Network-Aware Path Planning for Autonomous MobileRobots in Industrial Environments Article Swipe
Kathalkar, Om
,
Hajj Hassan, Houssam
,
Kattepur, Ajay
,
Bouloukakis, Georgios
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17830291
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17830291
Autonomous Mobile Robots (AMRs) in industrial environments require reliable wireless connectivity for coordination,control, and safety operations. Traditional path planning algorithms focus solely on geometric constraints, often leadingrobots through areas with poor network coverage that can compromise mission-critical operations. This paper presents acomprehensive framework for network-aware path planning that incorporates wireless network quality metrics as path constraints. We validate our Sionna-based ray-tracing simulations against real-world measurements from the Hernang´omez et al. iV2I+ dataset, achieving strong correlation between simulated and realworld measurements (R² = 0.87 for SNR, 0.82 for throughput). Using this validated simulation framework, we implement three novel path planning algorithms: A* with network constraints, conditional variational autoencoder (CVAE)-based neural path planning, and Graph neural network-based multi-path(GraphMP) planning. Our evaluation demonstrates trade-offs between network quality requirements and path efficiency, withCVAE achieving 95.2% constraint satisfaction and GraphMP showing 23% shorter planning times. We provide practicalguidelines for selecting appropriate algorithms and network quality thresholds based on application requirements, enablingmore reliable AMR operations in industrial settings.
Related Topics
Concepts
Motion planning
Computer science
Path (computing)
Autoencoder
Artificial neural network
Any-angle path planning
Wireless network
Graph
Mobile robot
Constraint satisfaction problem
Artificial intelligence
Network planning and design
Constraint (computer-aided design)
Distributed computing
Real-time computing
Quality (philosophy)
Wireless sensor network
Engineering
Wireless
Robot
Redundancy (engineering)
Mathematical optimization
Robotics
Machine learning
Data mining
Metadata
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- other
- Landing Page
- https://doi.org/10.5281/zenodo.17830291
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7109185915
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W7109185915Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17830291Digital Object Identifier
- Title
-
Network-Aware Path Planning for Autonomous MobileRobots in Industrial EnvironmentsWork title
- Type
-
otherOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
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2025-12-05Full publication date if available
- Authors
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Kathalkar, Om, Hajj Hassan, Houssam, Kattepur, Ajay, Bouloukakis, GeorgiosList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17830291Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17830291Direct OA link when available
- Concepts
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Motion planning, Computer science, Path (computing), Autoencoder, Artificial neural network, Any-angle path planning, Wireless network, Graph, Mobile robot, Constraint satisfaction problem, Artificial intelligence, Network planning and design, Constraint (computer-aided design), Distributed computing, Real-time computing, Quality (philosophy), Wireless sensor network, Engineering, Wireless, Robot, Redundancy (engineering), Mathematical optimization, Robotics, Machine learning, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.Hernang´omez | 67 |
| abstract_inverted_index.leadingrobots | 26 |
| abstract_inverted_index.network-aware | 44 |
| abstract_inverted_index.network-based | 114 |
| abstract_inverted_index.requirements, | 154 |
| abstract_inverted_index.acomprehensive | 41 |
| abstract_inverted_index.mission-critical | 36 |
| abstract_inverted_index.multi-path(GraphMP) | 115 |
| abstract_inverted_index.practicalguidelines | 142 |
| abstract_inverted_index.coordination,control, | 12 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |