Distributed loitering synchronization is the process whereby a group of fixed-wing Unmanned Aerial Vehicles (UAVs) align with each other while they follow a circular path in the air. This process is essential to establish proper initial conditions for missions in the real world. We evaluate the performance of three synchronization algorithms using a setup of continuously moving fixed-wing drones randomly placed around a loitering circle. We consider the algorithm based on distributed consensus as a baseline. We propose two methods: the Minimum Of Shortest Arc (MOSA) algorithm that outperforms the baseline in this setup and Firefly multi-Pulse Synchronization (FPS), which is inspired by firefly synchronization. The latter method requires 10 times less communication while maintaining a performance comparable to the baseline. These algorithms were first tested in a simple simulation, then a more realistic simulation environment using Gazebo in which fixed-wing dynamics are considered. The proposed algorithms are rigorously tested in simulation through multiple trials involving a group of 10 UAVs, confirming the effectiveness of our approaches. The results were then validated in real flights using 3 fixed-wing drones.

Abstract Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.

In this paper, we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy employed as a gait-learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where "newborn" robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: How do gait-learning algorithms compare when applied to various morphologies that are not known in advance (and thus need to be treated as without priors)? To answer this question, we use a test suite of twenty different robot morphologies to evaluate our gait-learners and compare their efficiency, efficacy, and sensitivity to morphological differences. The results indicate that Bayesian Optimization and Differential Evolution deliver the same solution quality (walking speed for the robot) with fewer evaluations than the Evolution Strategy. Furthermore, the Evolution Strategy is more sensitive for morphological differences (its efficacy varies more between different morphologies) and is more subject to luck (repeated runs on the same morphology show greater variance in the outcomes).

In this paper, we present a swarm of Crazyflie nano-drones. The swarm can show various collective behaviors: Flocking, gradient following, going to a chosen point, formation, and scattered search of the environment. The methodology behind the behaviors is executed entirely on-board. Crazyflies use a common radio channel to share positions with each other. If desired, an operator can use the same channel and start, end, change or guide the collective behaviors online during the flight. We use the virtual force vectors and modify the way they are combined to achieve different behaviors instead of developing unique algorithms for each. This allows us to develop more collective behavior types with less effort. In the results, we show a detailed analysis of the behaviors and assess the coordination and the safety of the agents in addition to the performance as a collective. We conclude that our swarm of 6 Crazyflies was successful in the desired behaviors.

Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behavior that is an indirect result of the interactions between swarm members and the environment. In this paper we investigate whether an evolutionary approach can mitigate this problem. We consider a very challenging task where robots with limited sensing and communication abilities must follow the gradient of an environmental feature and use Differential Evolution to evolve a neural network controller for simulated robots. We conduct a systematic study to measure the flexibility and scalability of the method by varying the size of the arena and number of robots in the swarm. The experiments confirm the feasibility of our approach, the evolved robot controllers induced swarm behavior that solved the task. We found that solutions evolved under the harshest conditions (where the environmental clues were the weakest) were the most flexible and that there is a sweet spot regarding the swarm size. Furthermore, we observed collective motion of the swarm, showcasing truly emergent behavior that was not represented in-and selected for during evolution.

Potential field approaches have been often used to describe and model interactions within a swarm of robots performing collective motion, also called flocking. Despite the high number of proposed approaches, most have only been tested in simulation and among the minority tested on real robots, even fewer abandoned the laboratory boundaries in favor of real-world scenarios. In this work, we propose a decentralized flocking approach that builds over the classical potential field models and that is proved to work well both in simulated and real-world environments. Each robot in the swarm relies on limited information and can only perceive its local neighbors through limited communication of noisy position information. No information on individual drone orientations, velocities, or accelerations is exchanged or needed. The novel experimental achievement of this paper is the realization of collective motion in three dimensions with the above sensing limitations. The swarm dynamically adapts to the environment by keeping a preferred distance from the ground and by changing formation. To show the general applicability of the proposed control algorithm, we study how it performs with the use of different potential functions proposed in the literature and by comparing them via extensive evaluation of the results in a realistic simulated environment. Lastly, we compare the performances of the proposed approach and of the different potentials on a real-drone swarm of up to fourteen robots flying both in two and three dimensional formations and in a challenging outdoor environment.

