Prof. Jiyao Wang Profile

Prof. Jiyao Wang

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Proceedings Article | 29 January 2024 Paper

A vision-based tactile sensor with single contact surface for pressure and slippage estimation

Jian Feng, Shuhua Fang, Jiyao Wang

Proceedings Volume 12984, 129840C (2024) https://doi.org/10.1117/12.3021812

KEYWORDS: Sensors, Optical flow, Image processing, Cameras, Image sensors, Error analysis, Polydimethylsiloxane, Tunable filters, Deformation, Binary data

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Touch is an important way that a human being senses the surrounding environment, but in robotic applications, it is difficult to obtain static and dynamic tactile signals simultaneously. In this paper, we proposed a new vison-based tactile sensor to estimate pressure and slippage distance at the same time. The sensor recognizes the deformation degree of elastomer through image processing, and the pressure is estimated according to the radius of contact region. Sensor captures the surface of the contact object and tracks feature point to calculate optical flow. Then the slippage distance is estimated by Kalman filtering and integration of the optical flow. The sensor is realized in a small package, so it can be useful in wide range of scenarios. Here we also built a two-dimensional experimental platform to test the sensor, and the experimental results show that the average error of pressure estimation is 6.4%, and the average error of slippage estimation is 14.4% at 5mm/s, demonstrating a good sensing performance for providing pressure and slippage via the single contact surface.

Proceedings Article | 31 May 2023 Paper

An improved feature point-based LK optical flow algorithm for robotic arm control

Xingrui Shi, Wei Xu, Jiyao Wang

Proceedings Volume 12704, 127043G (2023) https://doi.org/10.1117/12.2680052

KEYWORDS: Optical flow, Detection and tracking algorithms, Optical tracking, Robotics, Robot vision, Corner detection, Target detection

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Robot vision technology has been widely used in many fields since its emergence, and real-time control of robotic arm control systems has long been a challenging problem. When using optical flow algorithms for tracking in conventional vision control, its accuracy and large movements are affected by the size of the integration window to track fast-moving objects, making it difficult to achieve real-time control in robotic arm vision control. By using a smaller integration window, the Pyramid Lucas-Kanade (LK) method can solve the problem that large motions cannot be tracked, but the basic Pyramid LK method is not very accurate. Therefore, an improved LK optical flow method is proposed for the practical application of the traditional LK method for robotic arm control with poor real-time performance and accuracy, applying a combination of the improved FAST (Features from Accelerated Segment Test) corner point detection and the pyramid LK optical flow algorithm. With the improved FAST algorithm, the corner points with the strongest grey-scale variations can be extracted quickly. This approach allows better estimation of optical flows with strong corner points to track moving objects. The corner points calculated by applying the improved FAST feature point detection are first used as candidate feature points; then the candidate feature points are re-extracted by setting filtering conditions with the information obtained from the robotic arm; and finally, the target feature points are tracked using the LK optical flow method.

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