The development of sensor system that is built into a hand of a humanoid robot toward environmental monitoring is presented in this paper. The developed system consists of a color C-MOS camera, a laser projector with a lens distributing a laser light, and a LED projector. The sensor system can activate/disable these components according to the purpose. This paper introduces the design process, pre-experimental results for evaluating components, and the specifications of the developed sensor system together with experimental results.
In recent years, several low-price 3D laser scanners are being brought to the market and 3D laser scanning has been widely used in many applications. For example, 3D modeling of architectural structures and digital preservation of cultural heritages are typical applications for 3D laser scanning. Despite of the development of light-weight and high-speed laser scanners, complicated measurement procedure and large measurement time are still heavy burden for the widespread use of laser scanning. We have proposed a robotic 3D scanning system using multiple robots named CPS-SLAM, which consists of parent robots with a 3D laser scanner and child robots with corner cubes. This system enables to perform 3D laser scanning without complicated post-processing procedures such as ICP, and large-scale 3D models can be acquired by an on-board 3D laser scanner from several positions determined precisely by the localization technique named Cooperative Positioning System, CPS. This paper proposes an automatic planning technique for an efficient laser measurement for the CPS-SLAM system. By planning a proper scanning strategy depending on a target structure, it is possible to perform laser scanning efficiently and accurately even for a large-scale environment. Proposed technique plans an optimal scanning strategy automatically by taking several criteria, such as visibility between robots, error accumulation, and efficient traveling, into consideration. We conducted computer simulation and outdoor experiments to verify the performance of the proposed technique.
This paper proposes a novel probabilistic neural network and a prosthetic arm control system designed to prevent unintended forearm motions caused by anomalies. The network incorporates a Gaussian mixture model and a one-versus-the-rest classifier model, and can be used to estimate the posterior probability of predefined and undefined classes through training with given data. The control system incorporates the proposed network, thereby preventing unintended prosthetic arm motion in which an unexpected action is performed. In experiments conducted with a forearm amputee and two healthy subjects, electromyogram (EMG) classification ability of the proposed network was demonstrated, including for cases with unlearned motions. The results of the experiments also showed that the system enables smooth prosthetic arm control using EMG signals while preventing malfunctions caused by anomalies.
In our research, we focus on the information collection in multi-field by aerial vehicle. We develop hardware mechanism generating more efficient lift power to achieve the ability to tilt with continuous and infinite rotation and keep arbitrary tile angle while flying. In this work, we propose a new mechanism for aerial robot including four propellers separated to two mutually connected bi-copter modules. We call this new aerial robot Bi2Copter. With the proposed mechanism, we are able to realize take-off, landing and flight under vertical orientation of body, leading to the feasibility of image capture with full 360° spherical coverage as well as exploration and measurement parallel to the target surface of which curvature changes continuously. This paper presents the motivation to this new aerial robot, the design of proposed mechanism and flight control accompanied with experiment results.