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.
Automated Software Testing Using Metahurestic Technique Based on an Ant Colony Optimization
