Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous navigation plays an increasingly crucial role in rover-based planetary missions. End-to-end navigation approaches developed upon deep reinforcement learning have enabled great adaptability in complex environments. However, most existing works focus on geometric obstacle avoidance thus have limited capability to cope with ubiquitous non-geometric hazards, such as sinkage and slippage. Autonomous navigation in unstructured harsh environments remains a great challenge requiring further in-depth study. In this letter, a DRL-based navigation method is proposed to autonomously guide a planetary rover towards goals via hazard-free paths with low wheel slip ratios. We introduce an end-to-end network architecture, in which the visual perception and the wheel-terrain interaction are fused to learn the representation of terrain mechanical properties implicitly and further facilitate policy learning for non-geometric hazard avoidance. Our approach outperforms baseline methods in simulation evaluation with superior avoidance capabilities against geometric obstacles and non-geometric hazards. Experiments conducted at a Mars emulation site suggest the successful deployment of our approach on a planetary rover prototype and the capacity of dealing with locomotion risks in real-world navigation tasks.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it