Friction modeling and compensation for haptic master manipulator based on deep Gaussian process

This paper investigates friction modeling and compensation for haptic master manipulator used in robot-assisted minimally invasive surgical system. Friction modeling and compensation is based on deep Gaussian process (DGP) and it does not require the utilization of explicit friction models. Therefore, the proposed friction modeling and compensation algorithm can circumvent the drawbacks associated with model-based methods. Through the adoption of implicit posterior variational inference, the proposed algorithm can accurately predict and compensate friction even when there exists parametric uncertainty in the dynamics of the haptic master manipulator. The effectiveness and feasibility of the proposed approach is validated experimentally through the robot-assisted minimally invasive surgical system. Experimental results demonstrate that the proposed method outperforms several existing alternatives in representing and compensating friction effects.

Automated summary

This paper investigates friction modeling and compensation for haptic master manipulator in a robot-assisted minimally invasive surgical system. Based on deep Gaussian process (DGP), the proposed algorithm can accurately predict and compensate friction without the need for explicit friction models, thus overcoming drawbacks associated with model-based methods. Through implicit posterior variational inference, the algorithm can effectively handle parametric uncertainty in the dynamics of the haptic master manipulator, demonstrating superior performance in representing and compensating friction effects compared to existing alternatives.