Development of Novel Steering Scenarios for an 8X8 Scaled Electric Combat Vehicle
Bibliographic record
Abstract
This work investigates the steering and wheel speed control of a completely custom built 8x8 scaled electric combat vehicle (SECV) which has been constructed to meet the Ackermann condition at low speeds. During remote control operation the scaled vehicle is capable of continuously maintaining and varying the individual wheel speed and individual wheel steering angles of all eight wheels in real time. Several steering scenarios have been developed including traditional (front 2-axle steering), fixed third axle (first, second and fourth axle steering), all wheel steering and crab steering (all wheels are parallel with same steering angle). The traditional, two axle steering scenario is experimentally tested for accuracy in this work with planned future research for experimental analysis of the other steering configurations. This work is conducted using Arduino software to control the physical SECV and TruckSim software to simulate the dynamics of the vehicle. The results obtained from the physical testing of the wheel angular velocity were validated using a handheld tachometer device. The steering angle measurement of each wheel was validated using linear actuator sensors. It was seen that the physical results from the SECV are within acceptable range of the theoretical data calculated and simulated in Trucksim Software. The continuous steering method is applied by investigating the relationship between the steering angles of all eight wheels while operating the steering system from zero to the maximum steering angle of the 1st axle inner wheel during a turn. A major contribution of this work is a novel physical experimentation of the continuous Ackermann relationship for eight wheels. During testing of the traditional two-axle steering configuration the metrics of performance that were reviewed include: wheel speed, center velocity, yaw rate, and eight-wheel steering angles. With these metrics being compared with the Trucksim simulation, the experimental results obtained from the scaled 8x8 electric combat vehicle are a solid foundation for the development of future full-size 8x8 electric combat 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 | 0.000 | 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) | 0.003 | 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".