With the advent of robots combined with artificial intelligence, robots have become an indispensable part of production and life. Especially in recent years, the collaboration between humans and machines has become a research trend in the field of robotics, with high work efficiency and flexibility. The advantages of safety and stability make intelligent robots the best choice for the current industrial and service industries with high labor intensity and hazardous working environments. This paper is aimed at studying the software design of an intelligent sensor robot system based on multidata fusion. In this paper, through the needle robot’s high precision requirements and the problem of fast response, a path design method based on the ant colony optimization (ACO) algorithm is proposed. Path planning is performed by intelligent robots for obstacle avoidance experiments, while global optimization is performed by the ant colony optimization (ACO) algorithm. For adaptive functions including obstacle reduction and path information length, the safest and shortest path is finally achieved through the ant colony optimization (ACO) algorithm. The experimental results show that using the ant colony optimization algorithm to perform simulation experiments and preprocessing operations on the data collected by the sensor can improve the accuracy and effectiveness of the data. The ant colony algorithm performs fusion and path planning, and on the basis of ensuring accuracy, it can speed up the convergence speed. Through the data analysis of obstacle avoidance experiments of intelligent robots, it can be concluded that it is very necessary for intelligent robots to install ultrasonic sensors and infrared sensors in obstacle avoidance, because the error between the test distance of the ultrasonic sensor and the infrared sensor and the actual distance is 0.001.

中文翻译：

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基于多数据融合的智能传感机器人系统软件设计研究

随着机器人结合人工智能的出现，机器人已经成为生产生活中不可或缺的一部分。尤其是近年来，人机协作成为机器人领域的研究趋势，工作效率高，灵活性强。安全稳定的优势使智能机器人成为当前劳动强度大、工作环境危险的工业和服务行业的最佳选择。本文旨在研究一种基于多数据融合的智能传感器机器人系统的软件设计。本文针对针型机器人精度要求高、响应速度快的问题，提出了一种基于蚁群优化（ACO）算法的路径设计方法。路径规划由智能机器人进行避障实验，全局优化由蚁群优化（ACO）算法进行。对于包括障碍物减少和路径信息长度在内的自适应函数，最终通过蚁群优化（ACO）算法实现最安全最短路径。实验结果表明，利用蚁群优化算法对传感器采集的数据进行模拟实验和预处理操作，可以提高数据的准确性和有效性。蚁群算法进行融合和路径规划，在保证精度的基础上，可以加快收敛速度​​。通过智能机器人避障实验的数据分析，