A Systems Engineering Approach to High-Level Task Execution: A Case Study in Robotic Lawn Mowing Using LIMO and Gazebo
Bibliographic record
Abstract
The successful deployment of autonomous systems hinges on the effective integration of perception, planning, and control subsystems. This paper presents a systems engineering case study focused on the verification and validation of a high-level task scheduling framework in the context of service robotics. We demonstrate the feasibility of this framework by applying it to a structured lawn mowing scenario, where the high-level execution plan is generated by the framework and translated into actionable commands within a Gazebo simulation. Using a LIMO robot model, we implement the complete plan in a realistic simulation environment, validating both the interoperability of system components and the practicality of the abstract plan. The results confirm that the framework's output can be effectively interpreted and executed on a realworld robot model, demonstrating a critical step in the systems engineering life cycle. This work provides a concrete methodology for validating abstract planning frameworks through simulation and reinforces the value of integrated, simulationbased verification in robotics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".