ESA multibody simulator for spacecrafts’ ascent and landing in a microgravity environment

Toso, Mario; Pennestrı̀, Ettore; Rossi, Valerio

doi:10.1007/s12567-015-0081-5

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…PyJama implements fusion filters in quaternions. The motivation for using quaternions is the data stability, considering that the rotation using Euler angles can present more errors and the occurrence of Gimbal Lock [ 12 , 13 ]. Thus, Complementary Filter(CF) with Gradient Descent and Gauss–Newton [ 6 , 14 ], Kalman Filter (KF) with Gradient Descent and Gauss–Newton [ 6 , 14 ], and MadgwickAHRS [ 5 ] were implemented.…”

Section: Methodsmentioning

confidence: 99%

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Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Almeida

Morya

Rodrigues

et al. 2021

Sensors

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The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

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Section: Methodsmentioning

confidence: 99%

“…Structurally, the class functions use the matplotlib. In addition, users can choose to use and visualize the data with the orientation in quaternions or Euler angles, the last is the most common way to visualize joint rotations [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Almeida

Morya

Rodrigues

et al. 2021

Sensors

View full text Add to dashboard Cite

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Deep learning and machine learning techniques for head pose estimation: a survey

Algabri,

Abdu,

Lee

2024

Artif Intell Rev

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Honey bees (Apis mellifera ligustica) swing abdomen to dissipate residual flying energy landing on a wall

Zhao

Yan

2017

Journal of Applied Physics

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Whether for insects or for aircrafts, landing is one of the indispensable links in the verification of airworthiness safety. The mechanisms by which insects achieve a fast and stable landing remain unclear. An intriguing example is provided by honeybees (Apis mellifera ligustica), which use the swinging motion of their abdomen to dissipate residual flying energy and to achieve a smooth, stable, and quick landing. By using a high-speed camera, we observed that touchdown is initiated by honeybees extending their front legs or antennae and then landing softly on a wall. After touchdown, they swing the rest of their bodies until all flying energy is dissipated. We suggested a simplified model with mass-spring dampers for the body of the honeybee and revealed the mechanism of flying energy transfer and dissipation in detail. Results demonstrate that body translation and abdomen swinging help honeybees dissipate residual flying energy and orchestrate smooth landings. The initial kinetic energy of flying is transformed into the kinetic energy of the abdomen's rotary movement. Then, the kinetic energy of rotary movement is converted into thermal energy during the swinging cycle. This strategy provides more insight into the mechanism of insect flying, which further inspires better design on aerial vehicle with better landing performance.

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ESA multibody simulator for spacecrafts’ ascent and landing in a microgravity environment

Cited by 3 publications

References 12 publications

Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Deep learning and machine learning techniques for head pose estimation: a survey

Honey bees (Apis mellifera ligustica) swing abdomen to dissipate residual flying energy landing on a wall

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