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To address the challenges of inadequate visual recognition accuracy, sluggish path planning, and imprecise trajectory tracking that contribute to picking failures in multi-degree-of-freedom robotic arms, this paper introduces a visually guided trajectory planning and control strategy specifically designed for six-axis robotic manipulators. The proposed approach emphasizes effective obstacle avoidance and precise target tracking within complex and dynamic environments. The strategy is initiated by integrating the YOLOv8 deep learning-based visual detection model, which ensures rapid and accurate target localization on the pixel plane, thereby enabling crucial real-time object recognition. The visually detected targets are subsequently utilized to steer an enhanced RRT*-connect algorithm, wherein path search efficiency is significantly improved by implementing adaptive step size and intelligent sampling strategies, thereby minimizing the computational burden and the number of sampling nodes. To further optimize the planned trajectories, B-spline interpolation is employed to achieve smooth, continuous, and stable motion paths. In the final stage, a sliding mode controller integrated with Model Predictive Control (MPC) principles is developed to ensure precise and robust trajectory tracking. This controller exhibits a high degree of responsiveness, effectively adapting to dynamic changes and maintaining accurate path following despite the presence of external disturbances. Comprehensive simulations and experimental validations underscore the robustness and efficiency of the proposed methodology, highlighting substantial improvements in reducing the number of sampling nodes, accelerating convergence rates, enhancing path smoothness, and achieving superior tracking accuracy, making it well-suited for high-performance trajectory tracking tasks in complex and dynamic scenarios.
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