Verification, Validation and Payload Identification of a Hardware-in-the-loop Simulation Platform for In-orbit Systems
Bibliographic record
Abstract
This thesis discusses the development of a hardware-in-the-loop simulation platform for emulating the contact of rigid bodies in orbit, such as an active debris removal mission of a Deorbiter CubeSat in low Earth orbit. The hardware-in-the-loop simulation platform, called the Mission Analysis Robotic Kit II (MARK-II), is located in Brampton at MDA Space’s Dynamic Robotic Emulation and Mixed Reality laboratory and features a custom serial manipulator that is equipped with a Force-Moment-Sensor (FMS) to emulate the contact dynamics of free-floating bodies. The motion of the Deorbiter CubeSat is simulated in MARK-II software, and the force or moment measured by the FMS accelerates the simulated CubeSat according to the programmed inertia. The tip of MARK-II carries a CubeSat attachment mechanism payload that emulates CubeSat motion and reacts to contact as if it were an in-orbit inertial body. MARK-II is verified and validated according to the testing needs of the Deorbiter CubeSat, which indicate that the MARK-II FMS does not accurately measure the contact force and moment required for testing the Deorbiter CubeSat due to sensor noise, gravity, and payload dynamics. To address this, the FMS is replaced with a more accurate FMS model and two algorithms are proposed to estimate the mass, centre-of-mass position, and moment of inertia tensor of the payload. The first algorithm uses joint telemetry data to estimate payload parameters. The second algorithm uses the FMS and an Inertial Measurement Unit (IMU) attached to the payload to calculate the payload parameters, which is more accurate for estimating payload parameters according to experimental results. The payload mass and inertia parameters are utilised in a Kalman filter that fuses sensor data from the FMS and the IMU to filter out payload gravity and dynamics and accurately measure the contact wrench on the payload. Experimental results show that the proposed Kalman filter successfully filters out sensor noise and the weight and dynamics of the payload while preserving accurate measurement of the contact force and torque.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".