Optimal Trajectory Planning and Compliant Spacecraft Capture Using a Space Robot
Bibliographic record
Abstract
Due to the ever-increasing volume of on-orbit debris, determining viable means of capturing and removing said debris has become vital.This thesis proposes solutions to the first two of three phases during debris removal: namely, deployment of a robotic manipulator, and the capture of a target spacecraft.For the deployment phase, to solve the nonlinear trajectory planning problem for free-floating robots, this thesis proposes the use of pseudospectral methods.Using three different pseudospectral optimal control tools, simulations are performed and it is shown that each tool is capable of finding a unique local minima, which ensures zero attitude change by the end of the deployment.Each solution is then validated using Pontryagin's minimum principle, Bellman's principle of optimality, and by propagating the control torques using a numerical integrator and the dynamics model.Experimental validation is performed at Carleton University's Spacecraft Robotics and Control Laboratory to further investigate the solutions obtained from each tool.Ultimately, it is determined that all solutions resulted in a reduced attitude disturbance at the end of the deployment manoeuvre.After the deployment phase, a nonlinear disturbance observer-based impedance controller is proposed for the compliant capture of free-flying spacecraft using a free-flying robot.An existing nonlinear disturbance observer that can be used to determine the end-effector disturbance for a fixed-base manipulator without knowledge of the second state derivatives is extended for use with a free-flying robot.The observer is shown to be asymptotically stable.The observer is then paired with an impedance controller in a simulated contact scenario, and the response is shown to be stable and compliant even in the presence of noise.Furthermore, the estimated contact force from the observer is compared to the actual disturbance, and the error converges asymptotically to zero.Finally, the proposed technique is successfully experimentally validated at Carleton University's Spacecraft Robotics and Control Laboratory.iii First and foremost, I want to express my gratitude to Steve Ulrich, my supervisor.Your patience and guidance helped make this thesis possible, hopefully you also learned something during our time working together.I also want to thank my friends and colleagues; in particular, thanks to Kirk Hovell (who, for all intents and purposes, built the lab from the ground up) and Justin Kernot (whose excellence in design work was fundamental to the success of the experiments in this thesis).Thanks as well to my family, to whom this thesis is dedicated to -family is the most important part of my life
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".