Hard Real-Time General-Purpose Robotic Simulations of Autonomous Air Vehicles
Bibliographic record
Abstract
High-fidelity general-purpose robotic simulators are a special class of simulator designed to simulate all the components of a real-world robotics system, including autonomous air vehicles and planetary exploration rovers, so that a real-world system can be tested and verified before/during deployment on the real-world hardware. General-purpose robotic simulators can simulate sensors, actuators, obstacles, terrains, environments, physics, lighting, fluids, and air particles, while also providing a means to verify the system’s autonomous algorithms by using the simulated vehicle in place of the real-world one. General-purpose robotic simulators are typically coupled with an abstract robotic control interface so that autonomous systems evaluated on the simulated vehicles can be deployed, unchanged, on the corresponding real-world vehicles and vice versa. However, the problem with the current technology and research is that neither the robotic simulators nor the robotic control interfaces support Hard Real-Time capabilities, and cannot guarantee that Hard Real-Time constraints will be met. The lack of Hard Real-Time support has major implications on both the utility and the validity of the simulation results and the functioning of the realworld autonomous vehicle. As a solution, this paper will present Hard-RTSim, a novel hard real-time simulation framework that will: 1) Bring Hard Real-Time support to generalpurpose robotic simulators; and 2) Bring Hard Real-Time support to abstract robotic control interfaces. Hard-RTSim guarantees that simulated events in the environment or modeled vehicle are produced and handled with finite (bounded) accuracy and precision. Furthermore it improves these temporal responses to ensure these bounds are representative of temporal requirements for a wide range of scenarios. The Hard-RTSim framework ensures that the simulator and the hard real-time processes will actually get to use the CPU when they request/need it, no matter how many other processes are loaded on the CPU. The experimental results of using the Hard-RTSim framework compared to not using it yield a huge improvement in responsiveness and reliability. There is an improvement of 35% when the CPU is minimally loaded and then as the CPU load is increased the improvement increases as well, all the way up to a 98% improvement when the CPU is loaded at its maximum. These substantial improvements in precision and reliability will help to further the state of space exploration, aerospace technology, and produce better and more reliable autonomous aerial vehicles and planetary exploration rovers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".