Within the field of multi-robot systems, developing systems that rely only on onboard sensing without the use of external infrastructure (e.g. GNSS) has many potential applications. However, relying only on visual-based modalities for localization presents challenges in terms of accuracy and reliability. We introduce a decentralized multi-robot lateral velocity estimation method for Unmanned Aerial Vehicles (UAVs) to improve onboard measurements in case GNSS infrastructure is not available. This method relies on sharing the onboard measurements of neighbors, as well as the estimation of the relative motion of a focal UAV within the swarm, based on observation of coworking robots. The proposed velocity estimation method does not rely on centralized communication to achieve high reliability and scalability within the swarm system. The performance of the state estimation approach has been verified in simulations and real-world experiments. The results have shown that a swarm of UAVs using the proposed velocity estimator can stabilize individual robots when their primary onboard localization source is not reliable enough.

In collective decision-making, individuals in a swarm reach consensus on a decision using only local interactions without any centralized control. In the context of the best-of-n problem - characterized by n discrete alternatives - it has been shown that consensus to the best option can be reached if individuals disseminate that option more than the other options. Besides being used as a mechanism to modulate positive feedback, long dissemination times could potentially also be used in an adversarial way, whereby adversarial swarms could infiltrate the system and propagate bad decisions using aggressive dissemination strategies. Motivated by the above scenario, in this paper we propose a bio-inspired defence strategy that allows the swarm to be resilient against options that can be disseminated for longer times. This strategy mainly consists in reducing the mobility of the agents that are associated to options disseminated for a shorter amount of time, allowing the swarm to converge to this option. We study the effectiveness of this strategy using two classical decision mechanisms, the voter model and the majority rule, showing that the majority rule is necessary in our setting for this strategy to work. The strategy has also been validated on a real Kilobots experiment.

Classic opinion dissemination models such as Majority model, and Voter model are particularly important in swarms robotics because they regulate the interactions among the agents while the swarm is engaged in collective decision-making processes requiring the consensus of the large majority or the unanimity of the group's members. In this paper, we compare the effectiveness of three different opinion dissemination models in a specific scenario where consensus is searched on an opinion disseminated within the swarm by a small number of legitimate agents. The task of the legitimate agents is hindered by few adversarial agents which disseminate within the swarm an “invalid” message. This scenario is meant to model a data communication manipulation attack in a swarm of robots. By comparing the dissemination models in different experimental conditions, the results of our study inform us on which model is more effective in supporting the case of legitimate agents, while reducing the disruptive effects of the data communication manipulation attack.

In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action. The novelty of our method is that it allows robots to collectively find consensus on the fastest action without measuring explicitly the execution times of all available actions. We study two analytical models of the decision-making method in order to understand the dynamics of the consensus formation process. Moreover, we verify the applicability of the method in a real swarm robotics scenario. To this end, we conduct three sets of experiments that show that a robotic swarm can collectively select the shortest of two paths. Finally, we use a Monte Carlo simulation model to study and predict the influence of different parameters on the method.

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as "task partitioning", whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.

IntroductionCollective Motion (CM) is observed in a va-riety of animal groups such as bird flocks and fish schools. In a recent study, Cavagna et al. (2010) found that the corre-lation lengths of speed and velocity fluctuations in starling flocks are not set by a specific interaction range, but are in-

In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to modify, and to tease apart the inherent principles that lead to the desired collective behavior. In contrast, our representation is based on completely readable and analyzable individual-level rules that lead to a desired collective behavior.

In swarm robotics, large groups of relatively simple robots cooperate so that they can perform tasks that go beyond their individual capabilities [1], [2]. The interactions among the robots are based on simple behavioral rules that exploit only local information. The robots in a swarm have neither global knowledge, nor a central controller. Therefore, decisions in the swarm have to be taken in a distributed manner based on local interactions. Because of these limitations, the design of collective decision-making methods in swarm robotic systems is a challenging problem. Moreover, the collective decision-making method must be efficient, robust with respect to robot failures, and scale well with the size of the swarm.

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.