An improved RRT-Connect path planning algorithm of robotic arm for automatic sampling of exhaust emission detection in Industry 4.0

Abstract

In the time of industrial 4.0 era represented by intelligent manufacturing, as the most widely used equipment in the industrial robots filed, robotic arms can replace people for a large number of exhaustive, repetitive and harmful work. In the process of vehicle exhaust emission detection, manual exhaust gas sampling is required, which is inefficient and easily harmful to the human body. Using a robotic arm in the detection scene can improve the detection efficiency and prevent the exhaust gas from affecting the human body. In this paper, an improved RRT-Connect path planning algorithm is proposed to realize a reliable path design required for automatic sampling control of exhaust emission detection. For the problems of blind expansion, low efficiency in the RRT and its improved algorithm, an improved RRT-Connect algorithm through setting the adaptive step strategy and using the fixed sampling function to construct four random trees from the starting point, the end point and the fixed point for search is proposed in this paper, which can solve the problem of slow expansion and speed up convergence. Experimental results show that compared with the unimproved RRT-Connect algorithm, the improved algorithm decreases the number of iterations by 18.11% and reduces the number of path nodes by 23.02%, which meets the path planning requirements of automatic sampling control of the robotic arm.