Particle Filter based Landmark Mapping for SLAM of Mobile Robot based on RFID System

This paper proposes a novel Simultaneous Localization and Mapping (SLAM) based on distributed particle updates for landmark mapping and validates it with an HFband2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) July 12-15, 2016 at the Banff Centre, Banff, Canada

Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents

Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents

Self-localization based on Image Features of Omni-directional Image

Omni-vision system using an omni-mirror is popular to acquire environment information around an autonomous mobile robot. In RoboCup soccer middle size robot league in particular, self-localization methods based on white line extraction on the soccer field are popular. We have studied a self-localization method based on image features, for example, SIFT and SURF, so far. Comparative studies with a conventional self-localization method based on white line extraction are conducted. Compared to the self-localization method based on white line extraction, the method based on image feature can be applied to a general environment with a compact database

Actin Family in INO80 Complex

Nuclear actin family proteins, comprising of actin and actin-related proteins (Arps), are essential functional components of the multiple chromatin remodeling complexes. The INO80 chromatin remodeling complex, which is evolutionarily conserved and has roles in transcription, DNA replication and repair, consists of actin and actin-related proteins Arp4, Arp5, and Arp8. We generated Arp5 knockout (KO) and Arp8 KO cells from the human Nalm-6 pre-B cell line and used these KO cells to examine the roles of Arp5 and Arp8 in the transcriptional regulation mediated by the INO80 complex. In both of Arp5 KO and Arp8 KO cells, the oxidative stress-induced expression of HMOX1 gene, encoding for heme oxygenase-1 (HO-1), was significantly impaired. Consistent with these observations, chromatin immunoprecipitation (ChIP) assay revealed that oxidative stress caused an increase in the binding of the INO80 complex to the regulatory sites of HMOX1 in wild-type cells. The binding of INO80 complex to chromatin was reduced in Arp8 KO cells compared to that in the wild-type cells. On the other hand, the binding of INO80 complex to chromatin in Arp5 KO cells was similar to that in the wild-type cells even under the oxidative stress condition. However, both remodeling of chromatin at the HMOX1 regulatory sites and binding of a transcriptional activator to these sites were impaired in Arp5 KO cells, indicating that Arp5 is required for the activation of the INO80 complex. Collectively, these results suggested that these nuclear Arps play indispensable roles in the function of the INO80 chromatin remodeling complex

Mobile Robot Self Localization based on Multi-Antenna-RFID Reader and IC Tag Textile

This paper presents a self-localization system using multiple RFID reader antennas and High-Frequency RFID-tag textile floor for an indoor autonomous mobile robot. Conventional self-localization systems often use vision sensors and/or laser range finders and an environment model. It is difficult to estimate the exact global location if the environment has number of places that have similar shape boundaries or small number of landmarks to localize. It tends to take a long time to recover the self-localization estimation if it goes wrong at once. Vision sensors work hard in dark lighting condition. Laser range finder often fails to detect distance to a transparent wall. In addition, the self-localization becomes unstable if obstacles occlude landmarks that are important to estimate position of the robot. Door opening and closing condition affects the self- localization performance. Self-localization system based on reading RFID-tags on floor is robust against lighting condition, obstacles, furniture and doors conditions in the environment. Even if the arrangement of the obstacles or furniture in the environment is changed, it is not necessary to update the map for the self-localization. It can localize itself immediately and is free from well-known kidnapped robot problem because the RFID-tags give global po- sition information. Conventional self-localization systems based on reading RFID-tags on floor often use only one RFID reader antenna and have difficulty of orientation estimation. We have developed a self-localization system using multiple RFID reader antennas and High-Frequency RFID-tag textile floor for an indoor autonomous mobile robot. Experimental results show the validity of the proposed methods.2013 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) Shibaura Institute of Technology, Tokyo, JAPAN November 7-9, 201