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In this work, we present a novel design for an embedded Radio Frequency (RF) antenna array that can distinguish various materials by analyzing changes in Received Signal Strength Indicator (RSSI) values. The use of a low-cost and small-form-factor microcontroller by Espressif makes this design both cost-effective and suitable for integration into various applications, differentiating it from previous studies. To enhance the material classification performance, a combination of Kalman filter and Support Vector Machine is proposed which does not require a large amount of training data for model optimization. Results demonstrate that the proposed machine learning model is able to perform material classification within a 2 m range, with an average accuracy of over 96%. Such a system is well-suited for intelligent mobile robotic applications particularly in warehouse automation or smart manufacturing lines due to its ability for proximal remote sensing, real-time monitoring, and multimodal sensing.

This paper presents a comprehensive review of the latest developments in fuzzy logic (FL) applications across critical domains which include energy harvesting (EH), ambient conditioning systems (ACS), and robotics and autonomous systems (RAS), highlighting FL's capability to address nonlinearities and uncertainties in diverse technological environments. Through a detailed comparative analysis of research trends over the past decade, it underscores the increasing significance of FL in EH and RAS, contrasting with the sustained interest in ACS. Furthermore, the evaluation of different fuzzy inference systems across domains provides valuable insights into their specific strengths and limitations, aiding researchers and practitioners in making informed decisions aligned with their application needs. Additionally, the paper explores advanced modifications and hybridizations of FL, such as swarm intelligence and integrations with other control strategies, emphasizing the necessity for robust and adaptive FL systems. The review also identifies key open problems and potential research directions, such as the demand for adaptive FL systems in EH and advanced optimization techniques in ACS and RAS. Overall, this state-of-the-art review not only summarizes the current state of FL applications but also outlines a roadmap for future research, offering valuable insights for advancing FL's role in handling uncertainties and nonlinearities in complex systems.

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

This review paper provides a detailed overview of the advancements and identifies pivotal challenges in the realm of autonomous load-carrying mobile robots, with a particular focus on indoor applications for both ground and aerial platforms. It critically examines the past decade’s innovations in load-carrying designs and sensor technologies, scrutinizing their impact on the enhancement of robotic autonomy and load management. The paper also presents an in-depth analysis of the latest trends in navigation and control algorithms essential for refining these robots’ operational efficacy in diverse indoor scenarios. By evaluating current research outputs, this work identifies critical areas for future exploration, such as improving indoor navigation complexity, optimizing load handling for varying conditions, and pioneering precise load-sensing techniques. The paper proposes innovative research paths designed to address the identified gaps, underscoring the necessity for breakthroughs in robot design, enhanced integration of systems, and increased operational efficiency. The overarching aim is to propel the functionality of autonomous load-carrying robots to new heights, ensuring they meet the increasing demands of various sectors including industrial, commercial, and service domains.

Material classification is pivotal across materials science, engineering, and various industrial sectors. Despite the high accuracy of traditional material classification methods, they often entail large, intricate, and costly setups that demand skilled operators. In this study, we introduce the MCT-array, a newly developed compact RF antenna array system measuring $100\times 100 \times 2$ mm, which functions as a transceiver. This device, equipped with 32 receiving antennas and 2 transmitters, leverages dynamic power adjustments to refine material detection accuracy. The study evaluates three machine learning classifiers, namely Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RandF) on twelve different materials. MATLAB simulations are initially conducted to identify optimal transceiver configurations. Following the identification of optimal parameters from these simulations, real-world experiments are conducted with the materials positioned 30 cm away from the antenna. Results demonstrate that RandF achieves a material classification accuracy of 94.84%, followed by SVM at 94.5%, and MLP at 94.1%. Detailed analysis further reveals that RandF is the preferred option for tasks demanding the highest levels of accuracy, SVM strikes an optimal balance between processing speed and accuracy, while MLP stands out for its rapid prediction times, making it especially suitable for real-time applications. Integrating an innovative portable RF transceiver with these machine learning models, achieving an impressive average accuracy of over 94%, represents a scalable and effective solution. This innovation holds significant promise for sectors engaged in material classification, particularly in the realms of robotics and automation.

This study presents a hybrid HHO-AVOA which is a novel optimization method that combines the strengths of Harris Hawks Optimization (HHO) and African Vulture Optimization Algorithm (AVOA) to address the path planning challenges encountered by differential wheeled mobile robots (DWMRs) navigating both static and dynamic environments, while accommodating kinematic constraints. By synergizing the strengths of both algorithms, the proposed hybrid method effectively mitigates the limitations of individual approaches, resulting in efficient and obstacle-avoiding navigation towards the target within reduced timeframes. To evaluate its efficiency, the proposed approach is compared against HHO and AVOA as well as other established methods which include whale optimization, grey wolf optimization and sine-cosine algorithms. Simulation results along with Monte Carlo analysis consistently demonstrate the superior performance of the hybrid method in both environments. In static scenarios, the hybrid algorithm achieves an average reduction of approximately 14% in path length and a 17% decrease in DWMR travel duration. In dynamic cases, it outperforms the rest with an average reduction of 27.6% in path length and a 27.2% decrease in travel duration. The algorithm’s low computational complexity is also exhibited via its fast convergence during path optimization which is a crucial attribute for real-time implementation, particularly in dynamically changing environments that demand quick decision-making. The superiority of the proposed hybrid method to balance the exploration and exploitation is also affirmed through a Wilcoxon rank-sum test with a 95% confidence interval.

