The use of LoRa technology as an alternative to GPS in the navigation of a mountain vehicle intended for people with special needs
Bibliographic record
Abstract
The navigation system for such a vehicle may use hardware solutions that differ in price, functionality, user-friendliness and problems associated with their operation. The navigation of such an off-road vehicle may be based on: GPS, LoRa wireless communication, and IMU inertial units. However, as the first half of 2024 has shown, this system is experiencing significant disruptions. In extreme situations, it may even be disabled (e.g. because of warfare). Where special-purpose off-road vehicles for PWSN are operated in mountainous terrain, especially on rocky ground with various structures, IMUs are particularly susceptible to errors building up during travel. This article addresses the research on the LoRa technology envisaged for implementation in a navigation system intended for this type of vehicle. Given the foregoing premises, it is crucial to determine the possibility of effective exchange of information in the transmitter-receiver system using the LoRa protocol for purposes of communication in difficult mountainous terrain, in the presence of obstacles, and often under harsh weather conditions. The pilot studies discussed in this article were conducted with the above problems in mind. This article formulates assumptions for a navigation system based on the LoRa standard. Based on the studies conducted by the authors, both the implementation validity as well as the advantages and disadvantages of the solution proposed have been described. The research results imply that the solution in question can be treated as an alternative if the GPS signal is either unavailable or significantly disturbed, and its additional features provide significant support for PWSN using off-road vehicles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".