In this paper, we discuss a robot vision in order to perceive people and the environment around a mobile robot. We developed a tele-operated mobile robot with a pan-tilt mechanism composed of a camera and a laser range finder (LRF). In this paper, we propose a method for sensor fusion to extract a human from the measured data by integrating these outputs based on the concept of synthesis. Next, we propose a method of hierarchical neural network based on Growing neural gas to construct a 3D environmental relation. Finally, we show experimental results of the proposed method.

In this study, the teleoperation robot control on a mobile robot with 2D SLAM and object localization using LPWAN is proposed. The mobile robot is a technology gaining popularity due to flexibility and robustness in a variety of terrains. In search and rescue activities, the mobile robots can be used to perform some missions, assist and preserve human life. However, teleoperation control becomes a challenging problem for this implementation. The robust wireless communication not only allows the operator to stay away from dangerous area, but also increases the mobility of the mobile robot itself. Most of teleoperation mobile robots use Wi-Fi having high-bandwidth, yet short communication range. LoRa as LPWAN, on the other hand, has much longer range but low-bandwidth communication speed. Therefore, the combination of them complements each other’s weaknesses. The use of a two-LoRa configuration also enhances the teleoperation capabilities. All information from the mobile robot can be sent to the PC controller in relatively fast enough for real-time SLAM implementation. Furthermore, the mobile robot is also capable of real-time object detection, localization, and transmitting images. Another problem of LoRa communication is a timeout. We apply timeout recovery algorithms to handle this issue, resulting in more stable data. All data have been confirmed by real-time trials and the proposed method can approach the Wi-Fi performance with a low waiting time or delay.

This paper presents a new 3D map building technique using a combination of 2D SLAM and 3D objects that can be implemented on relatively low-cost hardware in real-time. Recently, 3D visualization of the real world became increasingly important. In robotics, it is not only required for intelligent control, but also necessary for operators to provide intuitive visualization. SLAM is generally applied for this purpose, as it is considered a basic ability for truly autonomous robots. However, due to the increase in the amount of data, real-time processing is becoming a challenge. Therefore, in order to address this problem, we combine 2D data and 3D objects to create a new 3D map. The combination is simple yet robust based on rotation, translation, and clustering techniques. The proposed method was applied to a mobile robot system for indoor observation. The results show that real-time performance can be achieved by the system. Furthermore, we also combine high and low-bandwidth networks to deal with network problems that usually occur in wireless communication. Thus, robust wireless communication can be established, as it ensures that the missions can be continued even if the system loses the main network.

We here propose a three-dimensional shape presentation system using linear actuators which can control the displacement of a rod to a precision of 0.1 mm. We assume that this system will be used as a device to present topographical/shape information to people with visual impairments. For this study, we produced a prototype system equipped with precision linear actuators, and conducted experiments to evaluate the capabilities of the system.