During certain human-robot collaboration tasks, the operator interacts with the robot by hand guidance to adjust the end-effector pose for spatial operations. The rotational operation is less intuitive to humans than translation. In fact, imagining the path to the target orientation is more challenging. In the literature related to control strategies for robot manual guidance, it is usually proposed to control translation and rotation independently. Our research explored and quantified the factors that influence operational intuition. A Virtual Fixture spatial guidance framework with intuition maintenance is proposed. This novel guidance scheme enables operators to effortlessly and simultaneously control both orientation and position in an intuitive way. High operation precision and efficiency can be achieved without interfering with the main task by exploring the null space with constraint optimization.

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

Prostate boundary detection from ultrasound images plays a key role in prostate disease diagnoses and treatments. Due to the poor quality of ultrasound images, however, this still remains as a difficult task. Currently, boundary detection are performed manually, which is arduous and heavily user dependent. This paper presents a new approach derived from level set method to semiautomatically detect the prostate surface from 3D transrectal ultrasound images. In this method, a few initial bubbles are simply specified by the user from five particular slices based on the prostate shape. When bubbles evolve, they expand, shrink merge and split, and finally produce the desired prostate surface. To remedy the "boundary leaking" problem caused by gaps or weak boundaries, both region information and statistical intensity distribution are incorporated into the model. We applied the proposed method to eight 3D TRUS images and the results have shown its effectiveness.