Abstract
This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development.
Highlights
The fourth industrial revolution and digital innovation have led to the increased interest in the use of robots across industries; robots are being used in various industrial sites
Professional service robots are robots used by workers to perform tasks, and mobile robots are one of the professional service robots [2]
The reinforcement learning algorithm has the concept of a value function, a function of a state that estimates whether the environment given to an agent is good or bad
Summary
The fourth industrial revolution and digital innovation have led to the increased interest in the use of robots across industries; robots are being used in various industrial sites. A mobile robot needs the following elements to perform tasks: localization, mapping, and path planning. To review the use of reinforcement learning as a path optimization technique for mobile robots in a warehouse environment, the following points must be understood. It is necessary to understand the recommended research steps for the path planning and optimization of mobile robots. This paper discusses an experiment performed using a reinforcement learnin rithm as a path optimization technique in a warehouse environment. Warehouse path planning has been optimized using heuristics and meta heuristics Most of these methods work without additional learning and require significant computation time as the problem becomes complex. The reinforcement learning algorithm has the concept of a value function, a function of a state that estimates whether the environment given to an agent is good or bad. The formula is as follows [18,20]
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