semantics of the communication are undefined at the design phase and they correspond to the evolution process and fine-tuning of the neural controller, based on the communication information that becomes relevant for the task. The manuscript confirms that the evolution guides the communication towards very different mechanics and semantics, although the neural controllers are architecturally identical. Nonetheless, the evolved systems are scalable and robust under predefined perturbations. On the other side, [PERSON] et al. investigate how individuals without memory capabilities may display collective memory [15]. They gain inspiration from the [PERSON] experiment and propose an aggregation experiment on a swarm of robots. Within the experiment, the group encodes and recovers information that is not independently stored on the individuals of the swarm. On the contrary, the information is stored over specific spatial configurations of the group. The results show that a swarm of robots can have a memory of its own, independently of the internal state of its members. With simple agents and behavioral rules, the robots learn to associate different stimuli with spatial information. Focusing on planification, [PERSON] et al. develop a distributed path planner for multi-robot systems with a mechanism based on priorities [16], by integrating Particle Swarm Optimization (PSO) [17] and the Bresenham algorithm [18]. In their proposal, PSO is used to generate the optimal trajectory, with a multi-objective fitness function based on energy consumption and movement risk. The Bresenham algorithm is used for ensuring the full coverage of the operational area. The communication between robots is performed with each other in an ad hoc manner, and it is only used to share position and location among themselves. Extending the communication assumptions raised in the previous work, [PERSON] et al. propose a new method inspired by flocking algorithms and PSO, named the Local Charged Particle Swarm Optimization (LCPSO) algorithm [19]. In this case, the authors control a swarm of robots with communication constraints between agents. The authors test the algorithm in a formation scenario with source heading, where the positions of the center of gravity and attractors guide the swarm. Moreover, based on tunable parameters, the algorithm controls the swarm with minimal communication or interaction, achieving a stable formation which is invariant to time. Nonetheless, limited communication is also exploited in [20]. The authors propose a distributed and asynchronous approach to simultaneous task assignment and path planning. The authors refer to it as Hierarchical Task Assignment and Path Finding (HTAPF), exploiting a hierarchical representation of the locations where tasks must be executed. The proposal combines a bio-inspired collective decision-making process and a search-based path planning. The algorithm is decentralized for both tasks, being robust to limited communication and robot failures and scalable to different environments and group sizes. Task allocation supports multiple hierarchical decision levels and a utility function that directly considers the cost of motions. By dividing the level of competences, the authors of [21] develop a macroscopic foraging behavior for collectively transporting objects. The authors define two microscopic behaviors, both for displacement and collision avoidance. The robots adapt to unseen changes in the environment, operating coherently with its initial goal. The authors verify that the behavior definition on the microscopic system does not only simplify the learning of the global system, but it also provides a way to adjust microscopic behaviors without significant macroscopic changes. Based on the same concepts of division of behaviors, a multilayer architecture is presented in [22]. The different layers focus on trajectory planners, obstacle avoidance algorithms, and decision-making methods.