Analytical models for planning and control of autonomous mobile robots for logistic management
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K. Tejasvi
Abstract
Industrial and commercial business relies heavily on logistics. It involves the movement of products from one location to another, as well as their storage. Stored goods are transported to retail outlets on wooden pallets that have been transported from the warehouses. Customers order pallets, and those pallets are made to their specifications. Order picking is a time-consuming and physically exhausting task that is carried out by people. Walking for lengthy periods, lifting big things, and picking at an acute angle all contribute to exhaustion. As a result, workers suffer from musculoskeletal problems and perform poorly, which reduces production and causes delays across the supply chain. Automation in warehouses is currently being used to alleviate this issue and overcome productivity limits. The introduction of robots for process automation allows for greater flexibility. Autonomous ground vehicles and static manipulators are used to pick and place cargo by these robots. Intralogistics operations such as manufacturing, warehouses, cross-docks, terminals, and hospitals are now adopting autonomous mobile robots (AMR). Autonomous operation in dynamic conditions is possible because of their superior hardware and control software. AMRs can communicate and negotiate independently with other resources, such as machines and systems, and thus decentralize the decision-making process in comparison to an automated guided vehicle (AGV) system, in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs. Decentralized decision-making enables the system to respond flexibly to changes in the system state and the external environment. As a result of these changes, traditional ways of planning and controlling have changed. This study collects and categorizes studies toward intralogistic AMR planning and control. An in-depth assessment of the literature is provided to show how AMR technology developments have an impact on planning and control. It is our goal to help managers make the best decisions possible by providing them with an AMR planning and control framework to help them reach their goals. A research agenda for the future of this topic is also proposed at the end of our paper.
Abstract
Industrial and commercial business relies heavily on logistics. It involves the movement of products from one location to another, as well as their storage. Stored goods are transported to retail outlets on wooden pallets that have been transported from the warehouses. Customers order pallets, and those pallets are made to their specifications. Order picking is a time-consuming and physically exhausting task that is carried out by people. Walking for lengthy periods, lifting big things, and picking at an acute angle all contribute to exhaustion. As a result, workers suffer from musculoskeletal problems and perform poorly, which reduces production and causes delays across the supply chain. Automation in warehouses is currently being used to alleviate this issue and overcome productivity limits. The introduction of robots for process automation allows for greater flexibility. Autonomous ground vehicles and static manipulators are used to pick and place cargo by these robots. Intralogistics operations such as manufacturing, warehouses, cross-docks, terminals, and hospitals are now adopting autonomous mobile robots (AMR). Autonomous operation in dynamic conditions is possible because of their superior hardware and control software. AMRs can communicate and negotiate independently with other resources, such as machines and systems, and thus decentralize the decision-making process in comparison to an automated guided vehicle (AGV) system, in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs. Decentralized decision-making enables the system to respond flexibly to changes in the system state and the external environment. As a result of these changes, traditional ways of planning and controlling have changed. This study collects and categorizes studies toward intralogistic AMR planning and control. An in-depth assessment of the literature is provided to show how AMR technology developments have an impact on planning and control. It is our goal to help managers make the best decisions possible by providing them with an AMR planning and control framework to help them reach their goals. A research agenda for the future of this topic is also proposed at the end of our paper.
Chapters in this book
- Frontmatter I
- Contents V
- List of authors VII
- The impact of green manufacturing in Industry 4.0 for future ecosystems 1
- Custom manufacturing using Industry 4.0: cost-effective industry revolution model 17
- Cloud-based industrial IoT infrastructure to facilitate efficient data analytics 31
- An impact of robust Industry 4.0 strategy on supply chain management 53
- Machine learning based smart cloud factories 71
- Sustainable and flexible digital models for the manufacturing of ecosystem 91
- Industry 4.0: efficient supply chain management using energy-aware cloud infrastructure model 103
- Analytical models for planning and control of autonomous mobile robots for logistic management 121
- Industry 4.0: augmented reality in smart manufacturing industry environment to facilitate faster and easier work procedures 141
- Prediction of knee osteoarthritis progression using machine learning techniques 163
- Index 173
Chapters in this book
- Frontmatter I
- Contents V
- List of authors VII
- The impact of green manufacturing in Industry 4.0 for future ecosystems 1
- Custom manufacturing using Industry 4.0: cost-effective industry revolution model 17
- Cloud-based industrial IoT infrastructure to facilitate efficient data analytics 31
- An impact of robust Industry 4.0 strategy on supply chain management 53
- Machine learning based smart cloud factories 71
- Sustainable and flexible digital models for the manufacturing of ecosystem 91
- Industry 4.0: efficient supply chain management using energy-aware cloud infrastructure model 103
- Analytical models for planning and control of autonomous mobile robots for logistic management 121
- Industry 4.0: augmented reality in smart manufacturing industry environment to facilitate faster and easier work procedures 141
- Prediction of knee osteoarthritis progression using machine learning techniques 163
- Index 173
