Due to their location, malignant brain tumors are one of humanity's greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is time-consuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems.

Sanding is a common yet important task in the manufacturing of many wooden objects (e.g. furniture, decoration box), where the coated layer attached on objects is removed after the interaction with sanding belts. Existing sanding operation is heavily dependent on manual works, which is highly labor-intensive and with the low consistency of quality, and the issues of safety and health also arise after continuous working in the noisy and dusty environment. To deal with the aforementioned, this paper presents the development of a new autonomous sanding robot. The autonomous capability of the developed robot is reflected in the whole procedure of sanding. In particular, the CAD model of the target object is automatically constructed with the structured-light technology, and the sanding behavior on the target surface is self-regulated under the desired impedance model. Such feature makes the robot capable of working towards uncertain objects with minimum human involvement. The proposed impedance controller has the model-free advantage, by using the adaptive neural networks (NNs) to compensate the uncertain dynamics and the unknown disturbances online. The stability of the closed-loop system is rigorously proved with Lyapunov methods, and experimental results on different objects are presented to validate the performance of the developed robot. The implementation of the developed robot can systematically address the problems associated with manual works. Note to Practitioners—The current working environment of sanding is not healthy or safe to humans, due to the nature of noise, dust, high-speed sanding belt. By controlling the robot to autonomously perform sanding tasks can keep humans away from such environment and hence systematically address the issues of health and safety. This paper presents a new impedance control method for sanding robot. In impedance control, the control goal is specified as a dynamic relationship between the contact position and the interaction force. In this paper, the contact position is determined by using the 3D vision sensor, while the interaction force is regulated by referring to the human experience. In addition, the influence caused by unmodeled factors (e.g. unknown dynamics of the sanded object) is also dealt with by using the techniques of NNs. Such setting can effectively guarantee the sanding quality and also avoid the physical damage to the sanded object. Therefore, it lays the foundation for the autonomous robotic sanding.

3D optical sensors are becoming more and more popular in vision guidance of industrial robots and other industrial automation application. Although research on high-speed 3D shape measurement (or imaging) has experienced tremendous growth over the past decades, simultaneously achieving highspeed and high-accuracy performance remains a major challenge in industrial practice. This paper presents a new FPGA architecture for the PMP algorithm and designs an high-speed embedded binocular structured light 3D measurement sensor. The sensor mainly contains a DLP projector, two cameras and a Xilinx Zynq-7000 UltraScale which make real-time computation possible. Experiments showed the time consuming of processing a set of 9 × 2 images with a resolution of 2048 × 1536 using the sensor proposed in this paper is 32 ms that has a better performance than 255ms on GPU. To the best of our knowledge, this is the first high -speed FPGA-based embedded binocular structured light 3D measurement sensor. This proposed 3D sensor can obtain great quality 3D information in real time, it can be widely applied to robot random bin-picking and other industrial applications.

Despite successful clinical applications, teleoperated robotic surgical systems face particular limitations in the functional endoscopic sinus surgery (FESS) in terms of incompatible instrument dimensions and robot set-up. The endoscope remains manually handled by an assistant when the surgeon performs bimanual operations. This paper introduces the development of the Foot-controlled Robot-Enabled EnDOscope Manipulator (FREEDOM) designed for FESS. The system features clinical considerations that inform the design for providing reliable and safe endoscope positioning with minimal obstruction to the routine practice. The robot structure is modular and compact to ensure coaxial instrument manipulation through the nostril for manual procedures. To avoid rigid endoscope motions, a new compliant endoscope holder is proposed that passively limits the lens-tissue contact forces under collisions for patient-side protection. To facilitate hands-free endoscope manipulation that imposes minimal distractions to the surgeon, a foot-wearable interface is further designed to relieve the assistant's workload. The foot control method owns a short learning curve (mean 3.4 mins), and leads the task to be more ergonomic and surgeon-centered. Cadaver and clinical studies were both conducted to evaluate the surgical applicability of the FREEDOM to assist endoscope manipulation in FESS. The system was validated to be safe (IEC-60601-1) and easy for set up (mean 3.6 mins), from which the surgeon could perform various three-handed procedures alone in FESS without disrupting the routine practice.

This study proposes a novel active screw-drive in-pipe robot that can adapt the circular-type and square-type pipe structure. The pipe robot is composed of four driving units and a wall-pressing suspension mechanism. Each driving unit contains a motor, a transmission train, and an electromagnetic brake, which is for switching the motion transmission route. DC motors drive the helical wheels, and the incline angle of the helical wheels can be adjusted by using the electromagnetic brake. The wheels of the driving unit exhibit rolling and steering motion. Thus, the robot is capable of translation movements, rotation movements, and screw motions with respect to the axis of the pipe according to the different positions of the helical wheels. The robot can avoid obstacles by using the rotation and screw modes. Moreover, the wall-pressing mechanism is analyzed and modified, and a criteria for entering a reduction pipe reducer are derived for the double scissor-like suspension mechanism. We also analyze the robot motion in curved pipes in two typical postures. The simulation experiments reveal the relationship between the translation and rotation motion of the robot and indicates that the steering angle of the wheels can be regarded as a regulator to adjust the movement speed of the robot aside from tuning the posture of the robot. Elbow experiments are conducted to verify the effectiveness of the motion strategy. The robot can be adapted for both circular and square tube pipes without any change in its structure due to the special configuration.

Robots are playing an increasingly important role in modern surgery. However, conventional human-computer interaction methods such as joystick control and sound control have some shortcomings, and medical personnel are required to specifically practice operating the robot. We propose a human-computer interaction model based on eye movement with which medical staff can conveniently use their eye movements to control the robot. Our algorithm requires only an RGB camera to perform tasks without requiring expensive eye-tracking devices. Two kinds of eye control modes are designed in this paper. The first type is the pick and place movement, with which the user uses eye gaze to specify the point where the robotic arm is required to move. The second type is user command movement, with which the user can use eye gaze to select the direction in which the user desires the robot to move. Experimental results demonstrate the feasibility and convenience of these two modes of movement.