Dual-UAV Payload Transportation Using Optimized Velocity Profiles via Real-Time Dynamic Programming
Bibliographic record
Abstract
In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor λ. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".