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Many teams participating in robotic competitions achieve localization using a 2D map plotted using adaptive Monte Carlo localization, a robot operating system (ROS) open-source software program. However, outdoor environments often include nonlevel terrain such as slopes. In the indoor environment of multilevel structures, the data representing different levels overlap on the map. These factors can lead to localization failures. To resolve this problem, we develop a software by combining HDL localization, which is an ROS open-source software, with our own program, and use it to achieve localization based on a 3D map. Furthermore, the authors observe the erroneous recognition of a slope as a forward obstacle during a competition event. To resolve this, we propose a method to correct erroneous recognition of obstacles using a 2D laser range finder and 3D map and confirm its validity in an experiment carried out on a slope on a university campus. [ABSTRACT FROM AUTHOR]

In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the recognition of the opened/closed states of automatic sliding glass doors to allow for automatic robot-controlled movement from outdoors to indoors and vice versa by a robot. The proposed method uses an RGB-D camera as a sensor for extraction of the automatic sliding glass doors region and image recognition to determine whether the door is opened or closed. The RGB-D camera measures the distance between the opened or moving door frames, thereby facilitating outdoor to indoor movement and vice versa. Several automatic sliding glass doors under different experimental conditions are experimentally investigated to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

People with disabilities, such as patients with motor paralysis conditions, lack independence and cannot move most parts of their bodies except for their eyes. Supportive robot technology is highly beneficial in supporting these types of patients. We propose a gaze-informed location-based (or gaze-based) object segmentation, which is a core module of successful patient-robot interaction in an object-search task (i.e., a situation when a robot has to search for and deliver a target object to the patient). We have introduced the concepts of gaze tracing (GT) and gaze blinking (GB), which are integrated into our proposed object segmentation technique, to yield the benefit of an accurate visual segmentation of unknown objects in a complex scene. Gaze tracing information can be used as a clue as to where the target object is located in a scene. Then, gaze blinking can be used to confirm the position of the target object. The effectiveness of our proposed method has been demonstrated using a humanoid robot in experiments with different types of highly cluttered scenes. Based on the limited gaze guidance from the user, we achieved an 85% F-score of unknown object segmentation in an unknown environment. [ABSTRACT FROM AUTHOR]

Object detectors using deep learning are currently used in various situations, including robot demonstration experiments, owing to their high accuracy. However, there are some problems in the creation of training data, such as the fact that a lot of labor is required for human annotations, and the method of providing training data needs to be carefully considered because the recognition accuracy decreases due to environmental changes such as lighting. In the Nakanoshima Challenge, an autonomous mobile robot competition, it is challenging to detect three types of garbage with red labels. In this study, we developed a garbage detector by semi-automating the annotation process through detection of labels using colors and by preparing training data by changing the lighting conditions in three ways depending on the brightness. We evaluated the recognition accuracy on the university campus and addressed the challenge of using the discriminator in the competition. In this paper, we report these results. [ABSTRACT FROM AUTHOR]

At present, various robotics competitions are being held, including the Tsukuba Challenge. The purpose of participating in a robotics competition is to confirm what can be done with the current technology and to demonstrate new research results. Participating teams often use open source software (OSS) for path planning and autonomous navigation. OSS is advantageous in facilitating participation in robotics competitions. However, applying it to a new robot is difficult when a new research does not involve OSS. In addition, robot systems do not consist only of OSS, and the burden of developing and maintaining other systems is significant. To solve the above problems, a software platform that allows for the addition of research achievements of individual robots is desired. With such a platform, research elements that have already been developed can be shared without the need to develop a new system. This makes it easier to maintain and manage the system and increase its sustainability. [ABSTRACT FROM AUTHOR]

