No abstract

No abstract

The minimal invasive surgery reducing an incision is required for a tumor resection in the field of neurosurgery. The minimal invasive surgery has many advantages of operations which are less lost of blood and painless using a thin long pole shape surgical instrument. For all those advantages, it has defects of difficulties of visual insurance and limitations of moving surgical instruments. Therefore, many researchers are developing several manipulators which are capable of minimal invasive surgery using robot technologies to solve these problems recently. However, most developed surgery robots for minimal invasive surgery are limited to the operation of laparoscopic surgery. In this study, we developed a tele-operational master-slave system for the minimal invasive brain surgery. The master manipulator has 4 degree of freedom (DOF) mechanism for the manipulation of slave position and direction. Similarly, the slave end-effecter for the minimal invasive brain surgery has a thin long pole shape of 4DOF instrument. The master manipulator and slave end-effector have a similar configuration and 4DOF mechanism which consists of a linear motion and 3 rotational roll-pitch-yaw motions. Therefore, the position command matching between the two systems is very easy. In addition, the master and slave control systems are connected with TCP/IP based internet communication for the tele-operation surgery. Finally, various experimental results are executed to evaluate the performances of the proposed tele-operation master-slave system.

No abstract

No abstract

This paper presents a distance mapping-based multi-robot localization method, which works with incomplete data. We make three contributions. First, we propose the use of multi dimensional scaling (MDS) for multi-robot localization. Second, we formulate the problem to accommodate partial observations common in multi-robot settings. We solve the resulting optimization problem using "scaling by majorizing a complicated function," a popular algorithm for iterative MDS. Third, we take advantage of the motion information of robots to help the optimization procedure. Three policies are compared at each time step: random, previous, and prediction (constructed by combining the previous pose estimates with motion information). Using extensive empirical results, we show that the initialization by the prediction method results in better performance in terms of both accuracy and speed when compared to the other two initialization techniques.

No abstract

No abstract