Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles
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
With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.
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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 | 0.000 | 0.000 |
| 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