Ultra-High Frequency Radio Frequency Identification (UHF RFID) is widely used for item-level traceability. Existing approaches often exploit analog features of backscattered signals to sense a single environmental factor, typically assuming static measurement setups. In this paper, we categorize antenna-sensitive factors into two types: time-independent and time-dependent, and propose a classification algorithm to decouple them from time-series analog features. First, we apply Dynamic Time Warping (DTW) Barycenter Averaging to cluster features under different power levels for each time-independent factor, constructing offline models. Second, we combine DTW similarity between online measurements and offline references with model-based state estimation to identify the time-independent factor. Then, the time-dependent factor is estimated using a sliding-window cumulative probability method. We validate our approach through an infusion monitoring system using a flexible passive UHF RFID tag attached externally to the infusion tube. Here, liquid level represents the time-dependent factor, while liquid material is the time-independent factor. Experimental results demonstrate that our method accurately identifies both liquid level and material under varying measurement setups.

This paper presents a robust yet efficient Grey Wolf Optimizer-Support Vector Machine algorithm, termed K-GWO-SVM, for the analysis of ECG signals in smart healthcare systems, aiming to improve classification accuracy and computational efficiency. The proposed model introduces three main contributions: (1) the use of GWO to automatically search for the optimal hyperparameters of SVM tailored to each dataset, (2) a mini-batch strategy guided by K-means clustering to improve the efficiency and convergence of GWO by selecting representative subsets of data, and (3) an enhanced regulation function integrated into GWO that prevents premature convergence by improving the balance between exploration and exploitation. A convergence study is conducted to demonstrate the influence of mini-batch size on both classification accuracy and computational efficiency, showing that using mini-batches as small as 10% of the training data significantly improves computational efficiency without compromising classification accuracy. The K-GWO-SVM framework is evaluated on two benchmark datasets: WESAD for emotion recognition and MIT-BIH Arrhythmia for cardiac classification. The proposed model achieves 99.02% accuracy on WESAD with over a 90% reduction in computational time (10% mini-batch), and 100% accuracy on MIT-BIH with over a 50% reduction in computational time (50% mini-batch), validating its effectiveness, robustness, and suitability for deployment in resource-constrained smart healthcare environments. • Robust yet efficient K-GWO-SVM algorithm is presented for ECG signal analysis. • A novel mini-batch technique is introduced to reduce computational complexity. • K-means defines centroids to form mini-batches for faster GWO convergence. • Convergence study demonstrates mini-batch size effects on accuracy and efficiency. • Dual validation on WESAD and MIT-BIH datasets proves clinical applicability.

The Linear Quadratic Regulator (LQR) problem for systems with uncertainty is challenging: model-based design loses optimality, while prevailing reinforcement learning methods demand prohibitive data and computation. This paper introduces a Hybrid-Driven Recurrent Learning (HDRL) framework that bridges this gap by synergizing model-based optimal control with data-driven learning in a novel way. The core of HDRL is a hybrid training strategy: the policy is evaluated by rolling out on the actual uncertain system (forward pass), while the policy gradient is computed by backpropagating through a deterministic nominal model (backward pass). This approach creates a low-variance, model-guided policy gradient, a fundamental departure from high-variance model-free estimators. Architecturally, HDRL employs a recurrent neural network-like structure, repurposing the LQR cost as a self-supervised loss. A key enabler is our method to convert both additive and structured uncertainties into an additive signal, making training feasible without knowledge of the perturbed dynamics. Simulations demonstrate that HDRL provides a robust, sample-efficient, and practical solution for optimal control under uncertainty.

Graph Neural Networks have emerged as a formidable tool for analyzing point clouds, leveraging their capacity to aggregate local features across multiple spatial scales via layered structures. However, a significant challenge lies in effectively and selectively integrating these multi-scale features to maximize overall performance. To tackle this integration challenge, we design a novel model, MambaGCN, which employs a state space model to dynamically adjust the feature weights across spatial scales during aggregation, enabling more refined feature integration while ensuring computational efficiency. Unlike transformers with their quadratic complexity, MambaGCN achieves linear complexity, substantially reducing GPU memory usage and computational cost. Moreover, we have enhanced the architectural depth by designing a density-based farthest point sampling algorithm, which allows us to selectively downsample the input data to achieve varying levels of point density. This innovation facilitates the seamless concatenation of multiple MambaGCN layers, significantly deepening the structure of the network and enhancing its ability to tackle complex point cloud tasks effectively. Through these strategic developments, MambaGCN has demonstrated outstanding performance in tasks such as point cloud classification and part segmentation, affirming its robustness and efficiency in processing point cloud data.

