DRL-based Trajectory Planning and Sensor Task Scheduling for Edge Robotics
Bibliographic record
Abstract
Mobile Edge Computing (MEC) and Edge Robotics have recently emerged as transformative technologies, revolutionizing industries by enabling real-time processing, decision-making, and automation at the network edge. However, the dynamicity induced by the system’s conditions and specifically the mobility poses a challenge for optimally deciding where to execute a given computational task. As a response, we develop an intelligent algorithm for dynamic sensor task offloading tailored to the unique requirements of MEC-enabled robotic environments. Specifically, we first introduce the environmental dynamics including a sensor task’s end-to-end delay and the robots’ mobility and energy consumption and provide mathematical formulations to model these dynamics. Then, we mathematically formulate the optimization problem and its MDP counterpart and we propose a Deep Reinforcement Learning (DRL)-based computational offloading strategy to jointly optimize Quality of Service (QoS) and energy consumption through robot trajectory planning and fine-grained task allocation. Through hand-picked representative simulation scenarios, we demonstrate the superiority of our proposed mechanism in enhancing the overall system performance, specifically in optimizing task execution, reducing energy consumption, and mitigating transmission delays, compared to various baseline approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".