Task Scheduling with Mobile Robots—A Systematic Literature Review
The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but als...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/14/6/75 |
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| author | Catarina Rema Pedro Costa Manuel Silva Eduardo J. Solteiro Pires |
| author_facet | Catarina Rema Pedro Costa Manuel Silva Eduardo J. Solteiro Pires |
| author_sort | Catarina Rema |
| collection | DOAJ |
| description | The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots. |
| format | Article |
| id | doaj-art-9b44c5289ea84fa2b675800393f61fa2 |
| institution | DOAJ |
| issn | 2218-6581 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj-art-9b44c5289ea84fa2b675800393f61fa22025-08-20T03:16:39ZengMDPI AGRobotics2218-65812025-05-011467510.3390/robotics14060075Task Scheduling with Mobile Robots—A Systematic Literature ReviewCatarina Rema0Pedro Costa1Manuel Silva2Eduardo J. Solteiro Pires3INESC TEC–Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC–Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC–Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalINESC TEC–Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, PortugalThe advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots.https://www.mdpi.com/2218-6581/14/6/75task schedulingtask planningmobile robots |
| spellingShingle | Catarina Rema Pedro Costa Manuel Silva Eduardo J. Solteiro Pires Task Scheduling with Mobile Robots—A Systematic Literature Review Robotics task scheduling task planning mobile robots |
| title | Task Scheduling with Mobile Robots—A Systematic Literature Review |
| title_full | Task Scheduling with Mobile Robots—A Systematic Literature Review |
| title_fullStr | Task Scheduling with Mobile Robots—A Systematic Literature Review |
| title_full_unstemmed | Task Scheduling with Mobile Robots—A Systematic Literature Review |
| title_short | Task Scheduling with Mobile Robots—A Systematic Literature Review |
| title_sort | task scheduling with mobile robots a systematic literature review |
| topic | task scheduling task planning mobile robots |
| url | https://www.mdpi.com/2218-6581/14/6/75 |
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