Design of a Low Cost Motion Data Acquisition Setup for Mechatronic Systems

Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor

Design of a Low Cost Motion Data Acquisition Setup for Mechatronic Systems

Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor

DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories

This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate microgravity environments on Earth. Our approach addresses the system and environmental uncertainties in controlling such platforms by training policies capable of precise maneuvers amid dynamic and unpredictable conditions. Leveraging state-of-the-art deep reinforcement learning techniques, our suite achieves robustness, adaptability, and good transferability from simulation to reality. Our Deep Reinforcement Learning (DRL) framework provides advantages such as fast training times, large-scale testing capabilities, rich visualization options, and ROS bindings for integration with real-world robotic systems. Beyond policy development, our suite provides a comprehensive platform for researchers, offering open-access at https://github.com/elharirymatteo/RANS/tree/ICRA24

Advances in Control Techniques for Floating Platform Stabilization in the Zero-G Lab

peer reviewedThe study presents a novel control approach for managing floating platforms in the unique environment of a zero-gravity laboratory (Zero-G Lab) of University of Luxembourg. These platforms are pivotal for diverse experiments and technologies in space. Our solution combines Model Predictive Control (MPC) and Proportional-Derivative (PD) control techniques to ensure precise positioning and stability. The MPC algorithm generates optimal trajectories based on predictive platform models, adjusting paths for minimal effort. Augmented by a PD controller using feedback from the Optitrack motion system, real-time adjustments maintain stability by considering platform state, position, and orientation data. Extensive simulations and experiments within the Zero-G Lab demonstrate the effectiveness of our approach. The MPC-PD strategy accurately controls platforms, making them resilient against external disturbances and human interactions. This strategy holds promise for space exploration, microgravity experiments, and beyond, offering adaptable control in zero-gravity conditions

Enhancing Rover Teleoperation on the Moon With Proprioceptive Sensors and Machine Learning Techniques

Geological formations, environmental conditions, and soil mechanics frequently generate undesired effects on rovers’ mobility, such as slippage or sinkage. Underestimating these undesired effects may compromise the rovers’ operation and lead to a premature end of the mission. Minimizing mobility risks becomes a priority for colonising the Moon and Mars. However, addressing this challenge cannot be treated equally for every celestial body since the control strategies may differ; e.g. the low latency EarthMoon communication allows constant monitoring and controls, something not feasible on Mars. This letter proposes a Hazard Information System (HIS) that estimates the rover’s mobility risks (e.g. slippage) using proprioceptive sensors and Machine Learning (supervised and unsupervised). A Graphical User Interface was created to assist human-teleoperation tasks by presenting mobility risk indicators. The system has been developed and evaluated in the lunar analogue facility (LunaLab) at the University of Luxembourg. A real rover and eight participants were part of the experiments. Results demonstrate the benefits of the HIS in the decision-making processes of the operator’s response to overcome hazardous situations

Lightweight Floating Platform for Ground-Based Emulation of On-Orbit Scenarios

peer reviewe

ET-Class, an Energy Transfer-based Classification of Space Debris Removal Methods and Missions

Space debris is positioned as a fatal problem for current and future space missions. Many e ective space debris removal methods have been proposed in the past decade, and several techniques have been either tested on the ground or in parabolic ight experiments. Nevertheless, no uncooperative debris has been removed from any orbit until this moment. Therefore, to expand this research eld and progress the development of space debris removal technologies, this paper reviews and compares the existing technologies with past, present, and future methods and missions. Moreover, since one of the critical problems when designing space debris removal solutions is how to transfer the energy between the chaser/de-orbiting kit and target during the rst interaction, this paper proposes a novel classi cation approach, named ET-Class (Energy Transfer Class). This classi cation approach provides an energy-based perspective to the space debris phenomenon by classifying how existing methods dissipate or store energy during rst contact

Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris

Active debris removal (ADR) is positioned by space agencies as an in-orbit task of great importance for stabilizing the exponential growth of space debris. Most of the already developed capturing systems are designed for large specific cooperative satellites, which leads to expensive one-to-one solutions. This paper proposed a versatile hybrid-compliant mechanism to target a vast range of small uncooperative space debris in low Earth orbit (LEO), enabling a profitable one-to-many solution. The system is custom-built to fit into a CubeSat. It incorporates active (with linear actuators and impedance controller) and passive (with revolute joints) compliance to dissipate the impact energy, ensure sufficient contact time, and successfully help capture a broader range of space debris. A simulation study was conducted to evaluate and validate the necessity of integrating hybrid compliance into the ADR system. This study found the relationships among the debris mass, the system’s stiffness, and the contact time and provided the required data for tuning the impedance controller (IC) gains. This study also demonstrated the importance of hybrid compliance to guarantee the safe and reliable capture of a broader range of space debris

The Best Space Resource is the One You Can Catch and Reuse

From the beginning of space exploration more than 60 years ago, only a few in-orbit objects have been removed or reused. In fact, the Kessler Syndrome states that the number of space debris is growing exponentially [1], leaving unused uncooperative objects orbiting at high velocities at several altitudes, especially in Low-Earth Orbit (LEO). In other words, the situation brings up two main critical issues: not only a non-sustainable space environment for satellite missions, with orbit saturation, but also the creation of an unsafe place for future human-related space exploration missions. Active Debris Removal is a possible solution for tackling the problem of space debris. Despite being extremely challenging, catching autonomously and harmlessly an uncooperative object tumbling at high velocity demands reliability, compliance and robustness. The fruitful collaboration between industry and academia (Spacety Luxembourg - SnT-SpaceR research group at the University of Luxembourg), is leading to the cutting-edge concept of a two-step capturing mechanism. A first ‘soft capture’ ensures that the debris is received softly while dampening any vibrations generated during the contact. Then, a ‘hard capture’ secures the debris so that it would be deorbited or safely shipped for other orbits or space stations for reuse. Capturing debris and decommissioned in-orbit objects for recycling or reusing can be the anchor of new opportunities in space and beyond. Most of the objects in orbit can have aluminum parts, besides other beneficial materials among their subsystems, such as solar panels, antennas or electronics which can be reused. To maximize space resources reusability, it is important to not harm the target. Capturing solutions such as harpoons or rigid interfaces can cause damage to the targets, resulting in hardly exploitable resources, and even more smaller debris tumbling in orbit [2]. An application of the proposed capturing technology would be to collect defunct satellites and debris, thus contributing to a more sustainable environment in space, gathering those on a possible recycling orbit or to any future Space Station for recycling. References [1] Drmola J. and Hubik T., Kessler Syndrome: System Dynamics Model (2018), In-Space Policy, 44–45, 29–39 [2] Zhao P., Liu J. and Wu C., Survey on Research and Development of On-Orbit Active Debris Removal Methods (2020), Sci China Tech Sci, 63: 2188–221