This paper presents a reactive navigation method that leverages a Model Predictive Path Integral (MPPI) control enhanced with spline interpolation for the control input sequence and Stein Variational Gradient Descent (SVGD). The MPPI framework addresses a nonlinear optimization problem by determining an optimal sequence of control inputs through a sampling-based approach. The efficacy of MPPI is significantly influenced by the sampling noise. To rapidly identify routes that circumvent large and/or newly detected obstacles, it is essential to employ high levels of sampling noise. However, such high noise levels result in jerky control input sequences, leading to non-smooth trajectories. To mitigate this issue, we propose the integration of spline interpolation within the MPPI process, enabling the generation of smooth control input sequences despite the utilization of substantial sampling noises. Nonetheless, the standard MPPI algorithm struggles in scenarios featuring multiple optimal or near-optimal solutions, such as environments with several viable obstacle avoidance paths, due to its assumption that the distribution over an optimal control input sequence can be closely approximated by a Gaussian distribution. To address this limitation, we extend our method by incorporating SVGD into the MPPI framework with spline interpolation. SVGD, rooted in the optimal transportation algorithm, possesses the unique ability to cluster samples around an optimal solution. Consequently, our approach facilitates robust reactive navigation by swiftly identifying obstacle avoidance paths while maintaining the smoothness of the control input sequences. The efficacy of our proposed method is validated on simulations with a quadrotor, demonstrating superior performance over existing baseline techniques.

This study examined the method to simultaneously conduct identical experimental classes across different locations, particularly considering the potential for remote classes during pandemics such as COVID-19. The goal was to achieve a system configuration that allows for distributed classes while maintaining a sense of unity, similar to in-person sessions. Hierarchical optimal control methods were applied for this purpose. Further-more, this study addresses the communication delays that occur during remote control between distant locations by applying the concept of state-prediction control in time-delay systems. This approach demonstrated the ability to calculate the current state of a counterpart from the past states, thereby mitigating the impact of delays. The effectiveness of this approach was demonstrated experimentally using an inverted penduluman iconic system used in control engineering education.

This Innovative Practice Full Paper presents an experience-based lecture style for vibration engineering. In the proposed style, experiments of the free vibration using a seismic building and advanced vibration control using an effective device are provided in combination. In the first trial of this lecture style in 2015, two hundreds of second-year students felt the free vibration of the five-story building with a weight of 5,600 ton inside the building and witnessed the mechanism of the isolation layer. To follow the demonstration experimentally, we have built an experimental system. The device is composed of a vibration component corresponding to a building and a cart with a handle corresponding to the ground. They are connected via a linear actuator which can exert one-dimensional force to the vibration component as a function of the input voltage. Students oscillate the cart and observe the motion of the vibration component. In addition, the system has virtual reality cameras to attract students' interest. The lecture was conducted in 2017. A questionnaire showed that 100% of students valued the lecture as useful. Since 90% of students valued that providing large-scale demonstration and this lecture in combination as useful, the proposed style achieved a certain result.

In this study, psychological experiments are conducted to investigate harm-avoidance action characteristics in humans in close contact with robotic devices. For the experiments, a situation is created in which the sharp end-effector tip of a robot suddenly approaches the eyes of a facing participant. The avoidance reaction time is defined as the time interval from the beginning of the end-effector motion to when the participant begins movement to avoid harm. The results suggest that the avoidance reaction time does not depend on the type of work being performed but on the initial distance between the human's eyes and the approaching object. Using this information, safe human-robot working distances can be determined.

Wearable robots have finally reached a point at which they can be put to more practical use. However, methods for assessing their safety have yet to be properly established. Furthermore, for ethical reasons, such assessments must be conducted without exposing the human user to unnecessary danger. Therefore, in this study, a lower-leg dummy for the safety assessment of a wearable robot was developed to evaluate the effects of a misaligned knee joint.

