Two-Stage Fuzzy Logic-Based Controller for Mobile Robot Navigation
Bibliographic record
Abstract
A two-stage fuzzy inference system for a sonar sensor-based mobile robot is presented. The motion of the robot has been simulated as well as experimentally tested. Target seeking, obstacle avoidance, and emergency behaviors have been defined in a hierarchical fashion. The first stage of the fuzzy control system outputs an angular velocity based on the robot's five sonar sensor readings. The angular velocity and a measure of average object proximity, or freespace, is then fed into the second stage of the fuzzy controller and linear velocity is output. The freespace value is a measure of the openness of the immediate environment. Incorporating this variable in the fuzzy controller enables the robot to adapt to the environment and move at a more suitable speed. In our tests, the robot was able to react both quickly and correctly to the perceived sensor data as it navigated its way through a variety of environments. Testing has identified several system parameters that, when modified, can significantly affect the performance of the controller. A solid foundation is laid for possible optimization of these parameters via a learning algorithm
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".