Vision detection and path planning of mobile robots for rebar binding

Abstract

Focused on the problems of cumbersome operation, low efficiency, and high cost in the traditional manual rebar binding process, we propose a mobile robot vision detection and path-planning method for rebar binding to realize automated rebar binding by combining deep learning and path-planning technology. A MobileNetV3-SSD rebar binding crosspoints recognition model is built based on TensorFlow deep learning framework, and a crosspoints localization method combining control factor α and feature projection curve is introduced to achieve the localization of unbound crosspoints. In addition, A back-and-forth path-planning algorithm with priority constraints combined with dead zone escape algorithm based on improved A* is proposed to achieve complete coverage path planning of the working area and path transfer of the dead zone. In the field test of the robot prototype, the classification accuracy and localization accuracy reached 94.40% and 90.49%, and the robot was able to reach complete coverage path planning successfully. The experimental results show that the visual detection method can achieve fast, noncontact and intelligent recognition of rebar binding crosspoints, which has good robustness and application value. At the same time, the proposed path-planning method has higher efficiency in the execution of robot complete coverage path planning, and meets the basic requirements of path planning for rebar binding process.