Strategies for Cooperative Energy Distribution on Multi-Robot Warehouse Systems
Bibliographic record
Abstract
Autonomous mobile robots are one of the new and innovative ways to improve operation in industries such as warehouses, logistic companies, agricultural businesses, healthcare institutions, and a lot more.They are known for their operational improvement, safety, efficiency, and speed, automating several functionalities so they can be performed with little or no human intervention.These advantages can only be realized, however, if the degree of autonomy suffices for the task at hand.Whilst many degrees of autonomy exist (e.g., functional autonomy), in this thesis we are primarily concerned with an aspect of non-functional autonomy: energy.One of the important features of an autonomous robot system is the capability to charge autonomously with little to no human intervention.This thesis examines the energy distribution problem on multi-robot warehouse systems.We model warehouse systems where robots charge autonomously, pausing their workload when needed, to charge at a station.Depending on specific execution, it is possible that robots fully deplete their energy before arriving at a charging station, decreasing total work achieved, and becoming an obstacle on the warehouse floor.We then introduce the concept of energy sharing, where robots are capable of charging one another, essentially becoming mobile charging stations.In this context, the problem of energy distribution becomes a problem of multi-agent collaboration.We analyze the impact of our solution on a multi-agent simulation, showing that energy sharing for autonomous mobile robots in warehouse systems reduces the total number of depleted robots and contributes to increasing the amount of work performed.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".