Precision robotic initial screw insertion is frequently challenged by visual occlusions and dynamic force disturbances, which can degrade trajectory estimation accuracy and assembly reliability.
This paper proposes a dual-channel robust particle filter integrating visual and force feedback for re-al-time trajectory estimation. Stage-aware motion-continuity prediction, force-conditioned particle proposal, spatial attraction constraints, and Huber-based residual modelling are introduced to improve robustness under visual occlusion and uncertain contact.
Simulation and physical experiments show that the proposed method reduces 3D trajectory RMSE by 23.0% compared with single-channel particle filtering and by 27.6% compared with the extended Kalman filter, while improving insertion stability, contact compliance, and trajectory estimation accu-racy under visual occlusion and dynamic disturbances.
The proposed framework combines vision–force fusion, stage-aware particle propagation, and adap-tive robust estimation to provide an effective solution for high-precision robotic assembly under oc-clusion and uncertain contact conditions.