This paper addresses the distributed localization problem for a network of sensors placed in a three-dimensional space, in which sensors are able to perform range measurements, i.e., measure the relative distance between them, and exchange information on a network structure. While most existing studies primarily develop localization algorithms under the assumption that the entire sensor network is localizable, the problem of determining whether a sensor is localizable has received limited attention. However, neglecting this preliminary step can significantly hamper the accuracy of localization algorithms due to error propagation from unlocalizable sensors in iterative localization procedures. To address this research gap, we focus on two key challenges: i) deriving rigorous theoretical results and developing algorithms to verify sensor localizability, and ii)designing an efficient distributed localization algorithm that utilizes these localizability results. Specifically, we start by deriving a necessary and sufficient condition for sensor localizability using barycentric coordinates. Then, building on this theoretical result, we design a distributed localizability verification algorithm, in which we propose and employ a novel distributed finite-time algorithm for sum consensus. Finally, we develop a distributed localization algorithm based on conjugate gradient method and derive theoretical guarantees on its performance, ensuring finite-time convergence. The efficiency of our algorithm compared to the existing ones from the literature and its capability to handle scenarios with moderate levels of noise in the measurements are further demonstrated through numerical simulations.

This paper proposes a novel graph classification method to tackle the categorization of complex and irregularly structured graphs, demonstrating state-of-the-art performance on several benchmarks. This method is predicated on the principle of graph signal decomposition. Although significant research has been conducted on frequency decomposition for node classification tasks, its application to graph classification has not been extensively explored. To this end, we introduce an innovative graph classification framework known as the Multi-Frequency Graph Convolutional Network (MFGCN). The MFGCN framework operates by capturing both high-frequency and low-frequency information within graph data at various hierarchical levels, thereby enriching the graph representation, which is crucial for classification tasks. Subsequently, these multi-frequency graph representations are processed using 3D convolutional layers, followed by fully connected layers to perform the classification. We have conducted a theoretical analysis on the extraction of high and low-frequency signals from graph signals. To validate the efficiency of our MFGCN framework, we evaluated it against six benchmark datasets: COLLAB, MUTAG, IMDB-BINARY, PROTEINS, IMDB-MULTI, and D&D. Our results demonstrate a marked improvement in graph classification accuracy when compared to the most advanced existing methods.

Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot systems, such as changes in speed, can lead to suboptimal utilization of the system's capabilities, subsequently reducing the efficiency of task execution. In this paper, we developed a multi-robot dynamic spanning tree coverage (MDSTC) algorithm, an online area-division-based approach that adjusts region allocation among robots based on their coverage status through an exchange-based mechanism to enhance multi-robot system efficiency. To validate the effectiveness of the proposed algorithm, we conducted extensive numerical simulations. The results demonstrate that our algorithm achieves a superior solution in scenarios where robot efficiency remains balanced, closely matching the performance of state-of-the-art mCPP methods. Furthermore, when differences in efficiency arise among the robots during task execution, the proposed algorithm provides a dynamic solution that enhances overall coverage efficiency.

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://github.com/SUSTech2022/DRSS-sensor-array-calibration.

Emotion recognition or detection is broadly utilized in patient–doctor interactions for diseases, such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for nonbehavior-based emotion recognition in real time. This can be solved by implementing a single-channel electrocardiogram (ECG)-based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. For instance, the accuracy is about 82.78% by using a least squares support vector machine (SVM). Therefore, we propose a reliable and efficient emotion recognition scheme—exploitative and explorative gray wolf optimizer-based SVM (X-GWO-SVM) for ECG-based emotion recognition. Two data sets, one raw self-collected iRealcare data set, and the widely used benchmark WESAD data set are used in the X-GWO-SVM algorithm for emotion recognition. Leave-single-subject-out cross-validation yields a mean accuracy of 93.37% for the iRealcare data set and a mean accuracy of 95.93% for the WESAD data set. This work demonstrates that the X-GWO-SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.

With the development of large-scale power grids, the issue of distributed economic dispatch has received considerable critical attention. However, due to the existence of some effects such as valve-point effects, the nonconvex objective function remains a major challenge for the distributed optimization problem. This paper proposes a cooperative deep reinforcement learning algorithm for distributed economic dispatch with the nonconvex objective function. In the distributed algorithm, all nodes obtain the value of actions by observing the environment and update state-action-value function in coordination with local neighbors. The state-action-value function is approximated by a neural network, which allows the algorithm to be used for large and continuous state spaces. The advantages of the algorithm are demonstrated through several case studies.

