Currently, autonomous driving on sailing vehicles attracting attentions. In our previous study, a set of control rules was proposed to reach the windward target point using the position information obtained at intervals of several seconds for a sailing vehicle on the water, which is more difficult to stop and steer than on land and is more likely to sway leeward. In this study, in addition to this previous study, we conduct and analyze actual experiments using two methods that apply an obstacle avoidance method proposed previously on land to sailing vehicle on the water. Method 1 aims at the middle direction between the avoidance direction and the target point direction, and Method 2 steers in the direction opposite to the obstacle. We conducted actual experiments of obstacle avoidance using a sailing robot. We confirmed that obstacle avoidance was possible with either of the two proposed methods, and we clarified the characteristics of the methods by analysis. In the experiments, the wind blew almost orthogonal to the two-point reciprocating course with the midpoint as the obstacle, so the obstacle and the target point were located upwind from the direction of travel in all three times of meet with the obstacle. In this situation, Method 1 steered upwind to soften the Leeway effect and prevent the path from bulging downwind. In Method 2, the time to avoid obstacles was shortened by turning the rudder in the leeward direction.
It is not easy to realize the dynamic movements of legged robots, such as jumping or running. This is due to the poor power-to weight ratio of the existing actuator and the energy density of the existing energy source, which makes it difficult to generate a large amount of power instantaneously. In this paper, a robot system equipped with a vibration unit on the body part for jumping motion is proposed. The importance of improving the robot's vertical output for jumping motion is indicated. A unit consisting of a weight and a spring - the vibration unit - is proposed. In order to accumulate a small amount of power to make a large amount of energy, a legged robot system with the vibration unit on its body and serially configured leg mechanisms for vertical excitation is introduced. The mechanical and control system design is illustrated. The effect of the vibration unit is verified in computer simulations. Experimental results of the robot's jumping behavior with and without the vibrating unit are also shown and discussed.
Due to the current labor shortage, automation by robots has been expected in society. In manual sorting of garbage in recycling factories, there is a risk of injury due to sharp garbage, and robots are needed to replace them. In this paper, we propose an improved method of garbage sorting using thermal images. Previously, we classified three types of beverage container garbage from thermal images, but it could not cope with dense garbage. In this work, material classification is performed for each pixel of the thermal image, followed by clustering to correctly separate and classify garbage, even dense garbage. In the experiment, we collected thermal images of heated garbage on hot conveyor for classification, and verified the accuracy of the computed material and object maps, and compared them with previous work.
Robotic mobility analysis in rough terrain is essential for mission success on robots deployed on planetary surfaces, construction, or disaster sites. A Hardware-in-the-loop Simulation (HILS) is known as an accurate and feasible method for dynamic analysis. The HILS is the hybrid approach that considers the contact dynamics of a robot in an actual environment and calculates the robot vehicle dynamics in a numerical simulation. We developed a HILS for wheeled mobile robot in loose terrain using a single wheel test bed. The HILS is verified under several traction load conditions. Further, the HILS is coupled with a wheel contact model based on the Dynamic Resistive Force Theory (DRFT). This HILS-DRFT can simultaneously simulate multiple wheels of the mobile robot. To improve the reliability of the DRFT, we implemented a real-time tuning process for the scaling factor used in the DRFT based on the wheel sinkage. The HILS-DRFT with the real-time tuning accurately reproduces the driving force of the actual vehicle.
This study is to construct a system for patients to easily search for the appropriate stimulation positions of functional electrical stimulation (FES) to obtain the desired posture for daily rehabilitation. We applied 125 patterns of electrical stimulation using integrated power-net multi-point electrodes that we developed. The posture discrimination of each five fingers was performed using hand tracking. 20 different machine learning algorithms were investigated with the accuracy of for robust hand estimation based on the relationship with the induced hand posture and the stimulation patterns. The experimental results illustrated the degree of difficulty of estimating hand posture using the stimulus center based on the evaluation results using AUC as the evaluation index of machine learning, and that the consideration of direction vector of electrical stimulation increased the accuracy.
In this study, we evaluated the effect of the parameter of interest on the dynamic characteristics of the electric wheelchair thorough the automatic running experiment excluding the influence of human maneuvering and the mathematical stability analysis with the aim of investigating the cause of the low straightness of the front-drive electric wheelchair. Furthermore, from the experimental results, it was clarified that it is possible to effectively improve the straightness of the front-wheel drive wheelchair by using a control method that cancels the influence of centrifugal force.
Many serious injuries occur in preschools. Effective injury prevention requires measures customized to the individual preschool. This paper proposed a field-adaptive injury prevention support system that combines both epidemiological analysis of big data of accidents occurred at Japanese preschools and video analysis on accidents/incidents occurred at a specific preschool. While the big data analysis revealed serious injury patterns common in preschools, the video analysis allowed us to extract behavioral patterns in real situations arising at the target preschool. Analysis from both sides enabled the grasping of the behaviors and environments that could lead to serious accidents at the target preschool.