A New Measurement Method of Real-time Pose Estimation for an Automatic Hydraulic Excavator

Construction machinery automation has drawn more and more research interests in recent years. To realize the automatic operation of a hydraulic excavator, a key technology is the effective pose estimation of its multiple actuators. In this work, a new measurement method of real-time pose estimation is developed by using machine vision and artificial neural network (ANN) to overcome the limitations of existing methods. A high-resolution camera is installed on the excavator cab to capture the pose image with a constant view angle even when the excavator swings. Images are processed in hue, saturation and value space to extract the centroid pixel coordinate (CPC) of the markers fixed on the actuator joints. Taking the centroids as featured points, the mapping relations between the pixel coordinates and the joint angles are derived by training the ANN. A number of experiments are implemented under various conditions and the results show that the proposed method has good real-time performance and robustness. This work can provide basis for the feedback control of the actuators in the excavator.