In this paper, a curved path following scheme with the aid of the vector field (VF) and the notion of input-to-state stable (ISS) for a fixed-wing unmanned aerial vehicle (UAV) is developed. The VF strategy is a robust and valid guidance method and its stability is proved using ISS properties. Many existing path following algorithms for fixed-wing UAVs are only proposed for straight-lines and orbits. However, the path required to be followed is always in approximate curves rather than straight-lines and orbits in many high-level missions, such as obstacle avoidance, search, and surveillance. The nonlinear-theoretic notion of ISS is playing a central role in the control law design and stability analysis. The error kinematics are converted into two interconnected subsystems with proven ISS properties, which yield the overall system that is globally asymptotically stable, i.e., and the along-track error and the cross-track error asymptotically approach zeros from any initial position in the space. The followed path is defined in terms of the arc-length parameter, and it can be expanded according to the waypoint fitting without the need to obtain a global function representation. The singularity of multiple closest points on the path is eliminated by constructing a speed profile of a virtual point on the path. The scheme is validated with a semi-physical experiment combined by an actual autopilot, ground station and the X-Plane flight simulator. Flight tests using a small fixed-wing UAV show excellent tracking performance of the curved path following.

This paper studies a multi-agent formation control problem under a leader-follower framework, where the agents are governed by double-integrator dynamics and the objective is to achieve a formation with desired shape and a specified size. Firstly, a distributed control algorithm is developed for the leaders to achieve a desired distance. Then, by combining the control law for the leaders and a relative position estimation based control law for the followers, an estimation based formation control algorithm is developed for the entire multi-agent system to asymptotically achieve a formation with desired shape and a specified size in the case without initial relative position estimation errors and approximately attain the desired formation in the case with initial relative position initial estimation errors. Simulation results are provided to validate the proposed algorithm.

This paper investigates two formation control problems for a leader-follower network in 3-D. One is called the formation marching control problem, the objective of which is to steer the agents to maintain a target formation shape while moving with the synchronized velocity. The other one is called the formation rotating control problem, whose goal is to drive the agents to rotate around a common axis with a target formation. For the above two problems, we consider directed and switching sensing topologies while the communication is assumed to be bidirectional and switching. We develop approaches utilizing barycentric coordinates toward these two problems. Local control laws and graphical conditions are acquired to ensure global convergence in both scenarios.

This paper establishes a new framework for modeling electrical cyber-physical systems (ECPSs), integrating both power grids and communication networks. To model the communication network associated with a power transmission grid, we use a mesh network that considers the features of power transmission grids such as high-voltage levels, long-transmission distances, and equal importance of each node. Moreover, bidirectional links including data uploading channels and command downloading channels are assumed to connect every node in the communication network and a corresponding physical node in the transmission grid. Based on this model, the fragility of an ECPS is analyzed under various cyber attacks including denial-of-service (DoS) attacks, replay attacks, and false data injection attacks. Control strategies such as load shedding and relay protection are also verified using this model against these attacks.

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

We consider the control of formations of a leader-follower network, where the objective is to steer a team of multiple mobile agents into a formation of variable size. We assume that the shape description of the formation is known to all the agents, which is captured by a complex-valued Laplacian associated with the sensing graph, but the size scaling of the formation is not known or only known to two agents, called the leaders in the network. A distributed linear control strategy is developed in this paper such that the agents converge to the desired formation shape, for which the size of the formation is determined by the two leaders. Moreover, in order to make all agents in a formation move with a common velocity, the distributed control law also incorporates a velocity consensus component, which is implemented with the help of a communication network that may, in general, be of different topology from the sensing graph. Both the setup of single-integrator kinematics and the one of double-integrator dynamics are addressed in the same framework except that the acceleration control in the double-integrator setup has an extra damping term.

Time synchronization is critical for wireless sensor networks (WSNs) because data fusion and duty cycling schemes all rely on synchronized schedules. Traditional synchronization protocols assume that wireless channels are available around the clock. However, this assumption is not true for WSNs deployed in intertidal zones. In this article, we present TACO, a synchronization scheme for WSNs with intermittent wireless channels and volatile environmental temperatures. TACO estimates the correlation of clock skews and temperatures by solving a constrained least squares problem and continuously adjusts the local time with the predicted clock skews according to temperatures. Our experiment conducted in an intertidal zone shows that TACO can greatly reduce the clock drift and prolong the resynchronization intervals.

This article investigates the controllability problem of multi-agent systems. Each agent is assumed to be governed by a second-order consensus control law corresponding to a directed and weighted graph. Two types of topology are considered. The first is concerned with directed trees, which represent the class of topology with minimum information exchange among all controllable topologies. A very simple necessary and sufficient condition regarding the weighting scheme is obtained for the controllability of double integrator multi-agent systems in this scenario. The second is concerned with a more general graph that can be reduced to a directed tree by contracting a cluster of nodes to a component. A similar necessary and sufficient condition is derived. Finally, several illustrative examples are provided to demonstrate the theoretical analysis results.

