This paper proposes the design, fabrication, and testing of a wall-climbing robot (WCR) using gecko-inspired dry adhesives. The robot consists of two tank-like modules, which utilize adhesive timing belts for locomotion and adhesion. A single module is firstly optimized in design to maximize the adhesive force. Then, an under-actuated compliant mechanism is designed to connect two modules. The robot mimics not only the gecko's multiscale adhesive structures but also its multiscale bio-adjustment mechanism. Inspired by the gecko's digital behavior, the robot can performs gripping-in and furling out motions to adjust preloading forces for each module on surfaces sloped from 0 to 180 degrees with respect to the level, via the under-actuated compliant mechanism. The robot's prototype is manufactured using 3Dprinting. Dry adhesives with pillar-patterned surface are fabricated. The adhesives then form a layer and cover the exterior surface of the flexible belts. Testing performances show that the robot can achieve stable scaling on the wall and ceiling with adjustable contact force, as well as the terrain-compatibility for surface transitioning.
Determination of blood glucose concentrations in diabetic patients is a frequently occurring procedure and an important tool for diabetes management. Use of noninvasive detection techniques can relieve patients from the pain of frequent finger pokes and avoid the infection of disease via blood. This thesis discusses current research and analyzes the advantages and shortages of different measurement methods, including: optical methods (Transmission, Polarimetry and scattering), then, we give emphasis to analyze the technology of near-infrared (NIR) spectra. NIR spectral range 700 nm ~2300 nm was used because of its good transparency for biological tissue and presence of glucose absorption band. In this work, we present an outline of noninvasive blood glucose measurement. A near-infrared light beam is passed through the finger, and the spectral components of the emergent beam are measured using spectroscopic techniques. The device includes light sources having the wavelengths of 600 nm - 1800 nm to illuminate the tissue. Receptors associated with the light sources for receiving light and generating a transmission signal representing the light transmitted are also provided. Once a transmission signal is received by receptors, and the high and low values from each of the signals are stored in the device. The averaged values are then analyzed to determine the glucose concentration, which is displayed on the device.
Information chain consists of information acquisition, information processing, information transmission and information applications. Researches on information acquisition have been distributed in many disciplines since the multi-discipline property of information acquisition. The progress of information acquisition has been restricted, and information acquisition has become the bottleneck in information flow. The process and theory of information acquisition has been studied, and the discipline system of information acquisition science and technology is proposed.
A swimming microrobot driven by magnetic field is presented. A new smart material, ferromagnetic polymer was utilized as actuation material. The microrobot has a pari of FMP fins, which are soft and driven by magnetic field symmetrically. The principle of actuation is given. The size of the robot is 20mm by 14mm by 5mm. The robot can move forward and backward dependent on the magnetic flux density and the frequency. The robot has many possible applications, such as minimally invasive medical techniques.
Several kinds of sensors are installed in the robotic gripper. According to the outputs of multi-sensor, a data fusion technique is utilized to ensure the robot walking or grasping objects safely and reliably. In this paper, sensors of the gripper are introduced, such as force sensor for contact sensing and gripping force control, proximity sensor for collision prevention and position detection, and a displacement sensor for gripper openness control. The experiments of grasping objects with the gripper are presented, including firm grasp, virtual grasp, skew grasp, empty grasp and so on. The accurate information of grasping objects with the gripper is obtained using the multi-sensor data fusion technique based on the BP artificial neural network.
Conference Committee Involvement (1)
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II