Genetic Algorithm Based Optimization in Solving Multi Robot Task Allocation Problems

As the world moves on in the direction of automated production lines and advanced robotics, there is a need to solve the problems of task allocation to robots to maximize efficiency based on parameters that change from situation to situation. This paper imparted knowledge on basic-intermediate problem-solving techniques to solve problems in the MRTA category. Problems we need to solve are decisions to make or tasks to complete. The Framework of making decisions based on existing data falls under Artificial Intelligence. While the second part of doing tasks comes under the Framework of Robot Process Automation, for which machines already know what to do (Programmed), But need user input as to where to go to do the tasks in question. The purpose of this work is to learn about the different types of problems and how to solve them in the best possible way. This work implemented mixed linear programming with saving matrix methodology that minimized the total distance and path of robots in coordinating the robot environment, thus minimizing the idleness of the robot.