Energy-Aware Coordination of Heterogeneous Robotic Systems with Mobile Charging Stations for Long-Term Environmental Monitoring
Bibliographic record
Abstract
This paper presents an optimization-based control framework for energy-aware control of a heterogeneous robotic system deployed for long-term environmental monitoring applications. The robotic system is comprised of a mobile charging station controlled to provide the required energy resources to a robot whose tasks have to be executed continuously and persistently. The charging station is assumed to be able to harvest energy from the environment (e.g. via solar panels) and is controlled to maximize the collected energy. We propose suitable models for the energy dynamics of both robots, as well as models for the environment dynamics. The energy sufficiency of the heterogeneous robotic system is translated into the forward invariance of a subset of the system state space and turned into a control input constraint by leveraging Control Barrier Functions (CBFs). The resulting control algorithm is a convex optimization problem that is solved online. The feasibility of the optimization problem is analyzed and sufficient conditions to ensure it are provided in terms of model parameters. The effectiveness of the controlled system at ensuring energy awareness is validated both in simulation as well as on real robotic platforms.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".