The noninvasive rotary motion of bio-objects for three-dimensional (3-D) observation and posture adjustment has been a great challenge in microrobotics. This letter reports a harmless noncontact 360° rotation method with a controllable velocity, which applied the vortex induced by vibrating the micropipette in an open fluidic environment. The micropipette was driven by a piezo actuator, which was mounted on a copper pillar. By generating a resonance between the copper pillar and the piezo actuator fed with sine wave of a specific frequency, we controlled the micropipette to vibrate circularly. The vibration-induced vortex around the micropipette could trap micro objects close to it, and then drive the trapped objects to rotate with the uneven drag force acting on their surface. Experiments of 360° rotation of the microbeads were carried out to demonstrate this method and analyze the rotation velocity. An application in 3-D imaging under optical inverted microscopy was achieved. The spheroids assembled by single cells were rotated, which enabled us to observe the different aspects of the cell spheroids and reconstruct its 3-D view. [ABSTRACT FROM AUTHOR]

This paper presents a non-contact manipulation method using rotational flow generated by high speed motion of a glass needle. In order to generate an applicable motion that can precisely control the speed and position of a target object around an end effector, a manipulator actuated by three piezoelectric actuators is proposed. A compact parallel link produces precise three-dimensional motions. To reach the required end-effector frequency and amplitude, which is necessary for generating the rotational flow that can make rotational movement of microbeads having patterned data, the end effector motion is generated by controlling the three piezoelectric actuators at high speed. In this paper, we focus on rotational flow created by two-dimensional circular motion of the end effector mounted on the parallel link. By changing the amplitude and frequency of the circular motion, the generated swirl flow is analyzed in 3D space using 9.6-μm microbeads. We analyzed the velocity toward to the root of the end effector and angular velocity around the end effector by formulating models. From the analyzed data, we verified that the rotational flow has potential to manipulate micro-objects precisely in 3D space. [ABSTRACT FROM AUTHOR]

Keyframe selection is the process of finding a set of representative frames from an image sequence. We aim to achieve an automatic keyframe selection. The main problem is that the accuracy of keyframe selection is highly subjective to each particular user. To deal with this problem, we propose a VIEMO keyframe selection framework based on visual and excitement features that consists of two integrated modules, namely; event segmentation and keyframe selection. Firstly, scene change detection algorithm was applied for an event segmentation. Later, visual features which are contrast, color variance, sharpness, noise and saliency along with excitement features from a biosensor are used to filter keyframe that closely matches with user selection keyframe. Two different fusion scheme which are flat and hierarchical fusion were also investigated. To evaluate the quality of keyframe from the proposed method, we present an evaluation techniques which grades the quality of the keyframe automatically. Even when the keyframe does not exactly match with the keyframe selected by the user, the degree of acceptance calculated from visual similarity is provided. Experimental results showed that keyframe selection using only visual features yielded an acceptance rate of $$74.16 \,\%$$ . Our proposed method achieves a higher acceptance rate of $$83.71\,\%$$ . Moreover, the acceptance rate was improved by the average of $$9.55\,\%$$ in all participants. Therefore, our framework provides a potential solution to this subjective issue for keyframe selection in lifelog image sequences selection. [ABSTRACT FROM AUTHOR]

In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. [ABSTRACT FROM PUBLISHER]

In this paper, we present a high-speed pick-and-place method for cell-assembly applications. In addition to the range of motion and accuracy, the agility of a manipulation system is an important parameter, but it has been so far underrated in the literature. To begin high-speed micromanipulation, obtaining 3-D positions of both the target microobject and the end effector rapidly is necessary. Controlling the residual vibration of the end effector, which is greater at high speed, is another arduous task. Successful releasing of objects in microscale is also demanding. We propose a new fast detection algorithm for both the target microobject and the end effector, which will enable us to achieve high-speed control of the system. Moreover, to realize stable grasping for very fast movements, the vibration of the system is compensated, and a controllable vibration is applied to the end effectors while performing the releasing task. High-speed control of the microhand system is demonstrated with extensive experiments consisting of pick-and-place actions of 40–60 \mu\m microspheres; we aimed at performing the task in 1 s. A comparison with similar studies shows the advantage of the proposed automated high-speed micromanipulation system. [ABSTRACT FROM PUBLISHER]