Sharing the Load: Autonomous Multi-Rover Cargo Transport
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
A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to the Moon will need to be moved from a landing site to other key locations that may be up to 5 km away. Teach and repeat navigation is well suited to this task as utility rovers will need to repeat these cargo routes many times. One of the most significant challenges involves the modules that will be assembled together to form the habitat. Canada is studying potential Lunar Utility Vehicle (LUV) designs to carry these large payloads between the landing site and the location of the habitat. As the details of the cargo continue to evolve, using two, smaller LUVs to carry cargo together would provide high capacity and mission flexibility. In this paper, we develop and implement a distributed model-predictive controller that allows vehicles to carry cargo that is shared between them. The algorithm is compared to baselines in small-scale before being implemented onboard two 800 kg path-to-flight rovers and field tested carrying a 475 kg cargo between them. A custom cargo coupling decouples the kinematics of each vehicle while fully supporting the cargo's mass. In our field test, the rovers maintain a relative separation error of 9.2 cm and maximum error of 33.4 cm. This multi-vehicle control architecture retains the high-quality path tracking of lidar teach and repeat for each rover. We demonstrate that kinematic freedom of the vehicles allows a single controller to provide mission improvements for other operations as well.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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