In this study, a psychological experiment is conducted to investigate harm-avoidance action characteristics in humans in close contact with robotic devices. For the experiment, a situation is created in which the sharp end-effector tip of a robot suddenly approaches the eyes of a facing participant. We define three parameters that represent harm-avoidance action characteristics: the avoidance reaction time, maximum avoidance acceleration, and maximum avoidance speed. The results suggest that the avoidance reaction time depends on the initial distance between the human's eyes and the approaching object. The results show that there are individual differences in all parameters studied. The results also show that the avoidance reaction time is negatively correlated with the other two parameters, which have a strong positive correlation with each other. We conclude that the avoidance reaction time and maximum avoidance speed are considerable parameters on avoidance actions.

In the design of human-robot coexistence environments, human avoidance actions are not taken into consideration. We conduct a psychological experiment to observe human avoidance actions where a mechanical harm approached his or her eyes. Using the observed motions, we carry out “extrapolation simulation” of human avoidance actions in collision situations. As simulation results, first, we express collision probability using the motion range of the end effector of a robot as a variable. Second, we identify the motion conditions, which ensure the safety, of the end effector in our setup. Finally, we quantitatively compare the safety conditions between taking and not taking avoidance actions into consideration. This study contributes to providing a tool for conducting a practical risk assessment aimed at realizing a safe human-robot coexistence system.

The objective of our study is to develop a safety measure that ensures smooth control mode switching of an intelligent assist device (IAD)-Skill-Assist for the purpose of effective automotive manufacturing. Smooth switching, i.e., switching without abrupt deceleration, from the hands-off control mode (used for automatic travel) to the hands-on control mode (as typified by a power-assist control mode) is expected to enhance the productivity of IADs. However, the hazardous behaviors, i.e., careless behaviors that disregard an approaching IAD in hands-off control mode might cause collision and serious injuries to a human operator. For safety purposes, we focused on the operator's gesture of reaching for the force sensor of the Skill-Assist. Laser range sensors were employed to recognize the reaching gesture during control mode switching. A reaching gesture recognition algorithm, based on a hidden Markov model, processes characteristic patterns acquired by the laser range sensors, and judges the operator safety. Experimental results suggest that the Skill-Assist to which the proposed safety measure was applied could recognize the reaching gesture of an operator when the control mode was smoothly switched. In an experiment on hazardous behavior, the Skill-Assist could predict that the operator would be unsafe.

The objectives in this paper are to develop a method for safety-preservation oriented reaching monitoring (SORM) and to demonstrate its effectiveness for realizing a safe and smooth control mode switching. For more effective manufacturing activities, smooth control mode switching of a power assist device ldquoSkill-Assistrdquo from hands-off to hands-on is expected to enhance the productive efficiency. However, hazardous behaviors of an operator without paying any attention to a Skill-Assist in the hands-off control mode are possible to occur, and may result in serious injuries; that is why the SORM is demanded for smooth control mode switching. We focused on operator's hand reaching to the force sensor of a Skill-Assist conducted in the control mode switching, and two laser range sensors are employed to monitor the reaching. The SORM algorithm based on hidden Markov model processes the reaching information, and judges predictively whether the operator will be in a safe state or not. An experimental result shows the Skill-Assist to which the SORM is applied could recognize a control mode switching behavior with reaching while the control mode is switched smoothly without rapid deceleration. In an experiment for a hazardous behavior without reaching, the Skill-Assist could judge successfully an operator will be in a unsafe state. The judgment in the experiment for the hazardous behavior was evaluated by a minimum safe distance specified by an ISO safety standard, and it could satisfy the safety requirement.

The author's previous study proposed an efficient design method of time-varying gain type controller using the solutions of time-varying Riccati equations (TVREs) and its application to positioning control of vibration systems. This method generates the solutions of a TVRE and the responses of a controlled object simultaneously. The time-varying gains are obtained by realtime computations in actual implementations. This feature is convenient for the realization of some adaptive control. However, such a control method has not been fully addressed yet. This study discusses an adaptive nonstationary control (ANSC) method based on the feature of the previous study. We select a flexible structure installed on an X-Y table as a controlled object example. Its positioning and residual vibration reduction are the purposes of the application in this paper. The effectiveness of the ANSC method is verified by numerical calculations and experiments.