Collision-free single-step motion planning of biped pole-climbing robots in spatial trusses
Bibliographic record
Abstract
For a biped pole-climbing robot (BiPCR) with dual grippers to climb poles, trusses or trees, a feasible collision-free climbing path is inevitable. In this paper, we utilize the sampling-based algorithm, Bi-RRT, to plan a feasible single-step collision-free climbing motion for BiPCRs in spatial trusses. Under the orientation limit of a 5-DoFs BiPCR, a new state representation along with corresponding operations including sampling, metric calculation and interpolation is presented. A simple but effective model of BiPCRs in trusses is proposed, through which the climbing path planning problem is transformed to be similar to that of an industrial robot. In addition, the pre- and post- processes are introduced not only to expedite the convergence of the Bi-RRT, but also to ensure the safe movement for the robot near the poles. The effectiveness and efficiency of the presented Bi-RRT algorithms for climbing motion planning are verified in the simulation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".