Abstract
Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehousing, cross-docks, terminals, and hospitals. Their advanced hardware and control software allow autonomous operations in dynamic environments. Compared to an automated guided vehicle (AGV) system in which a central unit takes control of scheduling, routing, and dispatching decisions for all AGVs, AMRs can communicate and negotiate independently with other resources like machines and systems and thus decentralize the decision-making process. Decentralized decision-making allows the system to react dynamically to changes in the system state and environment. These developments have influenced the traditional methods and decision-making processes for planning and control. This study identifies and classifies research related to the planning and control of AMRs in intralogistics. We provide an extended literature review that highlights how AMR technological advances affect planning and control decisions. We contribute to the literature by introducing an AMR planning and control framework to guide managers in the decision-making process, thereby supporting them to achieve optimal performance. Finally, we propose an agenda for future research within this field.
Highlights
In recent decades, the technology in materials handling has advanced rapidly
One major development is the evolution of automated guided vehicles (AGV) into autonomous mobile robots (AMR)
Starting with the literature on AGVs, the present study identifies and classifies research related to the planning and control of Autonomous mobile robots (AMR) and proposes an agenda for future research in this field
Summary
The technology in materials handling has advanced rapidly. One major development is the evolution of automated guided vehicles (AGV) into autonomous mobile robots (AMR). Since 1955, when the first AGV was introduced (Muller, 1983), the guiding system that forms the core part of AGV material handling systems has evolved along various stages of mechanical, optical, inductive, inertial, and laser guidance into today’s vision-based system (Fig. 1). This vision-based system uses ubiquitous sensors, powerful on-board computers, artificial intelligence (AI) and simultaneous location and mapping (SLAM) technology, enabling the device to understand its operating environment and to navigate in facilities without the need to define and implement reference points in advance. AMRs, can adapt quickly to changes in the operating environment
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