Development and implementation of a tensegrity-based formation controller
Bibliographic record
Abstract
The formation control or cooperative work of multiple unmanned vehicles has proved to be\nmore effective in terms of time consumption and overall performance in a wide variety of\napplications. This project aims to review some of the research which has been done up to date and in the subsequent chapters a tensegrity-based formation controller is developed and implemented to assess its global behaviour. This development starts with the design of the heading angle control of a vessel based on the first order Nomoto model which is later transformed into a waypoint problem. Later on, a globally decentralised formation topology and the tensegrity concept is introduced in the context of formation control where a sliding mode controller, a linear-quadratic regulator servo controller and a conventional\nproportional-integral-derivative controller are designed and simulated to regulate the elongation of a virtual spring-mass-damper system which connects two vehicles. This is done by applying a force to this system which, from the point of view of the reference vehicle, can be regarded as a repelling force if the formation distance is smaller than the reference and attracting otherwise. The results obtained are satisfactory proving stability and feasibility of the proposed strategy and the limitations and drawbacks of each control technique used are described. However, it is observed that the LQR servo controller yields a better balance between performance and robustness. Afterwards, the tensegrity concept is implemented on a leader-light-guided-follower formation topology using the LEGO® Mindstorms NXT platform. The distance keeping evolution between the follower and the leader is assessed positively for both cases, a PID and LQR servo controller, however, this latter with better tracking while the leader is moving.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".