While the recurrent neural network (RNN) architecture has been the go-to model in transient modeling, recently the temporal convolutional network (TCN) has been garnering more attention as it has a longer memory than recurrent architectures with the same capacity. In this paper, we propose the use of the TCN for transient simulation of high-speed channels. The adaptive successive halving algorithm (ASH-HPO) is used to perform automated hyperparameter optimization for the TCN. It has two components, progressive sampling and successive halving. It iteratively expand the size of training dataset and eliminates a certain percentage of bad performing models. The progressive sampling component is modified to preserve the original sequencing of time series data to prevent information leakage. Also, the successive halving component is modified so that each eliminated model must be validated using at least two different validation datasets before it is being removed. The robustness of the proposed method is demonstrated using four high-speed channel examples, and the TCN is compared against existing convolutional neural network long short-term memory (CNN-LSTM) and dilated causal convolution (DCC) models. The TCN outperforms the other models consistently in all four tasks in terms of training speed, amount of training data to converge, and accuracy.

The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system’s complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notably, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the nonnormalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a squareshaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models.

Fusion of X-band Doppler radar and infrared sensors can offer a great advantage for close proximity time-to-collision (TTC) prediction in the field of autonomous robot navigation due to precise obstacle speed detection and direction sensing. Nevertheless, poor ranging performance from the infrared sensors may result owing to fluctuating reflectivity of the moving obstacle. The TTC prediction accuracy may also be further degraded when the obstacle’s trajectory is non-parallel to the robot’s heading direction due to uncertainty in the radar’s radiation pattern which typically increases at the side lobes of the antenna. Thus, to enhance the performance, we propose an exponential Gaussian Process Regression (E-GPR)-based prediction model which is able to approximate an unknown function in a probabilistic manner. The proposed method is validated via a series of experiments with an obstacle approaching the robot from different viewing angles with a speed ranging between 30 and 63 cm/s. Results demonstrate that the average TTC error based on the sensor fusion is 0.31s; but with the E-GPR method, the error is successfully reduced to 0.0937s, which is the largest error reduction when compared against other competing machine-learning models such as multilayer perceptron neural network, support vector machine and boosted tree.

Recent years have seen a dramatic rise in the popularity of autonomous mobile robots (AMRs) due to their practicality and potential uses in the modern world. Path planning is among the most important tasks in AMR navigation since it demands the robot to identify the best route based on desired performance criteria such as safety margin, shortest time, and energy consumption. The complexity of the problem can however become intractable when challenging scenarios are considered, which include navigation in a dynamic environment and solving multi-objective optimizations. Various classical and heuristic techniques have been proposed by researchers to mitigate such issues. The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable attention over the past decade. A systematic analysis of the strengths, shortcomings, and scope of each method is presented. The trends as well as challenges in practical implementation of the strategies are also discussed at the end of this paper. The outcome of this survey provides useful guidance for future research into creating new strategies that can enhance the autonomy level of AMRs while preserving their robustness against unforeseen circumstances in practice.

This article presents a modeling and simulation method for transient thermal analyses of integrated circuits (ICs) using the original and voltage-in-current (VinC) latency insertion method (LIM). LIM-based algorithms are a set of fast transient simulation methods that solve electrical circuits in a leapfrog updating manner without relying on large matrix operations used in conventional Simulation Program with Integrated Circuit Emphasis (SPICE)-based methods which can significantly slow down the solution process. The conversion from the thermal to electrical model is performed first by using the analogy between heat and electrical conduction. Since electrical inductance has no thermal equivalence, a modified VinC LIM formulation is presented which removes the requirement of the insertion of fictitious inductors. Numerical examples are presented which show that the modified VinC LIM formulation outperforms the basic LIM formulation, both in terms of stability and accuracy in the transient thermal simulation of ICs.

