Lara Laban - Academia.edu (original) (raw)
Lara Laban is a Autonomous Systems Engineer at Collins Aerospace in the Vehicle Autonomy group. With a profound background in automatic control, robotics, and computational engineering, Lara contributes to cutting-edge developments in UAV technology, focusing on autonomy and control systems.
Lara has a robust academic and professional trajectory. Prior to joining Collins Aerospace, she was a WASP Affiliated PhD Student at Lund University, Sweden, where she worked on DJI UAVs.
Address: Cork, Ireland
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Papers by Lara Laban
ArXiv, 2024
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems t... more Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.
ArXiv, 2024
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems t... more Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.