Collision avoidance for nonholonomic mobile robots among unpredictable dynamic obstacles including humans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it