This paper reviews some main results and progress in distributed multi-agent coordination from a graph Laplacian perspective. Distributed multi-agent coordination has been a very active subject studied extensively by the systems and control community in last decades, including distributed consensus, formation control, sensor localization, distributed optimization, etc. The aim of this paper is to provide both a comprehensive survey of existing literature in distributed multi-agent coordination and a new perspective in terms of graph Laplacian to categorize the fundamental mechanisms for distributed coordination. For different types of graph Laplacians, we summarize their inherent coordination features and specific research issues. This paper also highlights several promising research directions along with some open problems that are deemed important for future study.

We tackle the problem of a biped running over varied and unknown terrain. Running is a necessary skill for a biped moving fast, but it increases the challenge of dynamic balance, especially when a biped is running on varied terrain without terrain information (due to the difficulty and cost of obtaining the terrain information in a timely manner). To address this issue, a new dynamic indicator called the sustainable running criterion is developed. The main idea is to sustain a running motion without falling by maintaining the system states within a running-feasible set, instead of running on a periodic limit cycle gait in the traditional way. To meet the precondition of the criterion, the angular moment about the center of gravity (COG) is restrained close to zero at the end of the stance phase. Then to ensure a small state jump at touchdown on the unknown terrain, the velocity of the swing foot is restrained within a specific range at the end of the flight phase. Finally, the position and velocity of the COG are driven into the running-feasible set. A five-link biped with underactuated point foot is considered in simulations. It is able to run over upward and downward terrain with a height difference of 0.15 m, which shows the effectiveness of our control scheme.

The paper studies the formation control problem for distributed robot systems. It is assumed each robot only has access to local sensing information (i.e. the relative positions and IDs of its neighbors). Taking into consideration physical sensing constraints (e.g. limited sensing range) and the motion of the robots over time, it may be noted that the sensing graph for the system is directed and time-varying. This presents a challenging situation for formation control. As an initiative attempt to study this challenging situation, we suppose the sensing graph switches among a family of graphs with certain connectivity properties, under which a switching linear control law is then proposed. We show that for arbitrary dwell times or average dwell times, the proposed control law with properly designed control parameters can ensure global convergence to a desired formation shape. The proposed formation control law can be implemented in a distributed manner while the design of certain control parameters requires some global information.

The paper studies non-deadlock reconfiguration strategies in cooperative module systems with the goal of driving the modules from any initial configuration to any goal configuration. A matrix representation is introduced to describe the configuration of a hexagonal-shaped module system and its reconfiguration. We firstly find a sub-configuration in the goal configuration, called trunk, to ensure the success of the reconfiguration strategy to fill the trunk first and then the remaining. Next, based on the matrix representation, we propose a new reconfiguration strategy to fill the trunk and the whole goal configuration gradually according to a proper label sequence. It has been proved that the strategy ensures non-deadlock in reconfiguration from any initial configuration to any goal configuration as long as both are physically connected.

In the paper, we studied two local multi-robot coordination problems, namely, local gathering control and formation tracking. Firstly, a new local control strategy is proposed to address the gathering problem based on routing schemes and using only bearing measurements. The interaction graph might be directed and time varying, but the convergence is still ensured. Second, a zero steady-error formation tracking control is developed by introducing an integral component, which eliminates the steady-state error when the leaders preform uniform rectilinear motions. Last, a novel IR sensor is developed to measure relative bearing angles and distances of neighboring robots and experiments are carried out based on the developed mobile platforms with IR sensors, which demonstrate not only the success of our proposed control strategies, but also the applicability of our developed mobile platforms in the field of local multi-robot coordination.

This paper focuses on finding a dual quaternion solution to attitude and position control for multiple rigid body coordination. Representing rigid bodies in 3-D space by unit dual quaternion kinematics, a distributed control strategy, together with a specified rooted-tree structure, are proposed to control the attitude and position of networked rigid bodies simultaneously with notion concision and nonsingularity. A property called pairwise asymptotic stability of the overall system is then analyzed and validated by an example of seven quad-rotor formation in the Urban Search And Rescue Simulation (USARSim) platform. As a separate but related issue, a maximum depth condition of the rooted tree is found with respect to error accumulation along each path using dual quaternion algebra, such that a given safety bound on attitude and position errors can be satisfied.

