Research on target recognition of tea and water delivery robot based on convolutional neural network

This study proposes a target recognition system for tea and water delivery robot based on convolutional neural network, which can efficiently identify tea cups, water sources and users in complex service scenarios. The system combines lightweight convolutional neural network model with multi-sensor fusion technology to achieve high-precision target recognition and meet real-time requirements. In terms of hardware design, constant temperature protection mechanism and multi-directional buffer tray are integrated to ensure stability during transportation and effectively reduce tea spillage. The experiment verifies the high task completion rate and path planning accuracy of the system in static restaurants, dynamic families and mixed scenarios. Through the visualization analysis of three-dimensional surface maps and heat maps, the influence of environmental factors on system performance is revealed, providing a basis for optimization. The system significantly improves the adaptability and efficiency of robots in service scenarios.