Chiba Institute of Technology

About: Chiba Institute of Technology is a education organization based out in Narashino, Japan. It is known for research contribution in the topics: RNA & Magnet. The organization has 2663 authors who have published 4999 publications receiving 56870 citations. The organization is also known as: Chiba kōgyō daigaku & Kōa Institute of Technology.

Controlled balance losing in random step environment for path planning of a teleoperated crawler-type vehicle

Abstract: Rescue robotics is the application of robotics to the search and rescue domain, aimed at extending the capabilities and increasing the safety of the rescuers. Deployed on a site during a rescue mission, a mobile robot is teleoperated by a human operator from a safe place. To suggest to the operator a good direction to traverse the three-dimensional (3D) debris environment, we develop a pilot system, which requires a special path search algorithm on debris and a proper definition of a search tree. Although the main goal of the algorithm is to keep the robot maximally stable at every step of its path, in some cases we need the robot to change a 3D orientation discontinuously through losing its balance. Losing balance on purpose is an essential feature for safe climbing up and going down debris, and it is the central issue of this paper. Exhaustive simulations were used to structure and analyze data. Experiments with a real robot verified our approach to removing unsuitable search directions from the search tree and gave important feedback to the algorithm. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow

Abstract: In order to evaluate train timetables from point of view of the passengers, it is indispensable to estimate the disutility of passengers This can be done by accurately estimating the movements of passengers and trains In particular, when there is a large number of passengers, the interactions between the passengers and trains must be considered To this end, we have developed a microsimulation system to simulate both train operation and passengers' train choice behavior The system can simulate the train choice behaviors of more than one million passengers as well as their positions in trains It is possible to estimate the delays caused by crowding in trains as well The system is based on models of different attitudes of the passengers with respect to the train choice behavior, which includes the choice of the earliest train, transfer avoidance, and avoiding crowding; a passenger's train choice behavior reflects his/her preferences We applied this system to an actual railway line in a metropolitan area and evaluated two train schedules by calculating the generalized cost, which reflects each passenger's disutility based on his/her experience Numerical experiments confirmed that the proposed method is very useful for evaluating timetables from the point of view of the passengers © 2012 Wiley Periodicals, Inc Electr Eng Jpn, 181(4): 51–62, 2012; Published online in Wiley Online Library (wileyonlinelibrarycom) DOI 101002/eej21264

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning.

Abstract: This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.