The capabilities of collaborative robotics have transcended the conventional abilities of decentralised robots as it provides benefits such as scalability, flexibility and robustness. Collaborative robots can operate safely in complex human environments without being restricted by the safety cages or barriers that often accompany them. Collaborative robots can be used for various applications such as machine tending, packaging, process tasks and pick and place. This paper proposes an improvement of the current virtual impedance algorithm by developing an adaptive virtual impedance controlled mobile multi-robot system focused on dynamic obstacle avoidance with a controlled planar movement. The study includes the development of a mobile multi-robot platform whereby each robot plans a path individually without a supervisor. The proposed system would implement a two-layered hierarchy for robot path planning. The higher layer generates a trajectory from the current position to the desired position, and the lower layer develops a real-time strategy to follow the generated trajectory while avoiding static and dynamic obstacles. The key contribution of this paper is the adaptive virtual impedance controller for a multi-robot system that will maintain movement stability and improve the motion behaviour in a dynamic environment.

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use.

Vibrotactile displays can substitute for sensory channels of individuals experiencing temporary or permanent impairments in balance, vision, or hearing, and can enhance the user experience in professional or entertainment situations. This massive range of potential uses necessitates primary research on human vibrotactile perception. One leading aspect to consider when developing such displays is how to develop haptic patterns or symbols to represent a concept. In most settings, individual patterns are sorted as alphabets of haptic symbols which formulate tactons. Tactons are structured and perceivable tactile patterns (i.e., messages) that transfer information to users by employing the sense of touch. Hence, haptic patterns are critical when designing vibrotactile displays, as they not only affect the rate of information transfer but also determine the design of the displays (e.g., the number and the placement of tactors engaged) and how the information is encoded to achieve separability. Due to this significance, this paper presents an overview study on the cutaneous perception parameters (i.e., intensity, loci, frequency, duration, illusions, and combinations of these) for designing haptic symbols to identify mutual best-practices and knowledge gaps for future work. The study also provides developers from different scientific backgrounds with access to complex notions when engaging this specialized topic (i.e., the use of cutaneous perception parameters towards information transfer). Finally, it offers recommendations on defining which parameters to engage for a specific task or pattern.

The global poultry industry is a critical sector, tasked with meeting the increasing demand for animal protein. Despite its growth and efficiency, it faces challenges, including enhancing productivity, optimizing resource utilization, and ensuring animal welfare. Addressing these challenges requires innovative solutions to improve both the efficiency and sustainability of poultry production. This paper presents an in-depth analysis of how advancements in artificial intelligence (AI) and machine learning (ML) technologies are being integrated into poultry farming to revolutionize its practices. We explore the application of AI in monitoring systems, smart poultry houses, and automated management practices that significantly enhance production metrics and animal welfare. Our study delves into various AI-driven methods, such as predictive modelling, real-time environmental monitoring, and precision feeding systems. Furthermore, the research identifies the current limitations and future potential of these technologies in facilitating a shift towards more responsive and responsible poultry farming practices. Our findings suggest that embracing AI technologies not only contributes to the economic viability of poultry farms but also aligns with ethical standards and sustainability goals, indicating a promising direction for the future of poultry farming.

The paper comprises of a design of a Load Monitoring System (LMS) for Launch and Recovery Systems (LARS). The LARS is designed to deploy and retrieve divers from and to the host vessel in offshore operations. LARS are utilized by offshore companies to enable divers to execute work at depths up to 300 meters. LARS in the market rely on human interface and monitoring systems that do not compensate for system errors and dynamic loads experienced by the LARS. Current systems are still subject to loss of power, control system errors, electrical and mechanical failures. Present systems rely heavily on the mechanical structure to compensate for most of the errors. Improving these factors, will result in the reduction of costs and increase of the overall life cycle. This project will include real-time load monitoring system analysis of the dynamic loads applied to a Bell’s LARS.

The Performance Efficiency Factor (PEF) is a key composite metric in broiler production that integrates livability, average body weight, feed conversion ratio (FCR) and age. While tree-based machine learning models have shown promising results for live-weight prediction, they often struggle with temporal dependencies and sparse mortality data. This study evaluates five tree-based algorithms, Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Category Boosting (CatBoost) on daily sensor data from commercial broiler farms. Data were stratified into six age categories, pre-processed with outlier retention and feature engineering, and assessed using R², RMSE, MAE, paired t-tests for statistical significance and SHapley Additive exPlanations (SHAP) for explainability. Cross-farm validation was performed to assess generalizability. The models achieved high accuracy for live-weight prediction (R² up to 0.97, RMSE as low as 9.23 g in mid-cycle categories), with Random Forest and LightGBM performing best. Mortality prediction remained challenging (R² −1.63 to 0.53) due to its sparse and stochastic nature. Nevertheless, Day 21 to Day 28 PEF forecasts showed relative errors of only 4.42–5.40%, as live-weight predictions dominated the PEF calculation. SHAP analysis consistently identified bird age, feed intake per bird and temperature as the most influential predictors. Tree-based models offer a robust, interpretable and computationally efficient solution for live-weight and PEF forecasting in commercial broiler production. The findings support proactive farm management and highlight the need for hybrid approaches to improve mortality prediction.