Grip Force Estimation of Laparoscope Surgical Robot based on Neural Network Optimized by Genetic Algorithm

Huang, Junfei; Yan, Zhiyuan; Xue, Renfeng

doi:10.1145/3265639.3265660

Cited by 4 publications

(1 citation statement)

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“…It was difficult to learn and predict the interaction force using such many sequential and variational images in real time, due to the camera movement during the surgery operation. Huang et al proposed a method for clamping force estimation based on a neural network for a cable-driven surgical robot [36]. The results showed the training process and training errors, but not considered the generalization ability verification.…”

Section: Introductionmentioning

confidence: 99%

A Clamping Force Estimation Method Based on a Joint Torque Disturbance Observer Using PSO-BPNN for Cable-Driven Surgical Robot End-Effectors

Wang

Chen

et al. 2019

Sensors

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The ability to sense external force is an important technique for force feedback, haptics and safe interaction control in minimally-invasive surgical robots (MISRs). Moreover, this ability plays a significant role in the restricting refined surgical operations. The wrist joints of surgical robot end-effectors are usually actuated by several long-distance wire cables. Its two forceps are each actuated by two cables. The scope of force sensing includes multidimensional external force and one-dimensional clamping force. This paper focuses on one-dimensional clamping force sensing method that do not require any internal force sensor integrated in the end-effector’s forceps. A new clamping force estimation method is proposed based on a joint torque disturbance observer (JTDO) for a cable-driven surgical robot end-effector. The JTDO essentially considers the variations in cable tension between the actual cable tension and the estimated cable tension using a Particle Swarm Optimization Back Propagation Neural Network (PSO-BPNN) under free motion. Furthermore, a clamping force estimator is proposed based on the forceps’ JTDO and their mechanical relations. According to comparative analyses in experimental studies, the detection resolutions of collision force and clamping force were 0.11 N. The experimental results verify the feasibility and effectiveness of the proposed clamping force sensing method.

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Section: Introductionmentioning

confidence: 99%