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 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 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.