FPGA implementation of fuzzy wall-following control
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
The objective of this study concerns the design and implementation of a complete intelligent mechatronic system. The basic idea uses the concept of car maneuvers; control (fuzzy logic controller) and sensor-based behaviors together merged to implement the wall-following control algorithm. The fuzzy logic control algorithm (FLC) was considered as the heart of the controller due to the advantage of its easy implementation on an FPGA (field programmable gate array). The FLC is implemented on a compact custom FPGA board, which provides a powerful reconfigurable hardware platform and software design, at the same time. Complementing the system, a CPU synthesized on the FPGA takes care of interfacing with the external world. The FPGA board and the hardware network are demonstrated in the form of a controller embedded on the prototype car for a task of wall-following and obstacle avoidance. Experimental results on a car-like robot show that the algorithm proposed can successfully navigate the robot to follow the wall in an unknown and changing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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