Collision avoidance for nonholonomic mobile robots among unpredictable dynamic obstacles including humans
Bibliographic record
Abstract
In many service applications, mobile robots need to share their work areas with obstacles. Avoiding collisions is a fundamental requirement for these robots. In this paper a novel collision avoidance system is developed for avoiding unpredictable dynamic obstacles, including humans. The collision avoidance algorithm is based on the virtual force field (VFF) concept. The velocities of the obstacles are used in addition to their positions to improve the avoidance performance for dynamic obstacles. Unlike prior algorithms, the proposed VFF is designed to be continuous to diminish both path oscillations and the time cost for reaching the goal. To further reduce the time cost, a new virtual force (termed the detour force) is introduced. The detour force also solves the challenging avoidance problem that occurs when the centers of the robot, human/obstacle and goal are collinear; and the human/obstacle and robot are moving towards each other. In simulations and experiments with a maximum approach velocity of 1.7 m/s, the avoidance system with the new VFF algorithm generates collision-free paths with less oscillation and lower time cost.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".