# Integrated Robotic and Network Simulation Method
## 1 Introduction  
The increasing use of teams of mobile cooperative robots in a variety of applications including area coverage, exploration and cooperative transport, is also pushing research on cooperative control solutions. In general, cooperative robots may be considered as a set of movable sensors that exchanges information to complete a task. Nevertheless, these solutions are built on top of a communication network that has several imperfections such as delays and packet losses, ending up having a significant impact on team behavior [1], especially on decentralized control cases that rely heavily on explicit communication between robots [2]. These solutions are developed, tested, and validated through simulations, emulations, testbeds, or real robots [3; 4; 5]. Although these four concepts are not mutually exclusive, choosing to use one or all of them depends on material and financial resources available, study deadlines, project knowledge and personal experience. A convenient combination is to carry out simulations first, for their flexibility, and then implement the real system. However, moving from simulation to implementation can be time and effort consuming, despite new model-based engineering approaches that try to automatize this transition, but which are still limited in their capabilities. The effort increases significantly whenan unknown design problem is only identified later at testing time, forcing redesigns, new simulations and more implementation. One approach to soften simulation to implementation transition is to improve simulation accuracy, aiming at results that are more realistic. This approach has been followed in different ways, by integrating different robot physical aspects in 3D simulations as in eRobotics [6], or integrating different levels of implementation abstraction as in meta-modelling simulation for robotics [7] and in Cyber-Physical Systems (CPS) [8] or yet, by integrating a communication network simulation with a desired application as in Networked Control Systems (NCS) [9], Wireless Sensor Network (WSN) [10], Internet of Robotic Things (IoRT) [11], and in teams of mobile robots [10; 12; 13; 14]. Simulation of integrated application and network models has been achieved in diverse ways: co-simulating with two different simulators [12], expanding a network simulator with physical models [1], or by expanding the physical simulator with network models [9]. Even though the referred works represent advances in studying the impact of a network on robotic systems, they lack a systematic method to build and evaluate a complex simulation study. Several such methods do exist, though, aiming at different science domains such as general model simulations [15], CPS [8], Digital Twins [16], Network Simulation Only [17], and Network Emulation Only [3]. In the domain of robotics, methods often describe procedures related to a robotic task, e.g., a method for using a simulation framework to study mobile robots operating on uneven terrain [4] and a method for abstracting expression techniques for diverse types of robots [18]. Other general methods for robotics focus on different robot aspects, e.g., combining physical domains as in eRobotics [6], or initial ideas that still need to be improved, as in IoRT [11]. In our domain of interest, namely networked control systems made of teams of cooperating robots, an initial concept method has been shown by the authors, as a work in progress, to improve the assessment of the network influence on cooperative strategies [1; 19; 20]. However, several improvements could still be made, finally resulting in what we denominated as the Integrated Robotics and Network Simulation (IRoNS) method, which incorporates known validation and documentation techniques.