A Modular and Hierarchical Framework for Motion Planning with Feedback-based Motion Primitives
Bibliographic record
Abstract
As robots become more integrated into everyday society, an increasing emphasis is being placed on their ability to execute complex tasks while maintaining safety. One of the most fundamental tasks in motion planning and control is the coordination of multiple robots to safely and efficiently reach target destinations. In recent years, a hybrid systems approach, that which combines both continuous and discrete system descriptions, has proven to be an attractive methodology for control with complex specifications. Although there is extensive literature on both the design of continuous time feedback controllers and discrete motion planning algorithms, relatively few works address the rigorous integration of these two components, especially in the context of multi-vehicle coordination. In this dissertation, we leverage the hybrid systems paradigm to formulate a novel framework for motion planning and control of multi-vehicle systems that is modular, robust, and provably safe. This dissertation contains three distinct contributions. The first contribution considers the synthesis of low level continuous time feedback controllers for guiding system trajectories along a desired direction; to this end, an open problem in classical linear quadratic control was solved. The second contribution broadens the scope to develop a modular motion planning framework that combines low level feedback-based motion primitives with high level planning algorithms. Finally, the third contribution extends this modular framework towards a multi-hierarchy of motion primitives in order to improve scalability with respect to the number of vehicles. Both the second and third contributions include experimental validation on a collection of quadrocopters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".