraints, and an improved ant colony algorithm with grey relational analysis to optimize the telemetry data structure. The method is not only effective in preventing the deficiencies of conventional approaches, but also beneficial to the rational allocation of telemetry resources. The feasibility was verified by the telemetry data of a satellite with DFH-4 platform, and the results showed favorable convergence, along with valid improvement of the design process efficiency and application effect.
Incentivizing Local Controllability in Optimal Trajectory Planning

Controllability metrics are used to determine or incentivise the ease with which a system can be driven to a specified target. Such metrics stemming from the controllability gramian are currently used to analyse the local controllability of nonlinear systems about precomputed trajectories. This paper introduces an alternative application of these metrics to generate an optimal trajectory that directly incentivises the local controllability of a nonlinear system. Such optimal trajectory design techniques would be useful for reducing the additional control energy requirement necessary to achieve control objective satisfaction in the presence of unmodelled disturbances. We present an approach in which the LTV controllability gramian is used to construct an augmented optimal control problem that incentivises the local controllability of a nonlinear system about the optimal trajectory. A modified form of Zermelo’s boat problem is used to demonstrate the approach, showing that trajectories generated by solution of the augmented optimal control problem require the system to expend less additional control energy to recover the optimal terminal state following perturbation due to an unmodelled disturbance.
Finite-Region Asynchronous Fault-Tolerant Control of 2-D Markov Jump Systems with Sensor Faults

This paper investigates the finite-region asynchronous fault-tolerant control problem for two-dimensional Markov jump systems subject to sensor faults. The purpose of this paper is to derive a suitable asynchronous fault-tolerant control method to make the closed-loop two-dimensional Markov jump systems be finite region stable and fulfill the defined H∞ performance index. Then, some sufficient conditions are developed to acquire the finite-region control method by means of the stochastic Lyapunov-Krasovskii functional methods. In addition, to illustrate the feasibility of the methodology, a numerical example is presented.
An Assessment of Covid19 Using CNN Architecture

Hundreds of thousands of people have been affected by the COVID-19 epidemic all over the world. The high mortality rate has impacted more than 200 countries. The Corona virus is highly contagious, which means patients are consistently waiting for a diagnosis, resulting in time-consuming radiologists and poor patient care. Using deep learning techniques to recognize COVID-19 from chest X-ray images is important to expedite the process. Our project will use these techniques in order to expedite the process. A patient's chest X-ray will be taken into account, and the output of the model will be labeled as COVID-positive or COVID-negative. Because of its relative speed, greater accuracy is achieved and processing delays are avoided as compared to conventional methods. A novel strain of Coronavirus (COVID-19) was discovered in Wuhan, a city in China, and is rapidly spreading around the globe. There are currently approximately 215 countries with COVID-19 illnesses. WHO reports 11,274,600 cases worldwide. Despite the increasing number of COVID-19 patients in hospitals, there are few resources available to stop the epidemic. For this reason, it is imperative to accurately diagnose COVID-19. In order to prevent the spread of the disease, patients with the disease must be diagnosed immediately. This study suggests a deep learning-based approach to differentiate patients with COVID-19 from those with viral pneumonia, bacterial pneumonia, and those with healthy (normal) lung function.
A Social Referencing Disambiguation Framework for Domestic Service Robots

The successful integration of domestic service robots into home environments can bring significant services and convenience to the general population and possibly mitigate important societal issues, such as care provision for older adults. However, home environments are complex, dynamic and object-rich. It is, thus, very probable that service robots will encounter ambiguity while interacting with household items. To enable service robots to be more adaptive, we proposed a learning so-cial referencing computational framework and experimentally evaluated the framework on a mobile manipulator robot, Fetch, in object selection scenarios. The framework allows the robot to (1) detect and analyze the ambiguity level based on the robot's view and user's command, (2) assess the human's attention level and attract their attention, (3) disambiguate references to objects using human feedback and (4) learn novel objects after clarification from the user. System evaluation results are presented. The framework is modular and can be applied to different robotic platforms.
Exploration of Unknown Scalar Fields with Multifidelity Gaussian Processes Under Localization Uncertainty

Autonomous marine vehicles are deployed in oceans and lakes to collect spatio-temporal data. GPS is often used for localization, but is inaccessible underwater. Poor localization underwater makes it difficult to pinpoint where data are collected, to accurately map, or to autonomously explore the ocean and other aquatic environments. This paper proposes the use of multifidelity Gaussian process regression to incorporate data associated with uncertain locations. With the proposed approach, an adaptive sampling algorithm is developed for exploration and mapping of unknown scalar fields. The reconstruction performance based on the multifidelity model is compared to that based on a single-fidelity Gaussian process model that only uses data with known locations, and to that based on a single-fidelity Gaussian process model that ignores the localization error. Numerical results show that the proposed multifidelity approach outperforms both single-fidelity approaches in terms of the reconstruction accuracy.
Green Computing: Optimized Underutilized Host detection in IQR Vm Allocation Policy

In a broader sense, the term “green computing” refers to techniques and guidelines for creating, utilizing, and decommissioning computer resources in a way that is ecologically beneficial while still preserving overall computing performance. To do this, it is necessary to utilize less hazardous materials, use products as efficiently as possible while using the least amount of energy, and increase the reusability, recyclable content, and biodegradability of trash. Many businesses are making efforts to lessen the negative effects of their activities on the environment. An international environmental treaty known as the United Nations Framework Convention on Climate Change (UNFCC) aims to stabilize greenhouse gas emissions in the atmosphere at a level that would stop harmful anthropogenic interference with the eco system. Sustainable development entails growth that doesn't jeopardize the needs of coming generations. It is achieving human development goals while protecting the ecosystems and natural resources that are essential to society. The importance of green computing for sustainable development is emphasized in this paper's assessment of several significant recent studies in the subject. The use of computers and other computing devices in ways that are both energy-efficient and ecologically friendly is known as green computing also referred to as green technology. Energy-efficient servers, peripherals, power systems, and central processing units (CPUs) are frequently used in green computing firms. They underline the necessity of conserving resources and properly disposing of physical and electronic trash (e-waste).
Stabilization of a class of singularly perturbed switched systems

