Developing conflict-free routes for automated guided vehicles
Operations Research
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Robot Motion Planning
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Planning Algorithms
Genetics-Based Machine Learning Approach for Rule Acquisition in an AGV Transportation System
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 03
Knowledge-based event control for flow-shops using simulation and rules
Proceedings of the 40th Conference on Winter Simulation
Plan Repair in Conflict-Free Routing
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A cross-level approach to distribution planning
Proceedings of the 2012 Symposium on Emerging Applications of M&S in Industry and Academia Symposium
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Traditional routing algorithms for real world AGV systems in warehouses compute static paths, which can only be adjusted to a limited degree in the event of unplanned disturbances. In our approach, we aim for a higher reactivity in such events and plan small steps of a path incrementally. The current traffic situation and also up to date time constraints for each AGV can then be considered. We compute each step in real time based on empirical data stored in a knowledge base. It contains information covering a broad temporal horizon of the system to prevent costly decisions that may occur when only considering short term consequences. The knowledge is gathered through machine learning from the results of multiple experiments in a discrete event simulation during preprocessing. We implemented and experimentally evaluated the algorithm in a test scenario and achieve a natural robustness against delays and failures.