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Path Tracking Control of Lagrange Systems with Obstacle Avoidance
Kazunori Sakurama,Kazushi Nakano 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.1
This paper addresses a path tracking problem with obstacle avoidance for Lagrange systems. The proposed method is based on field potential methods in combination with navigation functions for obstacle avoidance. First, it is shown that a simple combination of the navigation function with the conventional path tracking controller does not work. Therefore, in order to cope with this problem, a new feedback law is proposed for a path parameter which characterizes the reference path. It is proved that the proposed controller achieves both path following and collision avoidance. Moreover, since the method adopts bounded navigation functions, the proposed controllers generate bounded input signals even when target systems approach obstacles. Finally, an experimental evaluation is performed with a two-link manipulator to illustrate the effectiveness of the proposed method.
Current status of the diagnosis of chronic pancreatitis by ultrasonographic elastography
( Kazunori Nakaoka ),( Senju Hashimoto ),( Ryoji Miyahara ),( Hiroki Kawashima ),( Eizaburo Ohno ),( Takuya Ishikawa ),( Takamichi Kuwahara ),( Hiroyuki Tanaka ),( Yoshiki Hirooka ) 대한내과학회 2022 The Korean Journal of Internal Medicine Vol.37 No.1
Chronic pancreatitis (CP) is pathologically characterized by the loss of exocrine pancreatic parenchyma, irregular fibrosis, cellular infiltration, and ductal abnormalities. Diagnosing CP objectively is difficult because standard diagnostic criteria are insufficient. The change of parenchymal hardness is the key factor for the diagnosis and understanding of the severity of CP. The ultrasonography (US) or endoscopic ultrasonography (EUS) elastography have been used to diagnose pancreatic diseases. Both strain elastography (SE) and shear wave elastography are specific diagnostic techniques for measuring tissue hardness. Most previous studies were conducted with SE. There are three methods of interpreting SE; the method of recognizing the patterns in SE distribution images in the region of interest, the method of using strain ratio to compare the hardness of adipose tissue or connective tissue with that of the lesion, and the method of evaluating the hardness distribution of a target by histogram analysis. These former two methods have been used primarily for neoplastic diseases, and histograms analysis has been used to assess hardness distribution in the evaluation of CP. Since the hardness of the pancreas increases with aging, it is necessary to consider the age in the diagnosis of pancreatic disorders using US or EUS elastography.
Formation Control of Swarm Robots with Multiple Proximity Distance Sensors
Kazunori Sakurama,Yusuke Kosaka,Shin-ichiro Nishida 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
This study deals with the formation control problem of swarm robots using position sensitive detector(PSD) proximity distance sensors based on light-emitting diodes (LEDs). These proximity distance sensors arelightweight and quickly responsive, and are expected to enhance the mobility and flexibility of swarm robots. However,as each sensor has a narrow detection angle, the formation control problem becomes more difficult than whenwide-directional distance sensors (such as cameras and laser rangefinders) are used. To overcome this difficulty,we design a two-part motion controller that controls both position and attitude. The attitude controller is necessaryfor continuous detection of other robots through the narrow detection angles. The designed controller is distributedin the sense that it requires only information on measured values of each robot’s own sensors. Next, we derive anappropriate sensor arrangement (positions and detection angles) that achieves the desired formation pattern. Finally,the effectiveness of the proposed method is demonstrated in an experiment performed by six omni-wheeled robotsequipped with LED-based PSD proximity distance sensors.