Application of Deep Learning in Autonomous Mobile Robot Control: An Overview
Bibliographic record
Abstract
Autonomous mobile robots (AMRs) are reshaping industries by automating tasks across diverse sectors, including logistics, healthcare, agriculture, and manufacturing. Recent advancements in deep learning (DL) have enhanced traditional control systems, empowering AMRs to process high-dimensional sensor data, improve perception, and navigate complex, dynamic environments. These techniques enable AMRs to perform a wide array of sophisticated tasks, including real-time navigation in crowded and cluttered spaces, dynamic obstacle avoidance, and accurate object recognition in settings like warehouses and agricultural fields. Beyond industrial applications, AMRs are also making significant strides in healthcare, particularly in elderly care, where they provide personalized assistance, help with mobility, and monitor health metrics through advanced perception and control mechanisms. This paper provides an overview of DL techniques in AMR systems, examining the roles of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL). CNNs are explored for visual perception tasks such as object detection, scene understanding, and localization. RNNs are utilized for processing sequential and time series data, such as inertial measurement units or force/torque sensors, to enhance scene perception. Finally, RLs are applied to decision-making and path planning in uncertain and dynamic environments. The paper also addresses the growing role of DL in overcoming key challenges in AMR systems, including enhancing robustness to environmental variations, enabling scalability across diverse operational scenarios, and improving autonomous decision-making capabilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".