Shuxiang Guo

Bio: Shuxiang Guo is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Robot & Spherical robot. The author has an hindex of 43, co-authored 736 publications receiving 9075 citations. Previous affiliations of Shuxiang Guo include Mie University & University of Toronto.

A new type of fish-like underwater microrobot

Abstract: This paper presents a new prototype model of an underwater fish-like microrobot utilizing ionic conducting polymer film (ICPF) actuator as the servo actuator to realize swimming motion with three degrees of freedom. A biomimetic fish-like microrobot using ICPF actuator as a propulsion tail fin and a buoyancy adjuster for the swimming structure in water or aqueous medium is developed. The overall size of the underwater prototype fish shaped microrobot is 45 mm in length, 10 mm in width, and 4 mm in thickness. It has two tails with a fin driven respectively, a body posture adjuster, and a buoyancy adjuster. The moving characteristic of the underwater microrobot is measured by changing the frequency of input voltage from 0.1-5 Hz in water and the amplitude of input voltage from 0.5-10 V. The experimental results indicate that changing the amplitude and frequency of input voltage can control the swimming speed of proposed underwater microrobot.

Micro active catheter system with multi degrees of freedom

Abstract: In this paper, we propose a new prototype model of serial-parallel type of active micro catheter (MAC) with two bending degrees of freedom. It is 4 Fr, 5 Fr and 6 Fr (1 Fr=1/3 mm) in diameter and consist of 3 active units incorporating SMA wires in lumina as the servo actuator. The bending motion, bending direction and bending angle of the MAC have been measured by application of electricity in air and in physiological saline solution. We also modeled this MAC. Experimental results show that the model of the MAC is reasonable for practical applications. By using simulators (conditions similar to a body cavity), we carried out the simulation experiments of "in vitro" and "in vivo". The experimental results indicate that the proposed MAC is applicable to intracavity operations. >

A novel robotic catheter system with force and visual feedback for vascular interventional surgery

Abstract: This paper proposes a novel master-slave robotic catheter operating system with force feedback and visual feedback for vascular interventional surgery (VIS). The robotic catheter system has good manoeuvrability, it can transmit the surgeon’s skill to insert and rotate the catheter and avoids danger during VIS using force and visual feedback. In addition, it can be used to train unskilled surgeons to perform VIS. We performed a simulation experiment to validate our system using an endovascular evaluator (EVE). The experimental results demonstrated that the stability and response of the system were good. The robotic catheter system is suitable for performing VIS.

Safety Operation Consciousness Realization of a MR Fluids-Based Novel Haptic Interface for Teleoperated Catheter Minimally Invasive Neurosurgery

Abstract: In catheter minimally invasive neurosurgery (CMINS), catheter tip collision with the blood vessel detection during the surgery practice is important. Moreover, successful CMINS is dependent on the discrimination of collision by a skilled surgeon in direct operation. However, in the context of teleoperated scenario, the surgeon was physically separated. Therefore, the lack of haptic sensation is a major challenge for a telesurgery scenario. A human operator-centered haptic interface is adopted to address this problem. In this paper, a teleoperated robotic-assisted surgery and psychophysics-based safety operation consciousness theory was presented. Moreover, a human operator-centered haptic interface design concept is first introduced into actuator choice and design. A semiactive haptic interface was designed and fabricated through taking full advantage of MR fluids. Furthermore, a mechanical model (force/torque model) was established. In addition, in case of no collision, transparency of a teleoperated system was realized; in case of collision, psychophysics-based collision discrimination control scheme was first presented to provide safety operation consciousness. Experiments demonstrate the usability of the designed haptic interface and correctness of the safety operation consciousness control scheme.

Micro catheter system with active guide wire

Abstract: In this paper, we propose a new prototype model of micro-catheter with active guide wire that has two bending degrees of freedom. The design and fabrication methods of this micro active catheters (MAC) are described. Prototype models are 3Fr, 4Fr, 6Fr (1Fr=1/3 mm) in diameter and consist of catheter tube and active guide wire with ionic conducting polymer film actuator on its front end as the servo actuator. The bending characteristics of the MAC have been measured by application of electricity in physiological saline solution. We also modeled this MAC for characteristic evaluation. Experimental results show that the model of the active catheter is reasonable for practical applications. By using simulators (whose conditions are similar to those of a body cavity), we also carried out simulation experiments "in vitro". The experimental results indicate that the proposed MAC is applicable to intracavity operations.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.