Fuzzy situation based navigation of autonomous mobile robot using reinforcement learning
Bibliographic record
Abstract
Design of high-level control systems for autonomous agents, such as mobile robots, is a challenging task. The complexity of robotic tasks, the number of inputs and outputs of such systems and their inherent ambiguity preclude the designer from finding an analytical description of the problem. Using the technology of fuzzy sets, it is possible to use general knowledge and intuition to design a fuzzy control system that encodes the relationships in the control domain into the form of fuzzy rules. However, control systems designed in this way are severely limited in size and are usually far from being optimal. In this paper, several techniques are combined to overcome such limitations. The control system is selected in the form of a general fuzzy rule based system. Antecedents of this system correspond to various situations encountered by the robot and are partitioned using a fuzzy clustering approach. The consequents of the rules describe fuzzy sets for change of heading necessary to avoid collisions. While the parameters of input and output fuzzy sets are designed prior to robot engagement in real world, the rules to govern its behaviour are acquired autonomously endowing the robot with the ability to continuously improve its performance and to adapt to changing environment. This process is based on reinforcement learning that is well suited for on-line and real-time learning tasks.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".