A smart assistance system for cable assembly by combining wearable augmented reality with portable visual inspection

Background Assembly guided by paper documents is the most widespread type used in the process of aircraft cable assembly.This process is very complicated and requires assembly workers with high-level skills.The technologies of wearable Augmented Reality(AR)and portable visual inspection can be exploited to improve the efficiency and the quality of cable assembly.Methods In this study,we propose a smart assistance system for cable assembly that combines wearable AR with portable visual inspection.Specifically,a portable visual device based on binocular vision and deep learning is developed to realize fast detection and recognition of cable brackets that are installed on aircraft airframes.A Convolutional Neural Network(CNN)is then developed to read the texts on cables after images are acquired from the camera of the wearable AR device.An authoring tool that was developed to create and manage the assembly process is proposed to realize visual guidance of the cable assembly process based on a wearable AR device.The system is applied to cable assembly on an aircraft bulkhead prototype.Results The results show that this system can recognize the number,types,and locations of brackets,and can correctly read the text of aircraft cables.The authoring tool can assist users who lack professional programming experience in establishing a process plan,i.e.,assembly outline based on AR for cable assembly.Conclusions The system can provide quick assembly guidance for aircraft cable with texts,images,and a 3 D model.It is beneficial for reducing the dependency on paper documents,labor intensity,and the error rate.

Rapid and robust initialization for monocular visual inertial navigation within multi-state Kalman filter

Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter （MSCKF）. In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF （I-MSCKF） navigation method is proposed in the paper. Based on the trifocal tensor and sigmapoint filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.

In-process adaptive milling for large-scale assembly interfaces of a vertical tail driven by real-time vibration data

Assembly interfaces,the joint surfaces between the vertical tail and rear fuselage of a large aircraft,are thin-wall components.Their machining quality are seriously restricted by the machining vibration.To address this problem,an in-process adaptive milling method is proposed for the large-scale assembly interface driven by real-time machining vibration data.Within this context,the milling operation is first divided into several process steps,and the machining vibration data in each process step is separated into some data segments.Second,based on the real-time machining vibration data in each data segment,a finite-element-unit-force approach and an optimized space–time domain method are adopted to estimate the time-varying in-operation frequency response functions of the assembly interface.These FRFs are in turn employed to calculate stability lobe diagrams.Thus,the three-dimensional stability lobe diagram considering material removal is acquired via interpolation of all stability lobe diagrams.Third,to restrain milling chatter and resonance,the cutting parameters for next process step,e.g.,spindle speed and axial cutting depth,are optimized by genetic algorithm.Finally,the proposed method is validated by a milling test of the assembly interface on a vertical tail,and the experimental results demonstrate that the proposed method can improve the machining quality and efficiency of the assembly interface,i.e.,the surface roughness reduced from 3.2μm to 1.6μm and the machining efficiency improved by 33%.