Real-time multi-robot path planner based on a heuristic approach
Bibliographic record
Abstract
The authors describe a real-time approach to solve the trajectory planning problem using the latest feedback of robot joint locations from robot controllers. The approach incorporates an efficient collision avoidance strategy to allow decisions to be made during execution by means of cylindrical robot link approximation. The developed algorithm has been verified with a dual-robot planning and control system for the mechanical assembly of a dish-washer power unit. The system consists of an ADEPTI and a PUMA 560 industrial robot running under the control of a Sun-4 Sparc 2 workstation at the high control level. A coarse motion planner is available to prevent the arm from colliding with stationary objects. In this dual-arm system, a task level multi-agent plan is generated to specify the logical sequence of assembly. From the execution results, it was found that even when the robot speed was varied from 25% to 80% of its full capacity, a collision-free path was found for each robot.>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".