In this paper, different state-of-the-art metaheuristic algorithms are analyzed to incorporate the collision free path planning approach for UAVs in disaster situation. The efficient path planning is used to identify the main cause of disaster like bushfires that are badly affecting the forest ecosystem throughout the globe. This novel approach is a first step towards a pre-disaster assessment and possibilities to save the survivors in minimal time. Different metaheuristic algorithms like PSO, GWO, WOA, BMO and DGBCO are compared for UAV path optimization capability. In order to test robustness of our proposed model, four different scenarios are presented which includes general environment, condense environment, maze environment and dynamic environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for a UAV to reach the destination. The exploration and exploitation groups working simultaneously in a dynamic environment makes it effective for UAV path planning using DGBCO algorithm. Based on the parameters selected, the DGBCO algorithm outperforms other algorithms and achieves 24.5% less transportation cost and 13.3% less computational time. Hence, DGBCO can be efficiently applied for UAV path planning optimization in any of the aforementioned environments.

Sustainable manufacturing remains a central objective of Industry 5.0. By successfully implementing harmonic human-robot teams in intelligent industrial systems, the efficiency and well-being of human workers can be increased. Achieving this requires a gradual approach from caged robots to advanced, seamless collaboration between humans and robots. Initially, that means transitioning to human-robot interaction (HRI) where there is an exchange of commands between the human and the robot. Further advancements within safety considerations, including collision avoidance through advanced machine vision, enable the exchange of workspace that defines human-robot collaboration (HRC). The next stage is physical HRC (pHRC) which requires safe and controlled exchange of forces through impedance and admittance control. Finally, this paper describes human-robot teaming (HRT), which is defined by the exchange of solutions as teammates. This is enabled by combining cutting-edge technologies such as digital twin (DT), advanced vision sensors, machine learning (ML) algorithms and mixed reality (MR) human–machine interfaces for operators. A key contribution of this work is reviewing the integration of HRT with DT and ML, highlighting how these technologies enable seamless perception, prediction, and decision-making in human-centric industrial systems. By reviewing these technologies, the paper highlights current challenges, limitations and research gaps within the field of HRT and suggests potential future possibilities for HRT, such as advanced disassembly of used goods for a more sustainable manufacturing industry.

The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Electromyography (EMG)-based gesture detection plays a crucial role in human-robot interaction (HRI), providing a seamless interface for controlling robotic systems through muscle activity. Despite its potential, EMG systems face significant challenges related to the security and privacy of sensitive biometric data, as well as the computational limitations of deploying deep learning models on edge devices. To address these issues, we propose a federated learning-based deep learning model for EMG gesture recognition, specifically designed for edge devices. Our model utilizes a custom dataset collected using a Mindrove 8-channel EMG armband, capturing eight distinct hand gestures—rest, move left, move right, move down, move up, open fingers, close fist, and twist hand—from 10 subjects with seven repetitions each, ensuring diverse and robust data for training. During preprocessing, a band-pass filter (50–450 Hz) was applied to remove noise and enhance signal quality, followed by a short-term frequency transform (STFT) with a 200 ms sample time and 50% overlap to extract relevant features from the EMG signals. The dataset was segmented into training and testing sets with a 70/30 split for evaluation. We evaluate several federated learning techniques, including FedAvg, FedProx, and FedSGD, demonstrating that FedAvg achieves the highest accuracy of 96.92% without quantization with Scenario 9 (15 epochs, 20 rounds) with minimal communication overhead. Additionally, our model is quantized, resulting in an 89% reduction in size and a high accuracy of 95.99%, representing a minimal loss of 0.93%, making it ideal for edge deployment without compromising performance. A comparative analysis with other deep learning models, such as Multi Convolutional Residual Networks (MCRN),Multi Convolutional Neural Networks (MCNN), Temporal Convolutional Networks (TCN), and InceptionNet, shows that our approach outperforms these models in both accuracy and efficiency. Experimental results validate the high accuracy of our model in both training/testing and real-time disaster scenario simulations using the Spot robot. The proposed solution provides a secure, efficient, and highly accurate framework for EMG-based gesture recognition on edge devices, ideal for human-robot interaction and assistive technologies such as in search and rescue operations.

Traditional surgical training faces challenges related to sustainability, cost, and ethical considerations. Limited access to resources, ethical concerns about cadaver-based learning, limited availability of surgical cases, and the high costs associated with hands-on training impede the effectiveness and accessibility of surgical education. Despite these challenges, the need for direct tactile experience remains critical, as it fosters the development of essential motor skills and tactile abilities vital for surgical practice. However, advancements in immersive technologies such as virtual reality (VR), augmented reality (AR), and haptics offer a promising solution to address these challenges. VR and AR technologies offer realistic, controlled environments where learners can visualise anatomical structures and practice surgical procedures without risks. The integration of haptic feedback in these immersive simulations further enriches the learning process by adding the sense of touch and physical interaction, aiding in mastering critical surgical skills. The incorporation of immersive technologies in surgical training presents an opportunity to address the constraints of conventional methods. These technologies offer the possibility of creating platforms that replicate real-life surgical scenarios, allowing learners to hone their skills and build confidence in a safe and controlled environment. Moreover, immersive learning offers an ethical alternative to cadaver-based training and extends access to training materials irrespective of geographical limitations. Such an approach allows for step-based training, where students may begin acquiring skills through immersive learning, then move to special surgical simulators, and finally to patients. This article deals with the impact of immersive technologies such as VR, AR, and haptics on reshaping conventional surgical training, by focusing specifically on basic surgical skills. Targeting educators, medical experts, and involved parties, the article aims to stimulate additional exploration and adoption of immersive technologies to elevate surgical competencies and patient care standards.