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To ensure that the delivery robot can safely and stably deliver experimental samples to the designated location, the FA-A * optimization algorithm is used to optimize and design the control system of the experimental sample delivery robot from both hardware and software aspects. Modify equipment components such as pose sensors, data processors, motor drivers, and controllers for the delivery robot, adjust the connection method of the system circuit, and complete the optimization of the hardware system. Build a mobile environment model for the delivery robot using the grid method, and identify the specific location of the experimental sample delivery object through image acquisition, feature extraction, and feature matching. Starting from the current position of the experimental sample and ending at the delivery terminal, the FA-A * optimization algorithm is used to plan the robot"s delivery path. Combined with the real-time tracking results of the robot"s posture, the robot"s control amount is calculated. Finally, the control function of the delivery robot is achieved from aspects such as position/speed, balance, and autonomous elevator riding. Through system testing experiments, it was concluded that, compared to traditional control, the position and speed control errors of the delivery robot were reduced by approximately 14m and 0.38m/s respectively under the optimized design system control, combining static and dynamic obstacle experimental scenarios.