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 developed and evaluated a P-CUBE series of block-based, tangible programming tools for beginner programmers, including the visually impaired. We conducted a trial class to create a speech program with P-CUBE3, which included a function to record the operational history data of the programming blocks. We classified the users’ operation patterns using the operation history data obtained in the class and evaluated the results. We propose a method for classifying user operation patterns from the operation history data obtained in the class and visualizing the tendency of block operations using a co-occurrence matrix. We also report the differences in the tendencies of blocking operations based on user characteristics revealed by the proposed method.

Leader–follower robots employ teleoperation, enabling flexible and safe operation. A robotic surgical system, as a typical leader–follower system, enables remote manipulation in minimally invasive surgery. However, inappropriate operator inputs may be directly transmitted to the patient side, potentially compromising safety. In this study, we propose a framework for estimating the operator’s operating strategy and suppressing motions caused by inappropriate operator inputs. The framework represents operating strategies on a strategy map defined by the degrees of precision, non-precision, and risk. A simplified fuzzy inference is used to estimate the degrees of precision and non-precision based on manipulation force and hand velocity, while the degree of risk is evaluated using gaze detection combined with the force and pressure applied by the instrument. Using processed kinematic and video data from the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), we validated the framework across gesture scenarios, including simulated cases of inappropriate operation. The results suggest that the proposed framework demonstrates the theoretical feasibility of effectively mitigating potentially unsafe motions within the simulated environment while preserving essential operational flexibility.