A New Approach to Manipulate Objects with a Team of Distributed Robots Based on Constrain-and-Move Strategy
Bibliographic record
Abstract
Based on constrain and move concept, a new algorithm CCM (complex Constrain and Move) is proposed to reorient objects with a distributed object handling mobile robots. In traditional constrain and move strategy, a group of robots constrain the object in undesirable directions and another group push the object to move it in desired path. In proposed CCM method, each robot does both constrain task and move task and the algorithm of robots are the same. A series of dynamic computer simulations are conducted to measure the efficiency of the new proposed algorithm for different experiments. The results of the simulations indicate that the system is stable in all experiments and it is more faults tolerant. Simulations also revealed that the minimum necessary numbers of robots in this method to handle the object is less than traditional constrain and move strategy. Introduction. A strong, complicated and expensive robot is needed to manipulate a heavy, big object. Such a robot usually depends on the shape of the object. The cooperation of some simple and cheap robots is a suitable way to manipulate objects with various shapes. Different
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".