Robotic binding of rebar based on active perception and planning

Abstract In the construction industry, rebar crosspoints binding relies heavily on manual work, which has become the bottleneck of construction-efficiency improvement. In this study, a full-automatic robot system, based on active perception and planning, is developed to realize the automation of the rebar crosspoints binding process. Based on the preprocessed image from an RGBD camera, a neural network method is proposed to recognize the rebar crosspoints. An active planning method to traverse the rebar plane is designed in the results of crosspoints recognition. Experiment results show that the rebar crosspoints recognition method has high accuracy (the detection accuracy is more than 89% and the classification accuracy is more than 98%). Experiments in realistic scenarios show that the robot system can traverse the rebar plane and bind the rebar crosspoints automatically to reduce labor costs. In the future, the robot system will work in curved environments and have higher detection accuracy.