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This paper aims to develop a novel miniature 3-axis force sensor which can detect the interaction forces during tissue palpation in MIS (minimally invasive surgery). MIS offers many significant merits compared with traditional open surgery, the wound to the patients and the postoperative pains are alleviated and reduced dramatically. However, the inherent drawback due to lack of force feedback still exists while conducting some operation procedures. For example, tissue palpation performed easily during open surgery could not be realized in an MIS manner.

The force sensor is based on the resistive-based sensing method that utilizes strain gauges to measure the strain when the external loads are acting on the tip of the sensor. A novel flexible tripod structure with bending and compression deformations is designed to discriminate the magnitudes and directions of the three orthogonal force components. A linear characteristic matrix is derived to disclose the relationship between the sensitivity and the geometric parameters of the structure, and a straightforward geometric parameterized optimization method considering the sensitivity isotropy is proposed to provide the sensor structure with high sensitivity and adequate stiffness.

The sensor prototype can perform force measurement with sensing ranges of ± 3.0 N in axial direction and ± 1.5 N in radial direction, and the resolutions are 5 per cent and 1 per cent, respectively. It is concluded that this force sensor is compatible with MIS instruments and the ex-vivo experiment shows that the sensor can be used to perform tissue palpation during MIS procedures.

This paper is intended to address the significant role of force sensing and force feedback during MIS operations, and presents a new application of the resistive-based sensing method in MIS. A tripod structure is designed and a straightforward optimization method considering the sensitivity isotropy of the sensor is proposed to determine geometric parameters suited for the given external loads.

The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control.

This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments.

The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture.

The proposed method can effectively solve the occlusion problem in visual servo control.