Simulation is a key technology in robotics as it enables the generation of environmental data and testing scenarios for development and maintenance purposes. However, simulations are an imperfect representation of the real-world and the so-called sim-to-real gap between simulation and reality hinders the deployment of virtual developed solutions without additional effort. Modeling complex systems like highly dynamic and holonomic mobile robots presents additional complexities in simulation. This paper addresses these challenges through a case study on creating a model for a highly dynamic logistics robot. The study considers the modeling of the entire system down to creating suitable colliders for the rollers of a Mecanum wheel. Additionally, the impact of significant physics parameters is presented. To bridge the sim-to-real gap, a pipeline is developed that utilizes a Motion Capture system to compare the behavior of a real robot with its simulated counterpart across various motions. By leveraging expert knowledge gained from the real-world data, the simulation model is manually tuned to replicate complex system behaviors, such as sliding effects.

As cities continue to expand, there has been a substantial increase in demand for factories and sewage systems, which in turn necessitated the need for maintaining the pipeline systems involved. These are typically difficult, dangerous, and expensive to investigate manually by humans, and therefore the use of automated robots is crucial to identifying cracks. Therefore, we propose PLIERS (Pipeline Leak Identification Emergency Robot Swarm). PLIERS is a system consisting of a swarm of robots that are used to investigate pipes for any cracks while communicating with one another as well as a server system. The robots are mounted with cameras to collect images of these cracks, which are then analyzed using a machine learning algorithm by the server to detect and confirm the cracks and their severity.