Input Shaped Trajectory Generation and Controller Design for a Quadrotor-Slung Load System
Bibliographic record
Abstract
Quadrotor-slung load systems offer a versatile method for transporting payloads. With these systems, it is particularly important to mitigate and manage unwanted payload swinging through the careful design of flight trajectories and controllers. Tracking input shaped trajectories for instance is an effective way to minimize post-flight swinging for rest-to-rest maneuvers by allowing some swinging motions during flight. In this paper, we first present a computationally simple method to generate input shaped quadrotor flight trajectories for non-rest-to-rest maneuvers. We then propose a controller to track these shaped trajectories and permit the associated payload swinging while also rejecting unwanted swinging disturbances. The path planner and controller are implemented in simulation and the performance of the controller is compared against two simpler controllers for an input shaped u-turn maneuver both with and without an initial swinging disturbance. The results of these simulations demonstrate the effectiveness of our path planner, while also showing that the proposed controller outperforms both baseline controllers considered.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".