Assigning Closely Spaced Targets to Multiple Autonomous Underwater Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This research addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles (AUV) in the presence of constant ocean currents. The main difficulty of this problem is that the non-holonomic vehicles are constrained to move along forward paths with bounded curvatures. The Dubins model is a simple but effective way to handle the kinematic characteristics of AUVs. It gives complete characterization of the optimal paths between two configurations for a vehicle with limited turning radius moving in a plane at constant speed. \n \n In the proposed algorithm, Dubins paths are modified to include ocean currents, resulting in paths defined by curves whose radius of curvature is not constant. To determine the time required to follow such paths, an approximate dynamic model of the AUV is queried due to the computational complexity of the full model. The lower order model is built from data obtained from sampling the full model. The full model is used in evaluating the final tour times of the sequences generated by the proposed algorithm to validate the results. \n \n The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. The novelty of the research is the path cost calculation that combines a Dubins model, an AUV dynamic model, and a model of the ocean current. Simulations were conducted in Matlab to illustrate the performance of the proposed algorithm using various number of task points and AUVs. The task points were generated randomly and uniformly close together to highlight the necessity for considering the curvature constraints. \n \n For a sufficiently dense set of points, it becomes clear that the ordering of the Euclidean tours are not optimal in the case of the Dubins multiple travelling salesmen problem. This is due to the fact that there is little relationship between the Euclidean and Dubins metrics, especially when the Euclidean distances are small with respect to the turning radius. An algorithm for the Euclidean problem will tend to schedule very close points in a successive order, which can imply long maneuvers for the AUV. This is clearly demonstrated by the numerous loops that become problematic with dense sets of points. The algorithm proposed in this thesis does not rely on the Euclidean solution and therefore, even in the presence of ocean currents, can create paths that are feasible for curvature bound vehicles. \n \n Field tests were also conducted on an Iver2 AUV at the Avila Pier in California to validate the performance of the proposed algorithm in real world environments. Missions created based on the sequences generated by the proposed algorithm were conducted to observe the ability of an AUV to follow paths of bounded curvature in the presence of ocean currents. Results show that the proposed algorithm generated paths that were feasible for an AUV to track closely, even in the presence of ocean current.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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