In this paper, we address the control of a class of switched systems cast in the framework of singularly perturbed systems. The class of switched systems that we deal with here is a particular case of switched affine systems where the state matrices are the same for all modes. These systems have been studied in the literature, wherein control design is carried out by solving Linear Matrix Inequalities (LMIs). However, the presence of the small parameter ε, characteristic of singularly perturbed systems, in the dynamical equation introduces numerical stiffness. To the best of the authors’ knowledge, these issues have not been addressed in the literature for the class of switched systems studied here. We propose an ε-dependent controller stabilizing the system and also an ε-independent controller, in the case where the parameter ε is not well-known. The design of these control laws is based on LMIs that do not present the ill-conditioning linked to ε. The proposed approach is illustrated by simulation results.
Bootstrapping the Dynamic Gait Controller of the Soft Robot Arm

In this paper, we propose a novel dynamic gait controller for the repetitive behavior of soft robot manipulators performing routine tasks. Compliance with soft robots is advantageous when the robot interacts with living organisms and other fragile objects. However, predicting and controlling repetitive behavior is challenging because of hysteresis and non-linear dynamics governing the interactions. Existing priorfree methods track the dynamic state using recurrent neural networks or rely on known generalized coordinates describing the robot's state. We propose to model the interaction induced by the repetitive behavior as gait dynamics and represent the dynamic state with Central Pattern Generator (CPG) tracking the motion phase and thus reduce the complexity of the robot's forward model. The proposed method bootstraps an ensemble of the forward models exploring multiple dynamic contexts that are expanded as it searches for repetitive motion producing the target repetitive behavior. The proposed approach is experimentally validated on a pneumatically actuated soft robot arm I-Support, where the method infers gaits for different targets.
A Survey on Facial Emotion Recognition

One of the most significant methods of interpersonal communication is facial emotion recognition, which is an intuitive mirror of a person’s mental state and contains rich emotional information. It is said to be a strong silent communication tool. Analysis of these expressions provides a far deeper understanding of human behaviour. With potential applicability in dynamic analysis, pattern identification, interpersonal communication, mental health screening, and many other fields, AI-based Facial Expression Recognition (FER) has emerged in recent years as one of the most important study areas. With the recent advancement in computer vision and machine learning, analysing facial emotions becomes a little bit easier. This paper presents a theoretical analysis of different facial emotion recognition techniques that were used in recent times.
Lyapunov Stability Regulation of Deep Reinforcement Learning Control with Application to Automated Driving

Reinforcement learning (RL) control for nonlinear dynamical systems has seen increasing interests in recent years. However, these methods have limited practical use due to the lack of safety and stability guarantees of the control policy. In this paper, we employ a control-theoretic approach to the stability of RL-based control. We propose a two-step framework to train a Deep Deterministic Policy Gradient (DDPG) agent regulated on the violations of the Lyapunov conditions. In the first step, our framework leverages neural networks to jointly learn stable system dynamics and an associated control-Lyapunov function (cLf) based on the current control policy. In the second step, a DDPG controller is trained to learn an appropriate control policy that maximizes the reward function and minimizes the violation of the Lyapunov condition based on the current iteration of the cLf. We employ a model of dynamics noise when learning the cLf to improve the exploration of alternative state trajectories. The proposed framework is tested on nonlinear vehicle dynamics in a lane-following highway environment. The experimental results demonstrate the ability of the proposed framework to regulate the control policy on learning stable system trajectories with desired driving characteristics.
A Model for Evaluating the Efficiency of Government Functional Departments based on VFT-AHP Data Analysis

An efficiency evaluation model of government functional departments is constructed, taking China`s Maritime Department as an example. Firstly, based on the value-focused-thinking method, an evaluation indicator system of maritime government departments efficiency evaluation is constructed, including 4 dimensions, 21 secondary indicators, and 41 tertiary indicators, constructing 2 categories of target indicators and monitoring indicators. Secondly, the weight of each indicator is determined by the AHP using the MATLAB software. Finally, the generalized utility function is used to calculate the comprehensive evaluation value of efficiency and an empirical analysis was conducted. The evaluation model will help to deepen the theoretical connotation of the efficiency of government functional departments and promote the exploration of quantitative evaluation methods of government efficiency.
Estimating Parameter Regions for Structured Parameter Tuning via Reduced Order Subsystem Models

Many large-scale systems are composed of subsystems operated by decentralized controllers, which are fixed in their structure, yet have parameters to tune. Initial tuning or subsequent adjustments dof those parameters ue to varying operating conditions or changes in the network of interconnected systems, while ensuring stability, performance, and security, pose a challenging task due to the overall complexity and size. Subsystems may not be willing or allowed to expose detailed information for safety and privacy reasons. In some cases, a comprehensive system model might not be available for global tuning, or the resulting problem might be computationally infeasible. To enable meaningful global parameter tuning while allowing for data privacy and security, we propose that the subsystems themselves should provide reduced-order models. These models capture the parametric dependency of the subsystem dynamics on the controller parameters. Specifically, we present a method to construct a region in the subsystems’ parameter space in which the deviation of the subsystem and the reduced-order model stays below a specified error bound and in which both systems are stable. A necessary and sufficient condition for such regions is derived using robust control theory. Notably, sufficiency can be expressed in terms of a linear matrix inequality. We demonstrate the approach by considering the temperature control of a large-scale building complex.