Genetics-Based Machine Learning Approach for Rule Acquisition in an AGV Transportation System

We propose an autonomous decentralized method for multiple AGV robots under uncertain delivery requests. Transportation route plans of AGV robots are expected to minimize the transportation time without collisions among the robots in the systems. In our proposed methods, each robot as an agent computes its transportation route by referring to the static path information, and it exchanges its route plan each other. Once collisions are detected, one of the two agents chosen by a negotiation rule modifies its route plan. The rule consists of a condition-part and an action-part, and one rule which matches to the conditions of two agents under negotiation is selected from a set of rules. The rules are generated and improved by a genetic based machine learning approach, where a set of rules is represented symbolically as an individual of genetic algorithms, and fitness of each individual is determined according to the total travel time of the AGVs and the adequacy of the condition-parts of the rules.