Prognostics-enhanced Receding Horizon Mission Planning for Field Unmanned Vehicles
Bibliographic record
Abstract
This paper presents a preliminary study for using prognostic information to enhance the mission/path planning in a non-uniform environment. Prognostic information is introduced in order to ensure that the mission failure can be minimized even when a fault occurs. This will enhance the performance of autonomous vehicles that often work in harsh environments that cause aging, fatigue, and fracture. When a fault occurs, the proposed path planning scheme predicts the remaining useful life (RUL) of the vehicle. This RUL is then used as a constraint in path planning to minimize the life consumption with other factors such as minimization of energy and travel time. The proposed planning algorithm integrates the prognosis and path planning in a receding horizon planning framework. Like field D* searching algorithm, the map is described by grids while nodes are defined on corners of grids. The planning algorithm divides the map into three areas, implementation area, observation area, and unknown area. We assume that the autonomous vehicle is equipped with onboard sensors that are able to detect and determine the terrain in a certain range, which is observation area. The implementation area consists of the gird next to the current node. The area beyond observation area is the unknown (un-observed) area where the terrain is unknown to vehicle. At a node, the vehicle plans the path from the vehicles’ current location to the destination. Only the path planned in the implementation area is executed. This process is repeated until the destination is reached or it turns out that no route can lead to destination or the vehicle reaches its end of life. The simulation results demonstrate the effectiveness of the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".