Browsing by Author "Kangwagye, Samuel"

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    A factory-level data-informed roadmap to industry 4.0 for low digital maturity steel manufacturing plants
    (International Journal of Advanced Manufacturing Technology, 2026-04-25) Kangwagye, Samuel; Ssempijja, Maureen Nalubowa
    This paper presents a factory-level, data-informed Industry 4.0 readiness and upgrade framework for steel manufacturing plants operating in low digital maturity environments, using Uganda as a representative case. Field data was collected at three medium-scale operational steel plants. Customized Digital Maturity Index (DMI) and cybersecurity risk (CSR) assessment criteria were developed and applied. Results show an overall DMI score of 1.8, indicating very low digital maturity with predominantly manual operations, absence of industrial robots, advanced automation, and integrated digital data systems. CSR assessment results show limited formal protection mechanisms. A phased, ROI-driven transition roadmap is proposed. A worked case using plant-level production data demonstrates that selective automation of four bottleneck stations could nearly double annual billet output and achieve an incremental payback period of approximately seven weeks under stated assumptions. Workforce transition modeling using a Markov approach indicates gradual role transformation over an expected horizon of about ten transition cycles rather than abrupt displacement.
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    Development of an integrated estimation and predictive control framework for safe navigation in mobile robots for industrial environments
    (Intelligent Service Robotics, 2026-02-09) Gnimady, Gilchrist R. S.; Murimi, Evan; Muchiri, Anthony K.; Kangwagye, Samuel
    The increasing integration of mobile robots in industrial environments has raised critical safety concerns, particularly in shared workspaces with human operators. Effective collision avoidance is essential to prevent accidents and enable smooth navigation in dynamic and unpredictable settings. This paper presents the development of an integrated estimation and predictive control framework for safe navigation in mobile robots for industrial environments. Here, safe navigation refers to improved, accurate motion of the robot (i.e., trajectory tracking, positioning, speed control) and enhanced collision avoidance (i.e., safe navigation around obstacles and humans). The estimation algorithm integrates data from LiDAR, RADAR, and IMU sensors using an Extended Kalman Filter (EKF) to overcome limitations such as LiDAR blind spots and IMU drift. The output of this algorithm is used as input to the MPC, which embeds an obstacle avoidance function directly into its cost function to enable proactive and adaptive collision avoidance. The complete framework is validated through both Gazebo-based simulations and real-world experiments in free-space navigation and navigation through obstacle-rich industrial environments. The results demonstrate that the robot can track reference trajectories while safely avoiding static and dynamic obstacles, including those located within sensor blind zones. The robot consistently maintains safe distances without unnecessary stops, achieving a balance between safety and operational efficiency. The framework offers a cost-effective alternative to high-resolution 360-degree perception systems and is well-suited for deployment in dynamic industrial workspaces.