Transient simulations of high-speed channels can be very time intensive. Recurrent neural network (RNN) based methods can be used to speed up the process by training a RNN model on a relatively short bit sequence, and then using a multi-steps rolling forecast method to predict subsequent bits. However, the performance of the RNN model is highly affected by its hyperparameters. We propose an algorithm named adaptive successive halving automated hyperparameter optimization (ASH-HPO) which combines successive halving, Bayesian optimization (BO), and progressive sampling to tune the hyperparameters of the RNN models. Modifications are proposed to the successive halving and progressive sampling algorithms for better efficiency on time series data. The ASH-HPO algorithm trains on smaller dataset subsets initially, then expands the training dataset progressively and adaptively adds or removes models along the process. In this paper, we use the ASH-HPO algorithm to optimize the hyperparameters of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and CNN-LSTM networks. We demonstrate the effectiveness of the ASH-HPO algorithm using a PCIe Gen 2 channel, a PCIe Gen 5 channel, and a PAM4 differential channel. We also investigate the effects of several settings and tunable variables of the ASH-HPO algorithm on its convergence speed. As a benchmark, we compared the ASH-HPO algorithm to three state-of-the-art HPO methods: BO, successive halving, and hyperband. The results show that the ASH-HPO algorithm converges faster than the other HPO methods on transient simulation problems.

This article presents formulations for the voltage-in-current (VinC) latency insertion method (LIM) for thin-film transistors (TFTs). LIM is a fast circuit simulation algorithm that solves circuits in a leapfrog manner, without requiring intensive matrix operations present in SPICE-based simulators. This allows LIM to have a far superior scaling with respect to the size of the circuit resulting in significant time savings on large circuit networks. The VinC LIM formulation for the TFTs written in this article has the benefit of a better stability compared to the original LIM formulation which allows the use of larger time steps. The performance of the new algorithm is demonstrated through the simulation of numerical examples of large flat-panel display (FPD) circuits. It is seen that VinC LIM greatly outperforms basic LIM and commercial SPICE-based simulators, where the presented algorithm is able to simulate circuits with more than 10 million nodes or devices in a reasonable time, which is not viable in many modern day SPICE-based simulators.

Artificial neural networks (ANNs) have been used to model microwave and RF devices over the years. Conventionally, S-parameters of microwave/RF designs are used as the inputs of neural network models to predict the electrical properties of the designs. However, using the S-parameters directly as inputs into the ANN results in a large number of inputs which slows down the training and configuration process. In this paper, a new method is proposed to first disassemble the S-parameters into poles and residues using vector fitting, and then the poles and residues are used as the input data during configuration and training of the neural networks. Test cases show that the ANN trained using the proposed method is able to predict the eye-heights and eye-widths of typical interconnect structures with minimal error, while showing significant speed improvement over the conventional method.

Smart canes are usually developed to alert visually challenged users of any obstacles beyond the canes' physical lengths. The accuracy of the sensors and their actuators' positions are equally crucial to estimate the locations of the obstacles with respect to the users so as to ensure only correct signals are sent through the associated audio or tactile feedbacks. For implementations with low-cost sensors, however, the users are very likely to get false alerts due to the effects from noise and their erratic readings, and the performance degradation will be more noticeable when the positional fluctuations of the actuators get amplified. In this paper, a multi-sensor obstacle detection system for a smart cane is proposed via a model-based state-feedback control strategy to regulate the detection angle of the sensors and minimize the false alerts to the user. In this approach, the overall system is first restructured into a suitable state-space model, and a linear quadratic regulator (LQR)-based controller is then synthesized to further optimize the actuator's control actions while ensuring its position tracking. We also integrate dynamic feedback compensators into the design to increase the accuracy of the user alerts. The performance of the resulting feedback system was evaluated via a series of real-time experiments, and we showed that the proposed method provides significant improvements over conventional methods in terms of error reductions.

In this work, a binaural model resembling the human auditory system was built using a pair of three-dimensional (3D)-printed ears to localize a sound source in both vertical and horizontal directions. An analysis on the proposed model was firstly conducted to study the correlations between the spatial auditory cues and the 3D polar coordinate of the source. Apart from the estimation techniques via interaural and spectral cues, the property from the combined direct and reverberant energy decay curve is also introduced as part of the localization strategy. The preliminary analysis reveals that the latter provides a much more accurate distance estimation when compared to approximations via sound pressure level approach, but is alone not sufficient to disambiguate the front-rear confusions. For vertical localization, it is also shown that the elevation angle can be robustly encoded through the spectral notches. By analysing the strengths and shortcomings of each estimation method, a new algorithm is formulated to localize the sound source which is also further improved by cross-correlating the interaural and spectral cues. The proposed technique has been validated via a series of experiments where the sound source was randomly placed at 30 different locations in an outdoor environment up to a distance of 19 m. Based on the experimental and numerical evaluations, the localization performance has been significantly improved with an average error of 0.5 m from the distance estimation and a considerable reduction of total ambiguous points to 3.3%.

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.

This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node’s translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches.

In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand’s side during the wireless transmission, the compensator is restructured into a Hammerstein−Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove’s and the bionic hand’s dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein−Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human−robot collaboration with the bionic hand.