This paper addresses distributed transmit beamforming problems based on a mobile assister node. Assuming that the distance to the receiver and the direction-of-destination (DoD) can be estimated, a stop-and-go strategy is proposed for a mobile assister node such that it moves gradually along the direction towards the receiver and provides SNR feedback information for transmitters. By receiving the SNR feedback information at each step, the transmitting sensors update their phases to maximize the SNR at the assister end. We then show that when the assister node reaches a threshold distance, the maximum SNR at the assister implies an approximate optimum SNR at the receiver end. Thus, the transmit beamforming problem is solved though the receiver initially is out of the communication range of the transmitters and is not able to provide SNR feedback information.

The paper studies the problem of a group of hexagonal-shaped metamorphic modules enveloping a static one-cell target. A distributed reconfiguration strategy is pro posed for each module to achieve the objective. The strategy guarantees that the system successfully evolves without collisions despite of the absence of centralized planning and global information exchange. The correctness of the algorithm is verified theoretically. An upper-bound of converging time based on the proposed algorithm is provided as well. Simulations well validate the results we obtain.

For a compass-like biped robot, the problem of achieving stable walking on both ideal inclined surfaces and complex environments is studied. For the case of walking on ideal inclined surfaces, a feedback-control law is obtained via feedback-linearization techniques so that the trajectory of the robot converges to a desired passive walking gait. Simulations show that it leads to a larger basin of attraction. For the case of walking in complex environments, a scheme is developed with regulable step length and walking speed. Different reference trajectories are constructed for different steps, and corresponding feedback-control laws are updated at the beginning of each step. Then, it is shown that the errors that measure the difference of the response trajectory and the reference one asymptotically converge to zero. An example of walking over stairs is given to numerically verify and demonstrate our approach.

The paper studies the problem of distributed transmit beamforming with autonomous and self-organizing mobile antennas. The objective is to design a distributed algorithm for a network of autonomous mobile robots with carry-on antennas so that they can form a functional antenna array and cooperatively transmit messages to a remote station. Note that the spatial relationship of the antennas also contributes to the directionality of the reception or transmission of a signal. In the paper, by exploiting the mobility of the antennas, we show that optimal beamforming can be achieved by reconfiguring the spatial relationship of the mobile antennas in a completely distributed fashion. A probability-based coordination scheme utilizing only the signal-to-ratio (SNR) feedback from the receiver is presented to update the positions of the antennas ensuring that they eventually converge to a global optimal configuration maximizing the SNR at the receiver. It is noticed that the spatial configuration of the antennas can also address the phase synchronization issue in transmit beamforming.

A moving-target-enclosing problem is investigated in the paper, where the velocity of the target is unknown and the neighbor topologies may change over time. Each robot only uses the relative position information of the target and its neighbors that may dynamically change over time. An adaptive scheme is proposed to estimate the velocity of the target. Then a distributed control law for each robot is presented, which consists of two parts: One amounts to ensuring the convergence of the distance between the robots and the target to the desired one and the other is used to achieve the uniform distribution when enclosing the target in motion. Lyapunov-based techniques and graph theory are brought together for rigorous analysis of the convergence and stability properties. Our control strategy is practically implementable with only onboard sensors. Simulations are provided to illustrate our results.

The paper studies hierarchical pursuit strategies for groups of mobile agents in the plane. It is shown that fascinating global patterns emerge from simple two-layer pursuit schemes, including rendezvous, uniform circular motion, complex circular motion, concentric circular motion, and concentric logarithmic spiral motion. Both rigorous analysis and simulations are provided.

The paper studies formation control of multi-agent systems under a directed acyclic graph. In a directed acyclic graph, the agents without neighbors are leaders and the others are followers. Leaders move in a formation with a time-varying velocity and followers can access the relative positions of their neighbors and the leaders' velocity. A local formation control law is proposed in the paper based on pursuit strategies and necessary and sufficient conditions for stability and convergence are derived. Moreover, the results are extended to the case with arbitrary communication delays, for which the steady-state formation is presented according both the control parameters and time delays.

In the paper, we study the motion planning problem of a mobile robot in the plane. The goal is to design output feedback control such that the resulting path of a mobile robot satisfies desired linear temporal logic (LTL) specifications. Our control strategy is divided into a local output feedback control problem and a supervisory control for LTL specifications. For the former one, we design output feedback control laws to ensure that output trajectories either remain in a simplex, or leave the simplex and enter an adjacent simplex in finite time. For the latter, we construct a transition system based on reachability and search for feasible paths that satisfy the LTL specifications. In this way, a piecewise affine output feedback control is obtained to solve the motion planning problem. A simulation result is presented to illustrate our approach.