BEHAVIOUR-DEFINED NAVIGATION FRAMEWORK FOR DYNAMICAL OBSTACLE AVOIDANCE IN MULTI-ROBOT SYSTEMS CONSISTING OF HOLONOMIC ROBOTS, 379-390.
Bibliographic record
Abstract
Dynamical obstacle avoidance is a challenging problem in the field of autonomous robot navigation. Current research in this field has been mostly limited to single robots, thus, there exists a gap in research in the field of dynamical obstacle avoidance in multi robot systems. While rich literature is available on multirobot systems, this paper attempts to propose a novel navigation framework for an environment which includes multiple robots. The proposed navigation framework applies certain behaviours to ensure a safe trajectory for the multi-robot systems. As opposed to other reported literature which focused on implementing their algorithms on non-holonomic robots, the proposed navigation framework is implemented on several holonomic robots. Simulations and real-life experiments were carried out using the proposed framework. Dynamic obstacles are considered in the environment and Khepera IV robots are used to conduct real-life experiments. Two dynamic obstacles were placed at different positions in the workspace. These obstacles had linear movements, whereby each robot could move horizontally and vertically across the workspace. Three experimental trials were performed. Results show that the proposed navigation framework is successful in navigating the multi-robot system to their respective target locations while avoiding obstacles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".