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We implemented and evaluated our previous path planning method for inspection using unmanned aerial vehicles (UAVs) in real-world, and identified its shortcomings in handling positioning errors. Then, we proposed an enhanced method to address this problem. The previous method theoretically guaranteed complete coverage of targets and data quality. However, we verified it in bridge inspection experiments and found that the former has not been ensured. The crucial factors of data omission are clarified as the errors in UAV positioning. Our previous method relies on appropriately setting ideal allowances to counteract positioning errors, which is challenging in practice. Therefore, we proposed an enhanced path planning method, which adaptively adjusts allowances according to positioning error to prevent omission while minimizing waypoints. In the simulation including positioning disturbances, the enhanced method consistently achieved full coverage in 1000 times simulation with over 28% waypoints less than the previous one.

This paper investigates the feasibility and effectiveness of applying AR (Augmented Reality) technology to assist UAV (Unmanned Aerial Vehicle) pilots in collecting data for structure inspections. The challenge of using UAVs to collect inspection data is that pilots could not precisely determine where to collect qualified data. Using AR technology to provide visual assistance to pilots is expected to tackle this challenge. However, previous research has focused on enhancing pilots’ perception of UAVs’ current situations and goal waypoints. How to indicate ideal data collection poses (positions and orientations) with AR and the practical improvement that AR can bring remain unknown. Therefore, we proposed a system that visualizes ideal poses as AR models, allowing pilots to easily collect qualified data by aligning UAVs with these models and taking images. Experiments in virtual and real confirmed that the system significantly improves the proportion of the valid inspection area (achieving up to 99.3%).

Abstract Finite control set model predictive control (FCS–MPC) has been deployed extensively in electric motor drives and power electronics applications. Many advantages, including fast transient response, have been reported. However, the improvement of its steady‐state performance, that is, suppression of current ripple, is one of the main problems of a conventional FCS–MPC strategy. This article presents a current control strategy based on FCS–MPC using the concept of variable voltage smoothing. In the proposed method, a finite set of novel voltage vectors, produced by the smoothing process, are introduced as candidate control input candidates, and the controller generates the optimum output voltage using a modulator integrated with the FCS–MPC scheme. Thus, the voltage candidates are generated flexibly, and a suitable voltage for acceptable steady‐state performance is determined as the control input. In addition, the smoothness in the proposed concept can be updated automatically depending on the drive situation, that is, transient, or steady‐state operation, thus avoiding degradation of dynamic performance. Simulation and experimental results obtained with a permanent magnet synchronous motor drive system demonstrate that the proposed control strategy is effective to achieve current control with fast tracking performance while suppressing current ripple.

In recent years, there has been extensive utilization of actuators driven by artificial muscles in wearable devices. However, the muscle distribution configurations of most wearable devices have been specifically designed and are difficult to generalize. Consequently, wearable devices that allow direct installation of actuators onto existing clothing are better suited for a wider range of application scenarios. This letter presents the development and evaluation of Funabot-Suit, a human muscle configuration inspired wearable assist device that employs McKibben artificial muscles to induce natural kinesthetic perception in the wearer’s torso. By integrating thin McKibben muscles into an existing motion capture suit, the Funabot-Suit is capable of generating four fundamental motions: forward and backward bending, and left and right twisting. The suit’s performance was assessed through experiments involving three subjects who wore the suit while standing and seated, with the subjects reporting the direction of their kinesthetic perception. The subjects also rated the perceived ease of kinesthetic perception direction on a five-point scale. Our results demonstrate that the Funabot-Suit successfully induces kinesthetic perceptions for the wearer, with a one hundred percent detection ratio for accurate responses in the command direction across all subjects and positions. We observed variations in the sensitivity of left–right and up-down sensations, which can be attributed to the positioning of artificial muscles and individual differences.

We developed a wearable assistive robot that directly measures the contact force acting between the robot and the human body, aiming to enhance the safety of such wearable devices. The force acting on the body is not directly measured, instead, it is estimated by measuring the torque acting on the robot joint in the wearable assistive robot. Despite the risk of failing to recognize dangerous forces due to modeling errors, the forces actually at work have not been examined. Here, we developed an arm-mounted assistive robot that directly measures the contact force as the distribution information to build a system and evaluate safety; in addition, the contact state between the robot and the human body was discussed. Accordingly, two experiments were conducted with 10 subjects. The first verified the contact force measurement performance of the robot, while the second demonstrated the application of contact force information to check the safety of the robot. The proposed robot can accurately measure the contact force, and the robot movement is safe under general control, for example, using the pain tolerance limit as the safety index. This eliminated the risk of not directly monitoring the forces acting on the surface of the human body. Furthermore, our result have implication for evaluating structural problems of the robot by evaluating contact conditions during movements.

Flexible actuators are popular in the consumer and medical fields because of their flexibility and compliance. However, they are typically difficult to model because of their viscoelasticity and nonlinearity. This letter proposes a method for correcting the deformation of the simulated flexible robots to make it similar to the deformation of real robots using point clouds by deep learning. Long short-term memory (LSTM) can simulate the next frame of actuator deformation from the previous frames of deformations. In this study, we presented the robots with four different muscle structures. We found that using an encoder–LSTM–decoder network can improve the similarity between the deformation of a learned muscle structure and the real deformation and is also effective in correcting the deformation of the unlearned structures. Our correction method reduced the average Chamfer distance of the simulated point clouds of the basic-type structure actuator from 15.89 to 7.81. This research can provide a new concept for future flexible robot modeling using point clouds.

Abstract Probabilistic localization based on Bayesian theory has been researched as a sensor fusion method to improve the robustness of localization. Pieces of position information, generated by sensors’ observation models with consideration for noises, are fused according to Bayesian theory. However, having large noises not considered in their observation models, the sensors output erroneous position information; thus, the fusion result has a significant error, even when the other sensors output correct ones. In this research, we have proposed a sensor fusion system with a relative correlation checking test to realize robust localization. Pieces of erroneous position information, biased against others and having a negative correlation with others, are detected and excluded in our proposed system by checking their correlation between all of them. The purpose of this paper is to evaluate the robustness of our fusion system by conducting recursive localization experiments in various environments.

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.