Simulation-Based Optimization Study Evaluating Future Automation Potential in the Logistics Industry - Strategies for multiple mobile manipulators working in a shared space

Abstract

Abstract Simulation plays a crucial role in evaluating and analyzing the automation industry. With advancements in technology, particularly in AI and vision with object recognition, there is a growing interest in developing new solutions. This thesis investigates the current capabilities utilizing the simulation software KUKA.Sim. Additionally, it aims to explore the understanding of the potential applications and challenges of deploying multiple mobile manipulator robots (MMR) in a shared space. A simulation model with several new components was developed and tested using a simulationbased optimization methodology. To see the effects of different parameters in a dynamical chaining environment. A literature study and a current state analysis were made. One case study was identified and the data was used to compare the results from the iterative simulation process. The research findings show that it is possible to enhance the current robot simulation software KUKA.Sim and thereby close the gap between a simulation and a real-world environment. Furthermore, the test result of the iterative simulation processes revealed several important findings regarding how the camera placement affected the output and the importance of considering collision avoidance when using multiple MMRs in a shared space. Through the use of simulation, the logistics industry can effectively optimize new types of automation solutions, leading to an increased adoption of multiple MMRs.