Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial settings, such as manufacturing plants. Existing speech enhancement solutions primarly rely on advanced digital signal processing techniques, deep learning methods, or complex software optimization techniques. This paper introduces a novel enhancement strategy that incorporates a physical optimization stage by dynamically modifying the geometry of a microphone array to adapt to changing acoustic conditions. A sixteen-microphone array is mounted on a robotic arm manipulator with seven degrees of freedom, with microphones divided into four groups of four, including one group positioned near the end-effector. The system reconfigures the array by adjusting the manipulator joint angles to place the end-effector microphones closer to the target speaker, thereby improving the reference signal quality. This proposed method integrates sound source localization techniques, computer vision, inverse kinematics, minimum variance distortionless response beamformer and time-frequency masking using a deep neural network. Experimental results demonstrate that this approach outperforms other traditional recording configruations, achieving higher scale-invariant signal-to-distortion ratio and lower word error rate accross multiple input signal-to-noise ratio conditions.

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