Analysis of Charging of the HTV-4 Based on On-Orbit Data

After three H-II transfer vehicles (HTVs) had finished their mission to resupply the International Space Station (ISS), NASA requested data of the HTV\u27s potential to evaluate the charging/discharging process that occurs when the HTV docks to the ISS. To measure these data, a new instrument was installed on the fourth HTV. This instrument allows us to measure the HTV-4 surface potential relative to the surrounding plasma, and is called advanced technology on-orbit test instrument for space environment-mini (ATOTIE-mini). The ATOTIE-mini observed the HTV\u27s local potential in the orbit for more than one month. The measured potential during the HTV solo-flight phase varied between -30 and -60 V in sunlight and was about 0 V in eclipse conditions. The HTV\u27s potential during the time when it was docked to the ISS followed the ISS\u27s potential with an almost constant offset of about 10 V. The data measured by ATOTIE-mini are consistent with those measured by the floating potential measurement unit on the ISS, and thus are considered reliable. The HTV\u27s potential level itself was acceptable for ISS. Note that the solar array panels can generate up to approximately 120 V, which is much larger than the absolute potential range in sunshine. We analyze the potential distribution on the HTV surface by a multi-utility spacecraft charging analysis tool, because ATOTIE-mini can only observe one point on the HTV surface. The analysis results are discussed with respect to the flight attitude

Improved 3D Human Motion Capture Using Kinect Skeleton and Depth Sensor

Kinect has been utilized as a cost-effective, easy-touse motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks' positions restrict Kinect's capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion's kinematic parameters were calculated using the landmarks' positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin's concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460 degrees) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843 degrees)

Moving Particle Semi-Implicit and Finite Element Method Coupled Analysis for Brain Shift Estimation

Neuronavigation is a computer-assisted technique for presenting three-dimensional images of a patient's brain to facilitate immediate and precise lesion localization by surgeons. Neuronavigation systems use preoperative medical images of patients. In neurosurgery, when the dura mater and arachnoid membrane are incised and the cerebrospinal fluid (CSF) drains out, the brain loses the CSF buoyancy and deforms in the direction of gravity, which is referred to as brain shift. This brain shift yields inaccurate neuronavigation. To reduce this inaccuracy, an intraoperative brain shift should be estimated. This paper proposes a dynamic simulation method for brain-shift estimation combining the moving-particle semi-implicit (MPS) method and the finite element method (FEM). The CSF was modeled using fluid particles, whereas the brain parenchyma was modeled using finite elements (FEs). Node particles were attached to the surface nodes of the brain parenchyma in the FE model. The interaction between the CSF and brain parenchyma was simulated using the repulsive force between the fluid particles and node particles. Validation experiments were performed using a gelatin block. The gelatin block was dipped into silicone oil, which was then gradually removed; the block deformation owing to the buoyancy loss was measured. The experimental deformation data were compared with the results of the MPS-FEM coupled analysis. The mean absolute error (MAE) between the simulated deformation and the average across the four experiments was 0.26 mm, while the mean absolute percentage error (MAPE) was 27.7%. Brain-shift simulations were performed using the MPS-FEM coupled analysis, and the computational cost was evaluated

A Human-Like Approach Towards Humanoid Robot Footstep Planning

Humanoid robots posses the unique ability to cross obstacles by stepping over or upon them. However, conventional 2D methods for robot navigation fail to exploit this ability and thus design trajectories only by circumventing obstacles. Recently, global algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper, we present a novel approach to footstep planning in obstacle cluttered environments that employs a human-like strategy to terrain traversal. Design methodology for obstacle stepping over motion designed for use with this algorithm is also presented. The paper puts forth simulation results of footstep planning as well as experimental results for the stepping over trajectory designed for use with hardware execution of the footstep plan

A Human-Like Approach Towards Humanoid Robot Footstep Planning

Humanoid robots posses the unique ability to cross obstacles by stepping over or upon them. However, conventional 2D methods for robot navigation fail to exploit this ability and thus design trajectories only by circumventing obstacles. Recently, global algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper, we present a novel approach to footstep planning in obstacle cluttered environments that employs a human&#8208;like strategy to terrain traversal. Design methodology for obstacle stepping over motion designed for use with this algorithm is also presented. The paper puts forth simulation results of footstep planning as well as experimental results for the stepping over trajectory designed for use with hardware execution of the footstep plan