Multi-Robot Path Planning for Mobile Sensing through Deep Reinforcement Learning
Bibliographic record
Abstract
Mobile sensing is an effective way to collect environmental data such as air quality, humidity and temperature at low costs. However, mobile robots are typically battery powered and have limited travel distances. To accelerate data collection in large geographical areas, it is beneficial to deploy multiple robots to perform tasks in parallel. In this paper, we investigate the Multi-Robot Informative Path Planning (MIPP) problem, namely, to plan the most informative paths in a target area subject to the budget constraints of multiple robots. We develop two deep reinforcement learning (RL) based cooperative strategies: independent learning through credit assignment and sequential rollout based learning for MIPP. Both strategies are highly scalable with the number of robots. Extensive experiments are conducted to evaluate the performance of the proposed and baseline approaches using real-world WiFi Received Signal Strength (RSS) data. In most cases, the RL based solutions achieve superior or similar performance as a baseline genetic algorithm (GA)-based solution but at only a fraction of running time during inference. Furthermore, when the budgets and initial positions of the robots change, the pre-trained policies can be applied directly.
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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 | 0.001 | 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".