# Remotely Controlled Robot Swarms: A Structural Analysis and Model for Structural Optimization
## 2 Materials and Methods  
The methods were based on analyses of existing systems in the human and animal world, including hierarchical and non-hierarchical structures. These systems can be converted into robot structures [16]. However, we met with some difficulties in following natural systems, so some simplifications and substitute solutions were found. Swarm structure and movement strategy analyses could start from the bottom, with insects, or from the top, with humans, e.g., soldiers in formation. The former has no visible structure and its members work together according to unknown rules. The latter has a clearly defined structure and rules of movement. Between them we have semi-structured swarms, such as bees, etc. Swarms with a reconfigured structure have also been identified [16], which we also took into account. During the literature review, we found more questions than answers about insect behaviour and how it can be transferred to the robot world; instead, therefore, we started with the most advanced structure. Initially, the hierarchical swarm model was built based on soldier formation movement in a matrix. Although it consisted of the most intelligent creatures, it was the easiest to analyse because the authors are experienced in military service. Therefore, it was easy to build a logical model and an algorithm for swarm behaviour. For these analyses, some typical scenarios were prepared. These scenarios described the movement conditions and required paths. These movements were planned to be performed in a straight line and in curves, in various formations, such as in single file or in a matrix. The algorithm for an individual robot's movement depended on its role in the swarm. Its task was to maintain its position in the swarm. In various scenarios, the formation was changed. During algorithm building, the information needed for robot and drive skills was defined. This information was delivered with control signals or by sensors. For such structural and logical models, relevant computer models were built. These models consisted of an environment model, a model for each robot (depending on its role in the swarm), and a command system. The non-homogeneous environment introduced some randomness into the process. Furthermore, the static distribution of robot parameters was introduced. In some scenarios, the failures of some robots were modelled to check the robustness of the swarm. Simulations were repeated a few times (for a changed  
Figure 3: The process of swarm structure optimisation. environment or a new robot setting in the swarm), recording the path of each robot. After each simulation, the results were analysed and discussed. If everything was proceeding in the assumed way, the structure and logicical algorithm of the robot were simplified. If not, the structure was improved. However, the method was directed towards the simplification of the structure in order to make it more similar to that of insects--a non-hierarchical, homogeneous swarm structure. For the purpose of verification, a swarm of five robots was used. The robots were built on a platform consisting of a remotely controlled electric vehicle with a modified driving control system and an added microcomputer module with sensors. These modules were from Arduino and featured a ultrasonic distance sensors; the others had a specially designed optoelectronic module with a camera and lighting markers. For a structural swarm, only one robot--the leader--received the control signal, and moved according to it. For a non-structural, homogeneous swarm, all the robots (using the same RF band) received and followed this signal. The main goal of this work was to determine the optimal structure of a robot, with the sensors and algorithms required in order to create the most effective swarm during motion on a required path. The starting point was the model of a soldier-like structural swarm. This model was verified with a computer model and real tests with simple scenarios.