VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS
Bibliographic record
Abstract
In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.
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.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".